Robotic inference and control systems

Abstract

A variety of smart inference systems and robotic control devices are disclosed. A device networking system interconnects mobile devices, robotic devices, and others. A robotic controller secures and manipulates a controlled device user interface. A smart device system interprets data based on a plurality of models operating on a plurality of runtimes based on a plurality7 of capabilities. A smart payment processor system assigns payments accounts automatically as a function of a semantic matching between one or more inferred semantic identities. A robotic emulation device captures and analyzes a video signal from a target device and transmits manipulation signals emulating a peripheral input device. A system of carts are physically coupled in a charging configuration. A conveyor is configured to couple with another conveyor and having sensors to detect weight, size, location or other aspects of conveyed items, and implementing semantic analysis for management and augmentation.

Description

ROBOTIC INFERENCE AND CONTROL SYSTEMSINVENTORLucian CristacheFIELD OF THE INVENTION

[0001] The inventions disclosed in this description relate to a variety of robotic inference and control systems. One disclosed invention relates generally to device networking systems, including such systems for interconnecting mobile devices, robotic devices, apparel devices, industrial devices, tracking devices, home devices, appliance devices, medical devices and / or any other devices.

[0002] Additional disclosed inventions relate to payment processor systems point-of- sale device having at least one transceiver, a processor, a memory storing a plurality of users and a plurality of payment accounts, wherein the processor is configured to apply semantic analysis to select a payment account in rapport with an item semantic identity in a payment transaction initiated at the point of sale.

[0003] A conveyor system is also disclosed, having at least one conveyor / cart unit configured to extend and / or couple with another conveyor. The conveyor system may comprise a plurality of sensors and / or barriers to detect item weight, size, location and / or to control access of the items in the conveyor system. The conveyor system may implement semantic analysis for management and augmentation towards supervisors and / or users. A cart system may further be physically coupled in a mutual and external charging configuration.

[0004] A robotic device is disclosed, having internal and / or external sensors and physically coupled and / or hosts a controllable device. Based on inferences the robotic device controls a controllable device. Multiple robotic devices may be coupled and composed to host and manipulate multiple controlled devices.

[0005] A smart device system is disclosed, comprising a processor, a memory and at least one sensing element, wherein the processor is configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards afirst smart device system user in relation with the affirmative and non-affirmative determinations.

[0006] A semantic robotic device is described which captures data from a plurality of devices or provider services and analyze it in rapport with a semantic goal to perform optimized manipulation and augmentation.

[0007] A securable robotic controller is also described, having internal and / or external sensors is physically coupled and / or hosts a controllable device. Based on inferences the robotic device controls a controllable device. Multiple securable robotic controllers may be coupled and composed to host and manipulate multiple controlled devices.

[0008] A robotic emulation device is described, having a processor, a memory and multimedia physical interfaces. The robotic emulation device captures a video signal from a target device, analyzes it and based on the analysis, transmits manipulation control signals to the target device input by emulating a computer peripheral input device, the manipulation control signals determining identification or selection of a target interface control associated with a first user interface encoded and transmitted in the video signal. The robotic emulation device may be connected via an embedded transceiver to at least one computer host or tenant for relaying the captured video data for semantic analysis and based on the semantic analysis applying the inferred manipulation semantics. Multiple robotic devices may be grouped, coupled and composed to host and manipulate multiple target devices.

[0009] A smart device system interprets data based on a plurality of models operating on a plurality of runtimes based on a plurality of capabilities. The smart device system may comprise or be coupled to a plurality of augmentation capabilities and / or a plurality of sensors. The smart device system processes data based on inputs data from sensors to provide semantic augmentation.BACKGROUND OF THE INVENTION

[0010] There are cases in which various target devices need to be robotically controlled and / or managed for improved efficiency. Connectable, attachable and robotic emulation devices enable novel computing capabilities, deployments and topologies.SUMMARY OF THE INVENTION

[0011] A preferred robotic semantic system may include one or more smart posts each having a base (which may optionally include a plurality of wheels or casters in the case of a mobile smart post), a power section, a trunk section, a structure fixation and manipulationportion, a control section, a clipping area, a portion supporting one or more antennas, and an optical sensor portion. Other modules may be incorporated with such smart posts including a copter module (e.g. for aerial transportation) and a display module (e.g. for providing semantic augmentation).

[0012] In one example of the invention, the smart post includes all or a subset of the components listed above in a manner in which they are integrated into a generally unified structure, such as a single pole or post having a hollow center and in which the listed components are attached or inserted into the post. In other versions, the components described above are generally assembled separately, such that they are produced as modules which are joined together to form the post. Thus, each of the above sections or regions or portions may be separately formed modules which are joined together, or may be separate portions of a unitary post or similar structure. In the discussion which follows, for the sake of simplicity each of the foregoing will be referred to as a module; it should be understood, however, that the same description applies to other embodiments in which the module is a portion or section of the smart post, and not necessarily a discrete module. It is to be understood that the post may use any number of modules of any type. In an example, a post may comprise multiple power modules and / or multiple antenna elements modules and / or multiple cameras modules.

[0013] One example of the invention includes a semantic robotic system comprising a plurality of communicatively coupled devices which use a plurality’ of semantic routes and rules and variable semantic coherent inferences based on such routes and rules to allow the devices to perform semantic augmentation.

[0014] In some versions, the devices comprise semantic posts.

[0015] In some preferred versions, the devices comprise autonomous robotic carriers.

[0016] In some examples of the invention, the devices comprise semantic composable modules.

[0017] In preferred versions of the invention, the devices comprise semantic units.

[0018] In some versions, the semantic system includes a semantic gate.

[0019] In some examples, the semantic system comprises a semantic cyber unit.

[0020] In a preferred implementation of the invention, the semantic posts implement crowd control.

[0021] In one example, the semantic posts implement guiding lanes.

[0022] In some examples, the semantic units perform signal conditioning.

[0023] In some versions of the invention, the signal conditioning is based on semantic wave conditioning, preferably based on semantic gating.

[0024] In some examples, the system performs video processing.

[0025] In some examples of the invention, the system performs semantic augmentation on video artifacts.

[0026] In preferred versions, the system may form semantic groups of posts and physically connect them through physical movement of the semantic posts motor components.

[0027] Preferably, the system uses concern factors in order to determine coherent inferences.

[0028] In some examples, the system forms a semantic group based on semantic resonance.

[0029] Preferably, the system invalidates a semantic group based on semantic decoherence.

[0030] In some examples, the system performs semantic learning based on the inference of semantic resonance.

[0031] In some versions, the system performs semantic learning based on the inference of semantic decoherence.

[0032] Preferably, the system learns semantic rules based on semantic resonance.

[0033] In preferred versions, the system leams damping factor rules. Preferably, the system leams semantic gating rules.

[0034] In some examples, the system leams a hysteresis factor based on semantic analysis.

[0035] In preferred versions, the system performs semantic augmentation using a variety of augmentation modalities.

[0036] In some examples, the system performs semantic augmentation comprising semantic displaying. Preferably, the system performs semantic augmentation on particular devices based on ad-hoc semantic coupling.

[0037] In some examples, the system performs semantic augmentation based on challenges and / or inputs.

[0038] In some examples, the system performs semantic encryption.

[0039] In some examples, the system performs semantic gating based on semantic inferences related to at least one video frame.

[0040] In preferred versions, the system uses semantic groups to form composite carriers.

[0041] In some examples, the devices comprise semantic meshes.

[0042] In some cases, the devices comprise biological sensors. In preferred examples, the biological sensors comprise at least one medical imaging sensor.

[0043] A payment processor system includes a memory storing data indicative of each of a plurality of users and a plurality’ of payment accounts associated with a first user among the plurality of users, the memory further storing a plurality of item semantic identities, and further storing associations between the plurality' of item semantic identities and a corresponding plurality’ of items for purchase. A point of sale device includes at least one wireless transceiver. At least one processor and a computer program is operable by the at least one processor to cause the at least one processor to detect a first payment transaction for one or more of the plurality’ of the items for purchase by the first user at the point of sale based on one or more inputs from the at least one wireless transceiver, and assign a first account from among the plurality' of payment accounts to sendee the first payment transaction with respect to the one or more of the plurality’ of items. The first account is assigned as a function of a semantic matching between one or more inferred item semantic identities and a user payment semantic identity’ associated w ith the first account, and further based on a reward provided by the first account, the re ’ard being inferred as being applicable to the user payment semantic identity and the item semantic identities. The one or more inferred item semantic identities is inferred by the at least one processor based on the detected first payment transaction and at least one of the stored associations between the one or more of the plurality of items for purchase and the corresponding plurality’ of item semantic identities.

[0044] In some versions, payment semantic identities are inferred based on matching linked rewards with user interests.

[0045] In some versions, the first payment account is associated with a credit card account.

[0046] In some versions, the first payment account is associated with a temporary’ account linked to a pemianent bank account.

[0047] In some versions, the first payment account is associated w ith a provider and / or broker (operated) (cloud / w eb) (computing / application) account assigned to the user.

[0048] In some versions, the system comprises a sensor and the system further determines a goodwill to be applied to an item charge amount based on a semantic inferred based on an input from the sensor and further based on a configuration stored in a semantic profile.

[0049] In some versions, the sensor is a vision sensor.

[0050] In some versions, the sensor is embedded in a user mobile device.

[0051] In some versions, the payment processor system comprises a sensor and wherein the payment transaction is initiated based on detected gestures based on inputs from the sensor.

[0052] In some versions, the sensor is embedded in a user mobile device.

[0053] In some versions, the payment transaction is initiated from the user mobile device.

[0054] In some versions, the first account is selected based on one or more additional detected gestures from the user, wherein the one or more additional detected gestures are inferred based on inputs from the sensor.

[0055] In some versions, the detected gestures from the user are configured into a semantic profile communicated from a user mobile device via the at least one wireless transceiver.

[0056] In some versions, the at least one wireless transceiver is a radio frequency transceiver.

[0057] In some versions, the processor and the memory are components of the point of sale device.

[0058] In some versions, the point of sale device is a mobile device.

[0059] A payment processor system includes a memory storing data indicative of each of a plurality of users and a plurality of payment accounts associated with a first user among the plurality of users, the memory further storing a plurality of semantic identities, and further storing associations between the plurality of semantic identities and a corresponding plurality of items for purchase. At least one processor and a computer program is operable by the at least one processor to cause the at least one processor to detect a first checkout transaction for one or more of the plurality of the items for purchase by the first user, and assign a first account from among the plurality of payment accounts to service the first payment transaction with respect to the one or more of the plurality of items. The first account is assigned as a function of a semantic matching between one or more inferred checkout semantic identities and a user payment semantic identity associated with the first account, and further based on a reward provided by the first account, the reward being inferred as being applicable to the user payment semantic identity and the checkout semantic identities. The one or more inferred checkout semantic identities is inferred by the at least one processor based on the detected first payment transaction and at least one of the stored associations between the one or more of the plurality of items for purchase and the corresponding plurality of semantic identities.

[0060] In some versions, payment semantic identities are inferred based on matching linked rewards with user interests.

[0061] In some versions, the first payment account is associated with a credit card account.

[0062] In some versions, the first payment account is associated with a temporary7account linked to a pemianent bank account.

[0063] In some versions, the first payment account is associated with a provider and / or broker (operated) (cloud / web) (computing / application) account assigned to the user.

[0064] In some versions, the system comprises a sensor and the system further determines a goodwill to be applied to an item charge amount based on a semantic inferred based on an input from the sensor and further based on a configuration stored in a semantic profile.

[0065] In some versions, the sensor is a vision sensor.

[0066] In some versions, the sensor is embedded in a user mobile device.

[0067] In some versions, the payment processor system comprises a sensor and wherein the payment transaction is initiated based on detected gestures based on inputs from the sensor.

[0068] In some versions, the sensor is embedded in a user mobile device.

[0069] In some versions, the payment transaction is initiated from the user mobile device.

[0070] In some versions, the first account is selected based on one or more additional detected gestures from the user, wherein the one or more additional detected gestures are inferred based on inputs from the sensor.

[0071] In some versions, the detected gestures from the user are configured into a semantic profile communicated from a user mobile device via the at least one wireless transceiver.

[0072] In some versions, the at least one wireless transceiver is a radio frequency transceiver.

[0073] In some versions, the processor and the memory7are components of a point of sale device.

[0074] In some versions, the point of sale device is a mobile device.

[0075] A semantic cloud system includes a plurality of computers comprising at least one processor, at least one memory, and at least one communication network interface, the memory storing a semantic goal wherein the semantic cloud system is programmed to apply semantic drift or entropy in rapport with the semantic goal in a semantic time interval to determine non-affirmative circumstances based on inputs from at least one connected sensingdevice and to cause the system to infer or factorize a leader affirmative measure and / or counter measure in relation with the non-affirmative determinations.

[0076] A cart system comprising a plurality of carts, each cart comprising a battery, a processor and at least one switching and conditioning circuit, the plurality of carts physically coupled in a mutual and external charging configuration, wherein, under the control of a plurality of processors, the plurality of switching and conditioning circuits condition, switch and route the electrical signal within and between each cart among the plurality of carts. In further examples, the electrical signal is switched, routed and / or conditioned based on semantic analysis. A first cart comprises and connects to a second cart via a socket connector which may include an electrical coupling; in some embodiments, the first socket connector is latched to secure it with a pairing socket connector of the second cart.

[0077] A robotic device having internal and / or external sensors comprises at least one latch or pod and / or at least one pocket for accommodating and / or securing to at least one controllable device. Once secured with the robotic device, a controllable device user interface is manipulated by at least one actuated link of the robotic device. Based on inferences the robotic device controls the controllable device user interface via the at least one actuated link. Multiple robotic devices may be coupled and composed to host and manipulate multiple controlled devices.

[0078] A semantic robotic device includes a processor, a memory and a transceiver to capture and present data from a plurality of devices or provider services. The semantic robotic device semantically analyzes the data and manipulates the target applications or services to capture and present data in the most affirmative ways in rapport with at least one semantic goal.

[0079] The semantic robotic device may emulate peripheral input device inputs to launch and manipulate software applications and / or provider services based on inferred manipulation semantic routes.

[0080] Based on further semantic inference on captured data the semantic robotic device is configured to refactorize capabilities and / or manipulation semantic routes in rapport with the at least one semantic goal.

[0081] The semantic robotic device may be configured to ingest user guide data and to a factorize a plurality of capabilities.

[0082] The semantic robotic device may apply semantic factorization on a plurality of goals among the at least one goal.

[0083] A securable robotic controller having internal and / or external sensors comprises at least one latch or pod and / or at least one pocket for accommodating and / or securing to atleast one controllable device. Once secured with the robotic controller, a controllable device interface or control is manipulated by at least one actuated link of the securable robotic controller. Based on inferences the securable robotic controller controls the controllable device interface or control via the at least one actuated link. Multiple robotic controllers may be coupled and composed to host and manipulate multiple controlled devices.

[0084] A smart device system comprises at least one processor and at least one sensing element, wherein the at least one processor is configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards a first user in relation with the affirmative and non-affirmative determinations. The at least one processor is configured to infer and / or apply affirmative measures and / or counter-measures to reduce the drift or entropy between at least one affirmative semantic or at least one non-affirmative semantic in rapport with subsequent inferred semantics.

[0085] A robotic emulation device comprises a processor, a memory and multimedia physical interfaces. The robotic emulation device captures a video signal from a target device, analyzes it and based on the analysis, transmits manipulation control signals to the target device input by emulating a computer peripheral input device, the manipulation control signals determining identification or selection of a target interface control associated with a first user interface encoded and transmitted in the video signal. The robotic emulation device may be connected via an embedded transceiver to a at least one computer host or tenant for relaying the captured video data for semantic analysis and based on the semantic analysis applying the inferred manipulation semantics. Multiple robotic devices may be coupled, grouped and composed to host and manipulate (groups of) multiple controlled target devices. Emulated peripheral input devices may comprise keyboards, mice, trackballs, touch pads / surfaces and / or combination thereof. The robotic emulation device may emulate peripheral input device functionality of a keyboard, mouse, trackball, touch pad / surface and / or combinations thereof.

[0086] A robotic control system comprises one or more processors and a memory, the memory storing a plurality of inference models and a plurality of capabilities semantics associated with the plurality of inference models, the one or more processors being configured to launch and operate a subset of inference models among the plurality of inference models, the one or more processors further being configured to perform semantic inference by applying the subset of inference models on an input data and, aggregating the outputs resulted from applying each inference model among the subset of inference models to the input data based on the plurality of capabilities semantics.

[0087] A smart device system comprising a memory, one or more runtimes the memory storing a plurality of inference models and a plurality of capabilities semantics associated with the plurality of inference models, the smart device system being configured to select, launch and operate a subset of inference models among the plurality of inference models on the one or more runtimes and to perform semantic inference by applying the subset of inference models on an input data and, aggregating the outputs resulted from applying each inference model among the subset of inference models to the input data based on the plurality of capabilities semantics.BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Preferred and alternative examples of the present invention are described in detail below with reference to the following drawings:

[0089] Fig. 1 is a front perspective view of a preferred smart post.

[0090] Fig. 2A is a front perspective view of a preferred optical module with dome for a preferred smart post.

[0091] Fig. 2B is a front perspective view of an alternate optical module for a preferred smart post.

[0092] Figure 3 is a front perspective view of a preferred module with multi-array antenna elements for a preferred smart post.

[0093] Fig. 4 is a front perspective view of a preferred clipping module for a preferred smart post.

[0094] Fig. 5A is a front perspective view of an alternate clipping module for a preferred smart post.

[0095] Fig. 5B is a front perspective view of another alternate clipping module for a preferred smart post.

[0096] Fig. 5C is a front perspective view of another alternate clipping module for a preferred smart post.

[0097] Fig. 6A is a bottom plan view of a preferred standing and moving base.

[0098] Fig. 6B is a bottom plan view of an alternate preferred standing and moving base.

[0099] Fig. 6C is a bottom plan view of another alternate preferred standing and moving base.

[0100] Fig. 7 is a front perspective view of a preferred module having a central post.

[0101] Fig. 8A shows a representative view of a plurality of posts arranged in a guiding configuration, shown in a retracted position.

[0102] Fig. 8B shows a representative view of the posts of Fig. 8 A, show n partially extended to form a guiding arrangement.

[0103] Fig. 8C shows a representative view of the posts of Fig. 8A. shown fully extended in one of many possible guiding arrangements.

[0104] Fig. 9 show s a plurality of posts in a perimeter delimitation configuration.

[0105] Fig. 10A illustrates a plurality' of posts in communication wirelessly with a remote control infrastructure.

[0106] Fig. 10B illustrates a plurality of posts in wireless communication w ith one another.

[0107] Fig. 11 illustrates an example of a configuration of a plurality' of smart posts forming a configuration of smart carriers.

[0108] Fig. 12 illustrates an alternate example of a configuration of a plurality’ of smart posts forming a configuration of smart carriers.

[0109] Fig. 13 illustrates a plurality of smart posts, such as those in Figs. 11 or 12, but in which the telescopic capabilities of the posts define enclosed areas within a pair of composed post structures.

[0110] Fig. 14 shows nine posts arranged in a 3x3 configuration forming a combined sensing and / or processing capability.

[0111] Fig. 15 is a representative view illustrating a combination of modules A through n which may combine to form a smart post.

[0112] Fig. 16 illustrates pluralities of smart posts or similar elements shown connected via semantic fluxes.

[0113] Fig. 17 illustrates a representative map of locations and intersections of the trajectories of actual and semantic movement between nodes.

[0114] Fig. 18 illustrates an alternate representative map of locations and intersections of the trajectories of actual and semantic movement between nodes.

[0115] Fig. 19A illustrates a preferred circuit diagram for conditioning a received signal based on a modulated semantic wave signal.

[0116] Fig. 19B illustrates a preferred circuit diagram for conditioning a received signal based on a modulated semantic wave signal.

[0117] Fig. 19C illustrates a preferred circuit diagram for conditioning a received signal based on a modulated semantic wave signal.

[0118] Fig. 20 illustrates a block diagram of a plurality of elements (e.g. semantic units) coupled through a plurality of links / semantic fluxes.

[0119] Fig. 21 illustrates a block diagram of a plurality of semantic units joined through a multiplexer as a semantic group.

[0120] Fig. 22 illustrates a block diagram of a plurality of semantic cells joined through a multiplexer as a semantic group of semantic cells.

[0121] Fig. 23 illustrates a multi-stage block diagram for processing of a collection of semantic cells.

[0122] Fig. 24A illustrates a block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.

[0123] Fig. 24B illustrates an alternate block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.

[0124] Fig. 24C illustrates an alternate block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.

[0125] Fig. 24D illustrates an alternate block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.

[0126] Fig. 25 is a block diagram of a semantic system including a plurality of robotic devices and an insurance provider.

[0127] Fig. 26A is an illustration of an observer directing attention to a first endpoint within a semantic field of view.

[0128] Fig. 26B is an illustration of an observer directing attention to a second endpoint within a semantic field of view.

[0129] Fig. 27 is an illustration of a field of view mapped to a display surface.

[0130] Fig. 28 is an illustration of a field of view mapped to an alternate display surface.

[0131] Fig. 29 is an illustration of a field of view mapped to an alternate display surface.

[0132] Fig. 30 is an illustration of a field of view mapped to an alternate display surface.

[0133] Fig. 31 is a representative view of a plurality of fairings.

[0134] Fig. 32 is a perspective view of a preferred robotic pallet.

[0135] Fig. 33 is a perspective view of an alternate robotic pallet.

[0136] Fig. 34 is a perspective view of a robotic pallet including arms in an unloading or loading process.

[0137] Fig. 35 is a perspective view of an alternate robotic pallet including arms in an unloading or loading process.

[0138] Fig. 36 is a side elevational view of a robotic pallet in a loading or unloading process.

[0139] Fig. 37A an elevational view of a preferred robotic pallet.

[0140] Fig. 37B an elevational view of a preferred robotic pallet.

[0141] Fig. 38A is an alternate view of a pair of semantic posts for a robotic post system.

[0142] Fig. 38B is an alternate view of a pair of semantic posts for a robotic post system.

[0143] Fig. 38C is an alternate view of a pair of semantic posts for a robotic post system.

[0144] Fig. 39A is a close-up view of an upper portion of a semantic post.

[0145] Fig. 39B is a close-up view of an alternate upper portion of a semantic post, incorporating a hook.

[0146] Fig. 39C is an exemplary view of a first semantic post and a second semantic post in the process of connecting a hook of a lockable band.

[0147] Fig. 39D is a block diagram of a preferred semantic post.

[0148] Fig. 40A is a front elevational view of a preferred robotic shopping cart.

[0149] Fig. 40B is a front elevational view of an alternate robotic shopping cart.

[0150] Fig. 40C is a front elevational view of another alternate robotic shopping cart

[0151] Fig. 41A is an exemplary close-up view of an upper portion of a semantic post in position to connect with a piece of luggage.

[0152] Fig. 4 IB is an exemplary view of a semantic post with an arm connected to a piece of luggage.

[0153] Fig. 41C is an exemplary view of a semantic post with a holding hook for securing an item.

[0154] Fig. 41D is an exemplary view of a semantic post with a support or platform for supporting an item.

[0155] Fig. 41E is an exemplary view of a semantic post with a support of platform for supporting an item and being moveable in the direction of the illustrated arrow, and shown in a position raised above the position of the support or platform as show n in Fig. 41E.

[0156] Fig. 41F is an exemplary view of a composed semantic post with a support or platform for supporting an item container.

[0157] Fig. 41G is an exemplary view of an item container.

[0158] Fig. 41H is an exemplary view of an item container.

[0159] Fig. 42 is a representative view of a plurality of posts forming a composable gate.

[0160] Fig. 43 is a close-up view7of a preferred lockable hook.

[0161] Fig. 44A is a preferred representation of a robotic gate and panel implementation.

[0162] Fig. 44B is an alternate preferred representation of a robotic gate and panel implementation.

[0163] Fig. 45 A is a sequencing and connectivity diagram between a mobile device and a holder / cart.

[0164] Fig. 45B is a further sequencing and connectivity diagram betw een a mobile device and a holder / cart. including a provider.

[0165] Fig. 45C is a block diagram of a preferred system including a mobile device, provider, and holder / cart.

[0166] Fig. 45D is a block diagram comprising composable connection / links between two devices connected through a provider.

[0167] Fig. 45E is a block diagram comprising composable connection / links between tw o devices and a provider mapped to hierarchical endpoints.

[0168] Fig. 46A is a block diagram of a preferred account access control system.

[0169] Fig. 46B is a block diagram of a preferred cloud computing system for use with the preferred account access control system.

[0170] Fig. 46C is a block diagram of a preferred semantic sensing system.

[0171] Fig. 47A is a front elevational view' of a pair of posts with lockable bands.

[0172] Fig. 47B is a close-up view7of an upper portion of a post with a lockable band.

[0173] Fig. 47C is an illustration of a preferred band holder for a post with lockable band.

[0174] Fig. 47D illustrates a preferred spinner mechanism for a band holder.

[0175] Fig. 47E illustrates a spinner mechanism including a spring.

[0176] Fig. 47F illustrates a spinner mechanism including a plurality of blades.

[0177] Fig. 47G illustrates a preferred lock for a lockable band.

[0178] Fig. 47H illustrates an alternate preferred lock for a lockable band.

[0179] Fig. 471 is an illustration of an alternate preferred band holder for a post with lockable band.

[0180] Fig. 48 is a representative illustration of a wireless module embedded in a door lock to harvest and / or provide energy to actuate electromagnets or identify / authenticate a user.

[0181] Fig. 49A is a preferred example of a door cylinder having a spinner / lock attached or linked to a bolt.

[0182] Fig. 49B is an alternate example of a door cylinder having a spinner / lock attached or linked to a bolt.

[0183] Fig. 49C is another alternate example of a door cylinder having a spinner / lock attached or linked to a bolt.

[0184] Fig. 49D is another alternate example of a door cylinder having a spinner / lock attached or linked to a bolt.

[0185] Fig. 49E is another alternate example of a door cylinder having a spinner / lock attached or linked to a bolt.

[0186] Fig. 50 is a representative illustration of an enclosure having a spinner attached to a knob and bolt, with another spinner attached to a handle and bolt.

[0187] Fig. 51 A is a perspective view of a linearly moveable bolt in a retracted position.

[0188] Fig. 5 IB is a perspective view of a pivoting or swinging bolt in an extended position.

[0189] Fig. 51C is representative illustration of an axle / spinner supported by an exterior shell of a lock and / or faceplates.

[0190] Fig. 5 ID is a representative illustration of a preferred hand crank.

[0191] Fig. 52 is a plan view of a preferred stopper.

[0192] Fig. 53A is a view of a preferred pin-lockable actuator.

[0193] Fig. 53B is a view of an alternate pin-lockable actuator.

[0194] Fig. 54A is a front elevational view of a preferred door having a lock and a camera.

[0195] Fig. 54B is a front elevational view of a preferred door having wheels.

[0196] Fig. 54C is a front elevational view of a preferred door being secured by a lock security module attached to a post.

[0197] Fig. 54D is a front elevational view of an alternate preferred door and lock security module with a plurality of posts.

[0198] Fig. 54E is a front elevational view of split doors with attachable robotic devices.

[0199] Fig. 54F is a front elevational view of split doors with attachable robotic devices.

[0200] Fig. 54G is a front elevational view of split doors with attachable robotic devices.

[0201] Fig. 54H is a front elevational view of slide doors with attachable robotic devices.

[0202] Fig. 541 is a perspective view of a door attachable lock security module.

[0203] Fig. 54J is a perspective view of a lock security module.

[0204] Fig. 54K is a perspective view of a door attachable lock security module comprising an actuated link.

[0205] Fig. 54L is a front elevational view of a preferred door being secured by a lock security module attached to a post.

[0206] Fig. 54M, is a front elevational view of a preferred door being secured by a lock security module attached to a post.

[0207] Fig. 54N illustrates posts embedded or attached to an appliance and having a cup holder module holding a cup and / or command / augmentation interfacing modules.

[0208] Fig. 540 illustrates an appliance comprising sockets for posts.

[0209] Fig. 54P is a lateral view of an attachable robotic device.

[0210] Fig. 54Q is a lateral view of an attachable robotic device.

[0211] Fig. 54R is a lateral view of an attachable robotic device.

[0212] Fig. 54S is a lateral view of an attachable robotic device.

[0213] Fig. 54T is a lateral view of an attachable robotic device.

[0214] Fig. 54U is a lateral view of an attachable robotic device.

[0215] Fig. 54V is a lateral view of a stackable configuration of attachable robotic devices.

[0216] Fig. 54W is a lateral view of an attachable robotic device comprising a pocket for a controllable device.

[0217] Fig. 54X is an illustration of a robotic device comprising a pocket for a controllable device.

[0218] Fig. 54Y is an illustration of a robotic device comprising a pocket for a controllable device such as a laptop computer.

[0219] Fig. 54Z is an illustration of a robotic device comprising a shaped pocket for a controllable device such as a laptop computer.

[0220] Fig. 54AA is an illustration of a stackable configuration of robotic devices comprising each a pocket for a controllable device.

[0221] Fig. 54AB is an illustration of a robotic device with a controlled enclosure and / or container.

[0222] Fig. 54AC is an illustration of a memory, display and / or user interface structure.

[0223] Fig. 54AD is an illustration of a robotic device with a docked unfolded controlled device.

[0224] Fig. 54AE is an illustration of a robotic device with a docked controlled device.

[0225] Fig. 54AF is an illustration of a robotic device with a docked controlled device.

[0226] Fig. 54 AG is an illustration of a composable robotic device with a docked controlled device.

[0227] Fig. 54 AH illustrates a first robotic device manipulating a controllable device for learning purposes by a second robotic device.

[0228] Fig. 54 Al illustrates a first robotic device manipulating a controllable device for learning purposes by a second robotic device.

[0229] Fig. 54 AJ illustrates one or more robotic devices manipulating one or more a controllable devices for learning purposes by one or more other robotic devices.

[0230] Fig. 54 AK illustrates a robotic device manipulating a rotating and / or pushable controllable device (control).

[0231] Fig. 54 AL illustrates a robotic device manipulating a rotating and / or pushable controllable device (control).

[0232] Fig. 55A is a perspective view of a smart basket.

[0233] Fig. 55B is a perspective view of a smart basket.

[0234] Fig. 55C is a perspective view of a smart basket.

[0235] Fig. 55D is a perspective view of a smart basket.

[0236] Fig. 56A is a perspective view of a first post having a first folded holder surface and a second post having a second holder surface.

[0237] Fig. 56B is a perspective view of a first post and a second post having a composed holder surface.

[0238] Fig. 56C is a perspective view of a first post having a first folded holder surface and a second post having a second folded holder surface.

[0239] Fig. 56D is a perspective view of a post having a folded and an unfolded holder surface.

[0240] Fig. 56E is a perspective view of a post having a folded and an unfolded holder.

[0241] Fig. 56F is a perspective view of a post having two folded holders.

[0242] Fig. 56G is a perspective view of a post having a folded and an unfolded holder.

[0243] Fig. 56H is a perspective view of a post having a folded and a partially folded holder.

[0244] Fig. 561 is a perspective view of posts having interconnected folded holders.

[0245] Fig. 56J is a perspective view of posts having interconnected folded holders.

[0246] Fig. 56K is a perspective view of posts having interconnected folded holders.

[0247] Fig. 56L is a perspective view of a post securing a plurality of objects.

[0248] Fig. 56M is a perspective view of a post securing a plurality of objects.

[0249] Fig. 57A is a perspective view of a fastening profile having a socket / pod.

[0250] Fig. 57B is a perspective view of an alternate fastening profile having a socket / pod.

[0251] Fig. 57C is a perspective view of an alternate fastening profile having a socket / pod.

[0252] Fig. 57D is a perspective view of an alternate fastening profile having multiple sockets / pods.

[0253] Fig. 57E is a perspective view of a fastening latching profile having multiple sockets in an unlatched position.

[0254] Fig. 57F is a perspective view of an alternate fastening latching profile having multiple sockets in a latched position.

[0255] Fig. 57G is a perspective view of an alternate fastening latching profile having multiple sockets in an unlatched position.

[0256] Fig. 57H is a perspective view of an alternate fastening latching profile having multiple sockets in a latched position.

[0257] Fig. 571 is a perspective view of an alternate fastening latching profile having multiple sockets in an unlatched position.

[0258] Fig. 57J is a perspective view of an alternate fastening latching profile having multiple sockets in a latched position.

[0259] Fig. 57K is a perspective view of an alternate fastening latching profile having multiple sockets in an unlatched position.

[0260] Fig. 57L is a perspective view of an alternate fastening latching profile having multiple sockets in a latched position.

[0261] Fig. 57M is a perspective view of an extensible fastening latching profile having multiple sockets in an unlatched position.

[0262] Fig. 57N is a perspective view of an alternate extensible fastening latching profile having multiple sockets some of which are in a latched position.

[0263] Fig. 58 is a block diagram illustrating a hierarchy of containers.

[0264] Fig. 59 is a block diagram illustrating a hierarchy of endpoints and associated transceivers.

[0265] Fig. 60A is a perspective view of an engaged actuated link.

[0266] Fig. 60B is a perspective view of a disengaged actuated link.

[0267] Fig. 60C is a perspective view of an engaged actuated link.

[0268] Fig. 60D is a perspective view of a disengaged actuated link.

[0269] Fig. 61A illustrates a post having a projector which projects an image to a screen, having adjustable positions and heights.

[0270] Fig. 61B illustrates components such as in Fig. 61 A, but in which a screen or other surface or feature is stowed rather than deployed.

[0271] Fig. 61C illustrates a pair of posts supporting a composable or foldable surface, illustrated as being deployed to create a contiguous surface.

[0272] Fig. 61D illustrates a post with an attachable robotic device attached to a wireless control device for controlling a user interface on a screen or display surface.

[0273] Fig. 61E illustrates a post with an arm manipulating an attachable robotic device attached to a wireless control device for controlling a user interface on a screen or display surface.

[0274] Fig. 6 IF illustrates a post with an arm manipulating an attachable robotic device attached to a wireless control device for controlling a user interface on a screen or display surface.

[0275] Fig. 61G illustrates a robotic device manipulating a controllable device.

[0276] Fig. 61H illustrates a robotic device manipulating a controllable device for learning purposes.

[0277] Fig. 611 illustrates a first robotic device manipulating a controllable device for learning purposes by a second robotic device.

[0278] Fig. 62A is a perspective view of a conveyor unit comprising a plurality of posts.

[0279] Fig. 62B is a perspective view of a conveyor unit comprising a plurality of posts.

[0280] Fig. 62C is a perspective view of a conveyor comprising multiple conveyor units and / or bands.

[0281] Fig. 62D is a perspective view of a conveyor comprising multiple conveyor units and / or bands.

[0282] Fig. 62E is a plan view of a conveyor comprising multiple conveyor units and / or bands.

[0283] Fig. 62F is a plan view of a conveyor comprising multiple conveyor units and / or bands.

[0284] Fig. 62G is a plan view of a conveyor comprising multiple conveyor units and / or bands.

[0285] Fig. 62H is a plan view of a conveyor comprising multiple conveyor units and / or bands.

[0286] Fig. 621 is a plan view of a conveyor comprising multiple conveyor units and / or bands.

[0287] Fig. 62J is a plan view of a conveyor comprising multiple conveyor units and / or bands.

[0288] Fig. 62K is a plan view of a conveyor comprising multiple conveyor units and / or bands.

[0289] Fig. 62L is a plan view of an extended conveyor comprising an attachable conveyor to a main conveyor.

[0290] Fig. 62M is a plan view of an extended conveyor comprising an attachable conveyor to a main conveyor.

[0291] Fig. 62N is a plan view of an extended conveyor comprising an attachable conveyor to a main conveyor.

[0292] Fig. 620 is a plan view of an extended conveyor comprising an attachable conveyor to a main conveyor.

[0293] Fig. 62P is a plan view of an extended conveyor comprising an attachable conveyor to a main conveyor.

[0294] Fig. 62Q is a plan view of an attachable conveyor engagement to a main conveyor.

[0295] Fig. 62R is a plan view of an attachable conveyor engagement to a main conveyor.

[0296] Fig. 62S is a plan view of an attachable conveyor engagement to a main conveyor.

[0297] Fig. 62T is a plan view of an attachable conveyor engagement to a main conveyor.

[0298] Fig. 63A is a perspective view of an engagement coupling gear.

[0299] Fig. 63B is a perspective view of an engagement coupling gear.

[0300] Fig. 64A is a plan view of an engagement coupling belt or chain.

[0301] Fig. 64B is a plan view of an engagement coupling belt or chain.

[0302] Fig. 65A is a schematic view of an electrical interconnection and / or charging topology.

[0303] Fig. 65B is a schematic view of a switching regulator.

[0304] Fig. 66A is a plan view of an electrical and / or signal connector.

[0305] Fig. 66B is a plan view of an electrical and / or signal connector.

[0306] Fig. 67 is a top plan view of a conveyor comprising a plurality of actuated barriers and / or wedges.

[0307] Fig. 68A is a perspective view of a conveyor structure comprising a shell or case panel.

[0308] Fig. 68B is a perspective view of a conveyor structure comprising a shell or case panel.

[0309] Fig. 69A is a schematic view' of a preferred robotic emulation device attached to a target device and optionally connected to a local / remote computer host or tenant.

[0310] Fig. 69B is a schematic view of a preferred robotic emulation device attached to a target device and optionally connected to a local / remote computer host or tenant.

[0311] Fig. 69C is a schematic block view of a preferred robotic emulation device attached to a target device.

[0312] Fig. 69D is a schematic block view of a preferred robotic emulation device attached to a target device.DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0313] The present invention relates to versatile smart sensing robotic posts, appliances and systems. Such systems can be used in various environments including airports, hospitals, transportation, infrastructure works, automotive, sport venues, intelligent homes and any other circumstances. In one version, the posts serve as stanchions and include clips or connectors for belts or ropes which may optionally be retractable within one or more of the posts. In this form, the smart posts may be used as barricades or crowd control in areas where it is desired to restrict or organize access to certain areas by a population.

[0314] In further use cases the smart posts may be used as appliances and smart infrastructure for applications such as robotics, wireless communications, security, transportation systems, scouting, patrolling etc.

[0315] The system may perform semantic augmentation, wherein the system uses semantic analysis for inferring / presenting / rendering / conveying / gathering information in optimal ways and / or using particular modalities based on circumstances, challenges, users and / or profiles.

[0316] In further application the smart posts are used for semantic augmentation via incorporated displays, speakers, actuation and other I / O mechanisms. In some examples, a display is mounted on the post and / or top of the post.

[0317] In further examples, the smart posts may comprise smart pop-up signs which allow traffic control (e g. REDUCED SPEED, CONTROLLED SPEED etc ). Alternatively, or in addition, the posts may comprise other semantic augmentation capabilities and / or outputs. It is to be understood that the signs / posts may register their capability' semantics on the semantic system and the system controls them based on semantic augmentation and / or analysis including semantic time management (e.g. REDUCED SPEED UNTIL ACCIDENT CLEARS, CONTROLLED SPEED UNTIL TRAFFIC FLOW IS NORMAL etc ).

[0318] The preferred smart posts (or appliances) may move independently or may be installed on moving vehicles and any other moving structures; alternatively, or in addition they may be installed on fixed structures such as walls, floors, and so on for sensing and control purposes.

[0319] Typically, a preferred post has sensing elements including at least a vision element such as a camera, and an array of antenna elements receiving and / or radiating electromagnetic radiation. The electromagnetic radiation may use various frequency spectrums including but not limited to low frequency, ultra-high frequency, microwave, terahertz, optical and so on. The camera and / or vision element may operate in visual, infrared and any other optical spectrum. It is to be understood that sensing elements may provide time of flight (TOF) capabilities.

[0320] In addition to electromagnetic energy sensing the smart robotic posts may include other sensing modalities (e.g. microphones) and / or any other analog and / or digital sensors and transducers used for other environmental measurements and detections (e.g. pressure, sound, temperature, motion, acceleration, orientation, velocity etc.). It is to be understood that such elements may be disposed in an arrangement about the smart post to enable detection of environmental conditions or parameters in geographic areas or zones about the post.

[0321] The system may use environment profiling and learning based on corroborating radiofrequency energy returns with optical (e.g. camera) sensing wherein both modalities sense conditions in the semantic model (e.g. at various endpoints) and create semantic artifacts (e.g. semantic groups, semantic routes) based on sensed conditions and semantic analysis. In an example the system determines artifacts through camera frame sensing and / or inference operating in optical spectrum and groups them with artifacts sensed and / or inferred through antennas operating in the microwave spectrum. Thus, the system may be very particular on conditions and inferences that resemble learning groups and patterns.

[0322] As depicted in Fig. 1 a preferred smart post 101 comprises a base 1 (which may optionally include a plurality of (geared) wheels or casters 10 in the case of a mobile smart post), a power section 2, a trunk section 3, a structure fixation and manipulation portion 4, a control section 5, a clipping area 6, a portion supporting one or more antennas 7, and an optical sensor portion 8. While the illustrated embodiment shows a hexagonal design (as viewed in a horizontal cross section taken through a vertical axis, in which the vertical axis extends centrally from the base to the optical sensor portion) it is to be understood that it can be shaped differently (squared, pentagonal, octagonal, circular etc. in other versions. Also, other modules may be incorporated with such smart posts including a copter module (e.g. for aerial transportation) and a display module (e.g. for providing semantic augmentation).

[0323] In one example of the invention, the smart post includes all or a subset of the components listed above and illustrated in Figure 1 in a manner in which they areintegrated into a generally unified structure, such as a single pole or post having a hollow center and in which the listed components are attached or inserted into the post. In other versions, the components described above are generally assembled separately, such that they are produced as modules which are joined together to form the post. Thus, each of the above sections or regions or portions may be separately formed modules which are joined together, or may be separate portions of a unitary’ post or similar structure. In the discussion which follows, for the sake of simplicity' each of the foregoing will be referred to as a module; it should be understood, however, that the same description applies to other embodiments in which the module is a portion or section of the smart post, and not necessarily a discrete module. It is to be understood that the post may use any number of modules of any type. In an example, a post may comprise multiple power modules and / or multiple antenna elements modules and / or multiple cameras modules.

[0324] The base 1 may comprise wheels 10 and its movement be controlled via motors, actuators and other control components or interfaces by a computer (or the equivalent, such as a processor having a memory and programming instructions) embedded in the robotic post. The standing base may comprise suspension (e.g. springs, shock absorbers, coils, coil- overs, piezo components etc.) and attachment mechanisms for wheels or for attaching to a structure (e.g. truck, automobile, container, pallet, conveyor etc.).

[0325] In further examples, the wheels may be geared / toothed wheels(or gears) which may be further engaged (with various surfaces / objects / conveyors) via ensembles such as depicted in Fig. 620-T, Fig. 63 A-B, Fig, 64 A-B.

[0326] Figs. 6A-C illustrate bottom plan views of the standing and moving base 1 in various embodiments comprising attaching mechanisms 20 and / or driving wheels 21. The (driving) wheel or wheels may' mount on attaching mechanisms and / or be retractable, tension- able and / or spring-able (e.g. for using, holding and releasing energy' for achieving particular compressions, extensions and / or motions); in an example, the post may use any three wheels, each on any non-adjoining edge / segment of the hexagonal shaped base while the other wheels may be inactivated and / or retracted. Analogously the driving wheels may function on similar principles (e.g. activate particular ones based on (semantic) circumstances and / or semantic groups). . Further, the mounts (wheel mounts, ball type mounts, module connecting mounts, band connecting mounts etc.) may be controlled (e.g. by compression, extension etc.) by semantic actuation based on observed circumstances. In an example, some mounts’ compression is stiffened and others loosened when the system uses, observes and / or infers a trajectory' which would determine an 80 HARD LEFT LEAN semantic; further, the 80 HARDLEFT LEAN may use further routes such as WHEEL MOUNT GROUP LEFT 75 COMPRESSION, WHEEL MOUNT GROUP RIGHT 25 COMPRESSION.

[0327] In further examples, at least two post rectangular bases comprise each four wheels in a rectangular partem one for each edge; when j oined on one of the lateral edge faces the base allows a combined support and thus the center of gravity moves towards the joining edge face. Instead of using the combined eight wheels for movement the combined post may use any inferred particular group from the combined base (e.g. in a triangular partem, rectangular partem etc.) and thus adapting to conditions, movements and efficiency.

[0328] Each module may comprise a computer or controller, memory’ or other computing units. While illustrated as separate modules, in other versions one or more physical modules and / or their functionality may fuse or be distributed among fused modules. For example, the standing base and moving module 1 may be fitted with a power supply such as one or more Li-Ion batteries, and therefore may serve as a single consolidated base and power supply module rather than two separate modules. In other embodiments, the power, control and antenna elements are combined in a single module rather than separate modules joined together. In yet other embodiments the trunk and antenna panels extend to the whole surface of the post.

[0329] The power module may comprise batteries (e.g. Li-Ion), fuel cells, super capacitors and / or other energy storage components. The electrical storage components may be charged via physical plug-in, wireless or any other charging technique.

[0330] As explained, multiple modules, whether physical or logical may fuse into a larger trunk module. In some examples such fused trunk module is telescopic and extensible, facilitating dynamic reconfiguration settings.

[0331] In some embodiments the standing base module and the trunk module are telescopic thus allowing height adjustment. The telescopic movement may be controlled through electric motors powered through the power module and controlled by the control module.

[0332] In some versions, the modules may be carried on a supporting post or frame, which may be configured as a central post defining a central vertical axis for the smart post. The modules may be attached to the post 9, as shown in Fig. 7, through a variety of mechanism with the preferred version being that the post comprises a frame on which modules slide, attach and lock / unlock (e.g. fig 7 middle column 9). In some versions the supporting post or frame comprises backplanes, connectors and / or communication buses; when slide into place the modules connect (e.g. via connectors) to the backplane, connection and / or communicationbus, thus allowing flexible module interconnects (e.g. Fig 15, showing a plurality of modules which includes Module A, Module B, and continuing through Module n).

[0333] Alternatively, or in addition, in other embodiments the modules comprise interlocking and interconnect features such as tongues and grooves, pegs and cavities, tabs and slots, clips, clamps and / or other interconnect systems that allow the modules to lock to each other while being stacked. Interconnect mechanisms allow the modules to be in signal communication via a composable bus formed by interconnecting buses of each module. It is to be understood that the buses may comprise electrical and / or optical components.

[0334] In some embodiments a collection of any types of modules may also communicate wirelessly viatransmit / receive components, antennas and / or panels embedded in each module. In some embodiments the communication between modules take place in the same post and / or other posts.

[0335] The modules may be in signal communication and communicably coupled for various purposes including for transmit / receives command signals via buses, providing status information (e.g. battery charging status), semantic augmentation (e.g. airline name, flight information, routing information etc.) and so forth. Post to post communication may also occur in such situations and further when the system infers, groups and / or deploy posts and units in particular configurations and / or missions.

[0336] In an example, the control module provides commands to actuators incorporated in the base module for guiding the posts through environment. Further in the example the control module may infer semantic routes such as GO TO LOCATION A and further TURN LEFT UNTIL ON THE DIRECTION OF LOCATION A and further when detecting a curb MODERATELY ACCELERATE TO CURB AND JUMP. The system may further infer from JUMP and HIGH CURB to LOAD SPRING 1 HIGH (e g. commanding driveline suspension spring 1 to load high tension via electrical motor actuation) and RELEASE SPRING 10 (e.g. high energy release) once HIGH CURB CLOSE. As mentioned, the control units command actuation based on such commands (e.g. commands electrical motors of the base module driveline, controls voltages, currents and / or electromagnetic fluxes / properties in time of such components etc.). While the previous example has been referred to communications between modules of the same post it is to be understood that similar use cases for post units and / or groups may require inter post communication and command whether master-master and / or master-slave.

[0337] In some examples the carriers command semantic groups of posts and / or modules in order to achieve particular movements. In an example, a composite 3X3 carriermay need to climb a stair and as such it may command rows of posts independently at particular times for achieving the goals.

[0338] The system elevates at least the first row of posts from the ground once in proximity of a stair and further moves forward and elevates further rows in order to climb the stairs while always maintaining the load initial posture (e.g. horizontal agnostic).

[0339] In an example of a climbing system the robotic system may be considered as formed from a number of rows and columns rows and columns and groups thereof. Thus, when climbing a stair at least the front upper row of modules moves upward (e.g. via telescopic means) and slide forward and rests at a first time on at least the second stair up from the current position. Once in position the lower level horizontal rows move in position forward on the subsequent stairs under the upper row position’s stairs and generate telescopic lift for the upper level horizontal rows that will detach from the upper stair / s, slide up and forward to attach to higher upper stairs and generate support for the ensemble allowing the lower level rows to detach from the supporting position and slide up and forward to upper stairs. While from the horizontal rows point of view stairs ascent is based on row movement such as slide up and forward, from the vertical columns point of view the movement is telescopic and / or retractable to elevate the horizontal rows. Analogously with stair ascent, stair descent is based on moving the vertical columns in a slide forward and down movement while the horizontal rows use a telescopic and / or retractable movement to slide forward the vertical columns. It is to be understood that in some cases the carrier may turn over on one side (e.g. such a vertical row' become horizontal and vice-versa) and / or reconfigure its layout for the particular mission (e.g. ASCENT, DESCENT etc.).

[0340] While in the example we may have referred to “row” and / or “column” it is to be understood that they may be used interchangeably with “semantic group of rows” and / or “semantic group of columns” and further, in a hierarchical manner, of semantic groups. The selection of rows and / or columns of sliding, telescoping, retracting and / or lifting elements may be based on semantic group inferencing which may also take in consideration the lift weight and height (e g. weight of carrier and load, height of load, height of telescoping areas, height of stairs etc.). Other factors such as surface traction grip, environment conditions and other factors may also come into effect.

[0341] In other examples, the semantic posts may use group leverage to achieve goals such as changing positions, lifting, jumping, getting straight and / or out of the ground. In an example, at least one post is sideways on the ground (maybe because it was pushed to the ground by external factors) and other posts are used to lift the fallen post and move it back tovertical position. In further examples at least two posts have fallen, and they leverage each other to lift to vertical position based on side by side maneuvering, latching, hooking, lifting, pushing and / or pulling.

[0342] It is to be understood that in some cases the post deployments based on semantic routes may be based on the semantics associated with various locations and / or other information. In an example the system detects that the area of GATE A having a scheduled DREAMLINE AIRLINE flight is DELAYED or boards later and hence smart posts at the gate may be re-deployed to other locations and areas based for example on a reward-based system. In such a system, the posts are deployed to locations associated with semantics having high rewards and incentives while pondering the total rewards (e.g. via opposite sign weights and / or rewards) with the accessibility, deployment and routing semantics in the semantic network model. In an example, the system infers a goal of redeploying the posts to a HAZARDOUS area (e.g. area B and / or via endpoint associated with B) which may entail high rewards in a particular circumstance however, routes and / or accessibility to the area are not available immediately (or maybe too busy) and / or maybe power scarcely available and thus increasing risk and / or lowering the total rewards of evaluating pursuing the goal via location endpoint B. In addition, the semantic inference allows goals, rewards and / or semantic routes to be adjusted and / or selected based on further semantic routes, goals and / or rewards (e.g. MINIMIZE COST AND RISK, MOVE FAST, MAXIMIZE POWER CHARGING etc ). It is to be understood that the semantic routes and / or goals may be hierarchical and compositional with higher-level abstraction semantic routes and / or goals comprising lower-level abstraction semantic routes and / or goals in a hierarchical and / or compositional fashion. Such hierarchy may be determined and / or mapped to hierarchies and topologies in hierarchical semantic network models thus allowing the semantic inference to pursue selectively (e.g. based on higher level endpoints comprising a low er level sub-model comprising a selection of endpoints and / or links) and hierarchically from lower to higher and higher to lower abstraction (e.g. endpoint) levels.

[0343] While in the previous examples a rewards-based system has been exemplified, it is to be understood that analogously other factors and indicators may be used for inferring, setting and / or evaluating semantic routes and / or goals (e.g. based on risk, cost). Further, such factors and indicators may influence one another via semantic inference (e.g. 10 RISK infers HIGH COST. HIGH COST infers HIGH RISK, HIGH RISK infers HIGH PAY REWARD, high reward goals infer high risk routes etc.).

[0344] The system may perform semantic factorization wherein a quantifiable (semantic) factor / indicator associated with a semantic artifact is adjusted based on semanticinference / analysis. It is understood that when referring to ‘’factorization” in this disclosure it may refer to “semantic factorization”. Semantic factorization techniques may be used such as explained in this application (e.g. based on semantic time management, decaying, indexing, resonance, (entanglement) entropy, divergence, damping etc.).

[0345] Semantic factorization may entail semantic decaying.

[0346] Semantic decaying occurs when a quantifiable factor / indicator associated with a semantic artifact decays or varies in time, most of the time tending to 0; as such, if the parameter is negative decaying is associated with increases in the semantic factor value and if the factor is positive decaying is associated with decreases in factor's value. Sometimes, when the semantic decays completely (e.g. associate factor is 0) the semantic may be inactivated, invalidated or disposed and not considered for being assigned to an artifact, semantic route, goal, semantic rule, semantic model and / or inference; further, based on the same principles the semantic is used in semantic group inference and membership.

[0347] Semantic factors may be associated with values of control voltages and currents in analog and / or digital components and blocks. Analogously, other material and further emission, dispersive, diffusive and / or quantum properties may be controlled (e.g. electromagnetic flux, conductivity, photon / photoelectron emission, polarization, etc.).

[0348] Decaying and semantic factors may be inferred and learned with semantic analysis. In some examples the system leams decaying and semantic factors for semantic rules and / or semantic routes.

[0349] The clipping module 6 (see Fig. 4) comprises bands and clips that can be used to hook up or pair two posts, such as by the attachment of opposite ends of a band, rope or belt to two separate posts. Each clip module has at least one band (see Fig. 4 showing one end of a band having a clip 25 attached, in which the band is retracted within the module) such that the attached clip or hook that can be used to clip together at least two posts by joining to a band clip insert or attachment point 26 on another post. The bands can therefore be extended to form a perimeter by moving and guiding the posts to the desired location. Once coupled or hooked the posts may move, thus extending the clipped bands and creating various configurations, potentially delimitating semantic zones (e.g. traveler or automotive guiding lanes, hazards emergency lanes, parking areas / lanes / space, work zones etc.). It is to be understood that while bands are exemplified for simplicity, other types of physical couplings may be used such as foldable barriers, nets etc. Alternatively, or in addition to the physical couplings the posts system may be performing the access control and / or zoning function viaphysical movement and / or sensing means (e.g. laser, vision, radiofrequency and / or other modalities).

[0350] Analogously, when the posts need detaching, they may move towards each other in order to detach the band clips at a closer distance in order to avoid band dangling. In other examples the posts detach while at farther distances and the band rolls attenuate the retraction movement through amortization or controlled retraction (e.g. based on springs and / or electrical means). It is to be understood that the semantic posts may perform clipping / unclipping, unfolding / folding of the bands, barricades and / or nets once they are commanded to allow / deny / control access.

[0351] In some examples, the posts may not move to each other in order to perform clipping but rather perform the shooting of drive threads, ropes and / or cables towards each other that may hook once colliding in the air (e g. male-female type of hooking, where one thread is a male connector and the other thread is a female connector). Once disconnecting such threads, ropes and / or cables may have mechanisms to manipulate the end hooks and latches.

[0352] Figs. 5A-C show further exemplary7preferred embodiments for coupling mechanisms to affix belts or bands from one post to another post. The coupling mechanism between two clips or hooks may comprise a sliding mechanism 31. insertion lock mechanism 32, hook lock mechanism 33, turning mechanism, plug and lock mechanism, latching an any other techniques. The sliding mechanism comprises hooks, clips or grooves that slide into each other via horizontal or vertical movement. The plug and lock mechanism may comprise plugs that lock into each other once connected. In a similar way the latching mechanism latches the hooks once connected. It is to be understood that any of these techniques use mechanical and / or electrical means for such clippings and latches and can be combined in any configuration.

[0353] The semantic posts may comprise a (foldable) barrier (or panel / net) mechanisms and / or modules. The barrier mechanism / module may comprise / control multiple barrier segments (e.g. from plastic, metal, fabric and / or any other material) which can be folded and / or extended thus forming shorter or longer barriers used to adapt to (semantic) access control needs (e.g. entry7points, controlled areas / endpoints etc.). Such barriers may comprise segments / panels (with grooves) which swivel, slide, extend and / or retract within / between each other with the sliding / swiveling movement being controlled via (electro)magnets, toothed rails, strings and / or cables. The barrier mechanism / module allows the barrier to lift / raise / drop / deploy / un-deploy / fold / unfold based on semantic access control. It is to be understood that the barrier segments may be folded and / or stowed thus shortening the barrierto a particular / minimum size. Further, the (compacted / folded) barrier may be stowed along the vertical length of the posts; further, the (compacted / folded) barrier may slide dow n along the vertical side of the post and thus, adjusting the height of the post to an optimal / minimum height. A barrier may comprise a panel / net and / or any other physical divider.

[0354] The barriers from at least two semantic posts may join and / or lock together using joining and / or locking mechanisms; such mechanisms may comprise mechanical and / or magnetic components. In some examples, the tips of the barriers comprise magnets which when in vicinity attract and lock together. Magnetism in the components may be controlled by semantic units (e.g. via a voltage, current, inductance, magnetic flux etc.) and thus controlling the timing (e.g. by time management) and / or intensity of the attracting and / or repelling magnetic fields.

[0355] Two joining posts may use j oining / composite capability / capabilities for communication, networking and / or energy transfer. In some examples, the bands, clips, barriers and their latches / connections / tips incorporate feed cables and connections.

[0356] It is to be understood that while in some examples the posts comprise capabilities such as joining and / or delimiting bands, barriers, pop-up signs and so forth in other examples they may lack such capabilities.

[0357] The semantic zoning and access control may be implemented by physical moving and positioning of the posts (e.g. as blocking posts, delimiting posts, guiding posts, semantic zoning posts etc.). In some examples the posts may or may not comprise joining and / or delimiting elements.

[0358] The semantic zoning and / or access control can be based on the augmentation provided via pop-up signs (e.g. capabilities, rise / fall commands etc.), displays (modules) attached to the semantic posts and / or other semantic fluxes.

[0359] The semantic posts may be controlled via a centralized and / or distributed computer system where the functionality is distributed among pluralities of control modules and / or other external computers, computer banks or clouds. In some examples the distributed computer system is organized in a hierarchical manner.

[0360] The powder module may comprise a power hooking mechanism that is used to plug-in and recharge the power module. It is to be understood that the plug-in may be automatic based on sensing and robotic capabilities. In an example, the charge socket is localized via sensing and the system guides a post’s rechargeable plug via orientation and / or routing in a semantic network model where at least one endpoint is mapped to the location of the charge socket; further, at lower endpoint levels other location based features and / or shapesof the socket are mapped and used with orientation and routing. It is to be understood that the location of the charge socket may be mapped and detected via any available sensing technique or a combination of those. In some examples, shapes, sockets and / or its features are detected via camera sensing (e g. frame processing based on deep learning, semantic segmentation, semantic analysis etc.). Further, the power module can be attached or detached by sliding and / or lifting the assembly (e.g. other modules, trunk) on top of it, potentially using the attached hooks and further lifting the power module and replacing it with another one.

[0361] The structure fixation and manipulation module 4 is used to attach the smart post to various fixed and mobile structures including walls and bases in any orientation. In some examples the base is a structure of a vehicle / carrier, drone, aircraft or any other mobile structures. In similar ways with the clipping the fixation module it may incorporate various latching, hooking and clipping mechanisms for attachment that may be present sideways and / or underneath. Further, the latching and locking mechanism may allow the movement and orientation of posts in various angles.

[0362] In some embodiments the clipping module and / or the structure fixation and manipulation module are used to compose larger formations and / or structures of smart posts. In some examples, those formations are based on semantic inference and semantic groups of posts. In an example, a group of smart semantic posts are joined together to form a larger structure (e.g. a larger transportation system, trailer unit, bed truck, vehicle, drone etc.). It is to be understood that the composable structure can comprise a variety' of configurations of the smart posts; for example there may be posts in the structure comprising sensing units such as optical module and / or antenna elements module while other posts in the structure (e.g. used to compose a flat transportation bed) may not have such capabilities (e.g. comprise a combination of the moving base module, power module, clipping and fixation module, control module and / or trunk module including any telescopic capabilities). Figs. 11 and 12 present example of such configurations where smart posts (for example, posts 101a through lOle; for simplicity, not all posts shown in Figs. 11 or 12 are labeled) are used in conjunction to form various configurations of smart carriers. As shown in those examples the system composes the sensing able posts with reduced posts (lacking some sensing capabilities) in order to form smart flat carrier beds.

[0363] Such composable configurations may be based on goals, missions and rewards thus, the system selecting the optimal configuration. In further examples, mission collaboration may occur where goals and / or sub-goals are split, challenged and / or distributed between modules, posts and / or semantic fluxes by semantic leadership.

[0364] In a similar manner of posts structure composability other smart carriers, (counter)measures and / or (group) formations may be achieved. In an example a group of posts are used to form, hook up and / or carry (groups of) (affirmative) (allowed) (counter)measures (e.g. a nets, water jets etc.) (for drone neutralization (sub)goals, interests and / or purposes(e.g. disablement, landing to (affirmative) endpoints etc.) and / or (associated) (intrinsic) (hierarchical) (higher level) goals / interests / purposes such as '‘keep airport (area) safe (for passengers / airplanes (takeoff / landing)) / (from collisions)”). In other examples, a group of posts hook up and carry drone neutralization (affirmative) measures and / or drone (goal / interest / purpose) (affirmative) (allowed) counter measures(e.g. to determine landing, drone goal / purpose steering / disablement etc.). It is to be observed that the intrinsic purpose of an airport is related with safety of passengers / airplanes and / or (as a sub goal) safety of takeoff and landing(wherein the takeoff and landing are determined as leadership factorized activities of / at the airport (endpoint) (object) and / or associated with the intrinsic purpose of the airport); as such, all (counter)measures may be affirmative (in the system views) with such purpose. Also, (unauthorized) (flying) drones may be non-affirmative with such purposes; thus, the system may apply affirmative measures in rapport with the airport purpose / goals and / or apply counter-measures against drone’s goals / interests wherein the drone's goals / interests are highly entropic with the airport's purpose and / or the systems goals; alternatively, or in addition, the system may apply affirmative measures in rapport with the airport purpose / goals and / or drone purpose / goals wherein the drone’s goals / interests are not highly entropic with the airport’s purpose and / or the sy stems goals. It is to be observed that affirmative measures in rapport with the airport purpose / goals may entail (counter) measures and / or non-affirmative measures in rapport with the drones goals / interests / purposes. In some examples the system deems an area as needed to be cleaned up of drones and based on the goal the system launches ANTI DRONE and DRONE DESTROY missions and routes. Such missions may be inferred for example based on user or flux feedback and / or input (e.g. mark an area, endpoint and / or trajectory' as CLEAN OF DRONES IN 20 MINUTES etc.). It is to be understood that those missions take in consideration the chain of authorization and / or hierarchy (e.g. of users and / or fluxes) in order to avoid potential conflicts. In an example, an area-based endpoint EC encompasses area-based locations EA and EB. When semantics and missions from a higher-level authorization is marked and / or established for such areas they will take leadership over lower authorization levels; the system pursues goal based inference on such missions with leadership associated to higher level authorization semantics, missions and groups; in the case of increased superposition (e.g. potentially based on a entropy and / or superposition indicator, factor, rateand / or budgets) the system may perform superposition reduction by asking for additional feedback (e.g. from a user, identity or semantic group based on authorization level, flux etc.) and / or assigning additional bias based on profiles and / or preferences. If no feedback or profile is available, the system may perform the missions based on higher levels policies and / or hard route semantic artifacts. It is to be understood that the authorization levels may be inferred for various semantic identities, semantic groups and / or semantic profiles based on semantic analysis and leadership. Thus, in a first context (e.g. as determined by a semantic view, route etc.) a semantic group A might be assigned a higher authorization level than semantic group B while in a second context the group A might be assigned a lower authorization level. In addition, or alternatively, the authorization levels (access control) are assigned based on inferred semantic artifacts (e.g. semantic routes, semantic profiles etc.) and the system uses the semantic artifacts and further projections for further inference and validation of authenticity7.

[0365] A confusion semantic factor may be inferred based on the incoherent and / or coherent superposition factors, indicators, rate and / or budgets wherein the confusion factor is high if the incoherent superposition is high and / or coherent superposition is low. Analogously, the confusion factor is low when the incoherent superposition is low and / or coherent superposition is high.

[0366] The system may prefer coherent semantic artifacts during analysis when the confusion factors are high and may use more incoherent semantic artifacts when the confusion factors are low.

[0367] Allowed confusion factors thresholds, intervals and / or budgets may be inferred, ingested, adjusted and / or predefined by inputs from users, semantic fluxes and semantic analysis. Confusion factor semantic intervals may be associated with semantic artifacts (e.g. semantic routes and / or rules) thus, allowing the system to apply such artifacts when the system exhibit a particular confusion range. In some examples, the higher the confusion factor, the higher priority based on leadership and / or factorization have the rules that are associated with such intervals (hard routes and rules may have explicitly or implicitly the highest priority ).

[0368] In cases where the allowed confusion is high and / or unbounded the system may exhibit an undetermined (time) interval of confusion and thus the system may use further semantic rules (e.g. access control, time management rules) to restrict and / or bound the confusion interval.

[0369] The system may adjust factors, budgets and or quanta in order to control the inference towards goals and / or keep (goal) semantic inference w ithin a semantic interval.

[0370] The system may infer DO NOT and / or H / ENT semantic artifacts (e.g. rules, routes, constraints etc.) associated with the semantic artifacts which generated (increase in) confusion (in semantic views).

[0371] Increases in confusion may be assessed based on thresholds, rate of increase, mapped overlays, indexing, hysteresis etc.

[0372] In further examples, when semantic areas intersect, overlap and / or are contained, the system may use the semantic areas depth axis (e.g. Z axis) attribute for hierarchy determination and for establishing the leadership semantics. In one example, if the area associated to endpoint EB is specified on the Z axis on top of area associated to EC. the system may provide more leadership bias towards semantic artifacts associated with higher placement on the Z axis, in this case EB. While the example specifies the positive bias towards higher Z axis factors it is to be understood that such biases may be configurable or provided as part of semantic profiles (e.g. associated with users(including persons, items, objects, containers, posts etc.), identities, semantic groups, semantic artifacts etc.).

[0373] In further examples, the system diffuses (only) leadership semantics between / through the (hierarchy of) (hierarchical) endpoints / links.

[0374] It is understood that the authorization rights and levels may be based or assigned on hierarchy levels and / or artifacts in the semantic model. For example, the right for DRONE SHUTDOWN related artifacts may be assigned to particular semantic groups (e.g. of users, semantic posts, endpoints etc.). While the previous example relates to a more specific application it is to be understood that the semantic network model inference may be guided by semantic superposition factors and / or biases provided in the context of semantic profiles and / or authorization at various hierarchy levels.

[0375] In some examples two endpoints may be associated with two zones which overlap (e.g. by coordinates, geographically, semantically etc.; two propert / facility areas overlapping on a no man’s land zone between two properties mapped to endpoints). Further, if the endpoints are associated with semantics and narratives and the endpoints are associated each with various semantic fluxes and / or agreements then the system may infer the intersection endpoint (a third endpoint) as an area associated with an inferred agreement (e.g. based on strong factorization) between the two semantic fluxes and / or agreements based on semantic analysis. Further, at least one endpoint associated and / or comprising the first and the second (and potentially the third) endpoints and based on the reunion of those zones may be associated with the semantics, agreements, fluxes and / or narratives of / at the two endpoints plus additional semantics, agreements, fluxes and / or narratives resulting from semantic analysis onsuch composable artifacts. Thus, the system infers and maintain hierarchical structures of semantic artifacts which help assign the law of the land and / or agreements to various mappings. It is to be understood that law of the land and / or agreements may be composed and comprise various semantic artifacts associated and / or particularized with semantic groups, semantic identities and so forth; further semantic analysis of the composable laws of the land may be based on semantic groups and / or semantic identities (e g. TRUCK OPERATORS ((INCLUDING / EXCLUDING) / (+ / -) S2P2), NURSE / S HOLDING A NEWSPAPER, JOHN’S TULIP CARRIER etc.). It is to be observed that the semantic identities (e.g. NURSE / S HOLDING A NEWSPAPER. JOHN’S / DOES TULIP CARRIER / CONTAINER etc.) may be developed in time based on semantic inference and may be related with semantic groups; further they can be inferred by semantic grouping. In an example semantic identity of NURSE HANDS and of a NEWSPAPER are formed as a semantic dependent group. In other examples, a semantic trail / route of NURSE, (HANDS, HOLD), NEWSPAPER may be used. In cases where the semantic identity and / or group collapses (e.g. to one artifact) in the inferred circumstances (e.g. as reflected based on semantic views and semantic artifacts) the system may be more specific about the semantic identifiers (e.g. “THE” NURSE HOLDING A NEWSPAPER, NURSE JANE, HEALTH AFFAIRS etc.). Further, the system may associate, group and / or learn semantic routes and / or rules (e.g. NURSE, HOLDING THE NEWSPAPER, WEDNESDAY, AFTER LUNCH - (NURSE) JANE (99.99%); (NURSE) (JANE), HOLDING THE NEWSPAPER, WEDNESDAY AFTER LUNCH - 70% etc ). Such inferred and learned artifacts may comprise time management (e.g. WEDNESDAY AFTER LUNCH); further, based on the semantic route and the identification of JANE it may create behavioral routes for the semantic identity comprising leadership semantics (e.g. NURSE and / or more precisely for NURSE JANE and / or JANE). It is to be observed that a semantic group may be specified by a / an (inferred) group semantic identity(e.g. TRUCK OPERATORS); alternatively, or in addition, an IN(or inclusion(or addition(or +))) and / or OUT(or exclusion(or subtraction(or - ))) (diffusion) (directional) (link) (filter) specifier may be indicated in association, linked and / or comprised with / in the group semantic identity(e.g. (TRUCK OPERATORS) (INCLUDING / EXCLUDING) / (+ / -) S2P2); in examples, “TRUCK OPERATORS” (->) IN(CLUDING) / + S2P2 indicates that the corresponding group refers to truck operators including, IN diffusing(to(wards) the group (endpoint(s)) and / or adding S2P2; in further examples, “TRUCK OPERATORS” (->) EXCLUDING / OUT / + S2P2 indicates that the corresponding group refers to truck operators excluding, filtering, OUT diffusing(out(wards) the group (endpoint(s)) and / or subtracting S2P2; further, IN / OUT diffusing may entail anIN / OUT (wards) diffusion and / or link to / from the larger group '‘TRUCK OPERATORS” (endpoint(s)).

[0376] As it is observed an artifact (e.g. person / nurse / Jane) may be identified by (inferred) grouping, possession (e.g. NURSE WITH A NEWSPAPER), activity and / or (associated) semantic times and / or endpoints. Alternatively, or in addition, a semantic identity comprises an activity at an endpoint (e.g. nurse / Jane manipulating a reading station in the CT room etc.).

[0377] The system may determine high entropic semantic identities for better identification within a population and / or group. As such, in order to differentiate within a group the system may look for a leadership semantic attribute, activity, endpoint and / or semantic time and / or (further) semantic identity which has a high entropy among the (other) members of the group and / or is resonant with the goals; a semantic attribute may be determined based on inferred possession. In examples, in order to differentiate at an endpoint amongst nurses / people the system may specify NURSE WITH A NEWSPAPER based on the determination that the other / majority of the nurses / people do not possess and / or carry / hold a newspaper and / or can be identified as a nurse (with a newspaper); alternatively, or in addition, NURSE WITH A NEWSPAPER is determined to have a high entropy with other (nurses) semantic identities (at endpoints). In further examples, the possession of the newspaper is determined and / or factorized to determine based on resonance with routes / goals and / or associated semantics and / or groups (e.g. interview Health Affairs readers etc.).

[0378] As explained, the law of the land at an endpoint may comprise particular rules and / or agreements published by an endpoint supervisor. As such, only the endpoint supervisor has the rights to publish / unpublish the laws of the land. Further, based on endpoint and / or supervisor hierarchy and / or detected credentials the laws of the land may be composed, augmented, resolved and / or validated hierarchically (for coherence / confusion); alternatively, or in addition, this may happen when confusion is detected and / or before publishing. As such, users, operators and / or supervisors may be notified and / or challenged in a (diffusive) hierarchical manner. Further, specific level laws, publishing and / or supervisors may be validated and / or approved with supervisor levels.

[0379] It is to be understood that an update / updating(or similar) a / the publishing refers to update / updating artifacts currently published(or posted) as a result of (the completion of) a previous activity of publishing. Thus, (the activity of) update / updating applies to currently published artifacts (and not to the previous activity of publishing the currently published artifacts (which would be non-sensical because that activity7would be completed,expired and / or in the past)). Also, in general, it is to be understood that an update / updating of / to a publishing may (also) encompass unpublishing (and / or removing) of published artifacts from the current publishing and / or unpublishing the current publishing; alternatively, or in addition, update / updating of / to a publishing may encompass publishing(or posting) of new artifacts and / or republishing(s)(or reposting(s)).

[0380] Publishing / unpublishing may encompass allowing / denying (diffusive) (controlled) access to the artifacts being published / unpublished. Further, when such artifacts and / or associated access control is / are updated / revised so is the publishing; alternatively, or in addition the publishing is updated at a (configured) semantic time. Alternatively, or in addition, publishing may comprise creating, copying, linking, allowing (diffusive) access and / or displaying (semantics and / or associated multimedia features associated with the) objects of the publishing; alternatively, or in addition, unpublishing may comprise invalidating, disabling, inactivating, deleting, unlinking, destroying, blocking (diffusive) access and / or not displaying (semantics and / or associated multimedia features associated with the) objects of the publishing.

[0381] When a (posted) publishing may generate confusion, the system may augment supervisors and / or not publish and / or update / unpublish (posted) artifacts which are being non-affirmatively factorized as per supervisors' goals in a potential hierarchical supervising manner.

[0382] In further examples, the system detects semantic shapes which move and / or are linked together and thus infers semantic grouping and / or identities. There may be instances where the semantic group (semantic) and / or semantic identity are / is associated with indicators and / or factors comprising higher confusion, low trust and / or risk (e.g. because they are unnatural, not learned, not believable etc.); further, the (semantic) leadership and / or factorization of one shape over the other may determine the semantic identity. In an example, the system detects a wheel and a mobile phone spinning around the wheel (e.g. in an un / controlled manner); while the factorization of the parts allow- potentially very believable inferences, the factorization of the composite reflects it’s hard believability as does not resemble any known route and / or is hardly / not diffused by semantic mles. Nevertheless, the system may infer a semantic route, group, shape and / or rule which have and / or are associated with decayed believability, elevated confusion and / or high-risk indicators and / or factors. Further, based on the factorization of particular circumstances and / or profiles the composite semantic inferences (e.g. of identities, routes, endpoints, SPINNING PHONE AROUND A WHEEL, SPINNING WHEEL WITH A PHONE etc.) may be factorized differently and have different believability factors. The believability factors may be associated with particularsemantic groups and / or leaders. In the example, the system may provide leadership of the (composite) semantic artifacts which are more believable (e.g. SPINNING WHEEL vs SPINNING PHONE etc.). It is to be understood that the system may use semantic shaping and / or overlaying of (known / saved) semantic network models in order to infer such believability factors and / or artifacts.

[0383] The inferences may be guided by privacy rules which may allow, deny and / or control inference and / or collapsing and thus inferring only the allowed level of granularity for semantic identities and / or semantic groups. In some examples, privacy rules may deny inferring, projecting and / or using semantic identities associated with a particular threshold or lesser number of objects and / or artifacts. It is understood that the level of inference granularity and / or localization may be based on hierarchical and / or projected inference.

[0384] The system may infer / assign leadership on particular locations, endpoints and / or semantic groups thereof to particular semantic identities and / or semantic groups thereof. Such leadership inference / assignment may be based for example semantic analysis including semantic time management. The (semantic) leadership may be inferred / assigned based on particular goals and / or factor intervals. In an example, two entities El and E2 (e.g. governments, companies etc.) share a common FISHING area / contract and are bounded by a goal / sub-goal / clause of DEVELOP FISHING, KEEP THE WATER CLEAN or DEVELOP FISHING BUT KEEP THE RISK OF CONTAMINAING THE WATER LOW. If the goals / sub-goals are not met while under a particular entity' leadership (e.g. El) then the system may change ratings of the entity El in rapport with the goals / sub-goals / clause and potentially update and / or index the time management rules asserting the leadership of the other entity (e.g. E2); thus, a new leadership (E2) is inferred and exerted (e.g. based on semantic profiles of E2) once the conditions are breached while potentially bounding the breaching entity (El) with (generated) goals / clauses (e.g. creating semantic artifacts including semantic routes, time management rules etc.) to (help / invest / pay / compensate / manipulate to) bring / recover the conditions to an agreed semantic artifacts baseline, anchor and / or goals. It is to be understood that such inferences, ratings and / or leaderships may be related with more complex environments with multiple entities, semantic fluxes and / or semantic groups contributing to collaborative contractual inferences such as explained throughout the application.

[0385] Semantic leadership is inferred and / or adjusted based on semantic analysis including semantic factorization.

[0386] The system uses semantic gating at endpoints in order to preserve confidentiality in relation with semantic inference associated with inferences related to objects and / or semantic identities passing through the endpoints.

[0387] While the examples show the modules stacked in a specific order it is to be understood that the order may be different in other applications. In some embodiments the antenna module may be positioned on top of the optical module; further, in other embodiments the optical module may not be present at all with the optical detection capabilities being performed by the antenna module. While this are specific examples, the generality and applicability of flexible module compositions extend to any configuration. In other examples as depicted in Fig. 13, the telescopic capabilities of the posts may allow the realization of enclosed areas within a composed post structure. For example, as illustrated, posts 61 are all “high raised” posts forming a perimeter about posts 62 which are relatively lower. The “high raised posts” are using telescopic capabilities to form an enclosed area on the lower posts. Such areas may be used for example to store or conceal tools, articles and any other artifacts. The enclosed posts area by the high raised posts may be based on a semantic group inferred based on a sensed pressure exercised by a load on the enclosed posts.

[0388] In further example the system elevates the post (e.g. via telescopic means) for hooking and / or latching to person or transportation wagons thus the composite carrier acting as a driveline for such wagons. Thus, the system may select specific wagons based on specific needs inferred via semantic inference and analysis. In further examples, users select specific wagons and the system assembles carrier beds based on the characteristics of the wagons and potentially the characteristics of the required route. It is to be understood that a wagon carrier driveline may be composed from a plurality of detached carriers and / or beds (e g. a driveline comprises four carrier beds, one for each comer of a wagon) which may be represented and / or inferred as semantic groups.

[0389] In general, the system performs assembly, couple and / or bond artifacts based on affirmative inferences. Further, in some examples, the system may not assemble / bond / couple artifacts which may result in non-affirmative and / or not allowed semantic identities (at endpoints / links).

[0390] In further examples, the system elevates posts for guiding, locking and / or connecting other artifacts or components into the enclosed areas; in an example the system encloses a higher capacity battery of a larger size wherein the system uses goal-based inference to determine the battery type and infer the enclosed area where to be placed. Further, in other examples the smart posts can join and / or clip for improved sensing and processing.Fig. 14 shows nine posts lOla-i in a configuration of 3x3 forming a combined sensing and / or processing capability.

[0391] In some examples, the composability of such elements and groupings is based on specific goals that may be specified by a user and / or inferred by the system. Further, when considering the goals and missions the system may use rewards and other factors-based inference.

[0392] For example, such goals may comprise of CARRY 7 BIG LUGGAGES or CARRY 7 6 BY 6 LUGGAGES and the system estimates the size of a flatbed and the number of required posts to fonn the flatbed based on mapping endpoints to areas to be covered by posts, luggage, and / or by using its own estimation of size, weight and / or indexing of the semantic BIG. In addition, the goal may comprise further restrictions such as USING A MAXIMUM 4’ CARRIER WIDTH; such restrictions may be based for example on estimating an optimal route of travel (e.g. based on a semantic route) where the system detects that particular areas and / or endpoints to be traveled comprise restrictions (e.g. a location comprising a door of 4’ width) and / or impeding likeable diffusion. Thus, in some examples, such restrictions may be based for example on inferred location-based semantics (e.g. using a camera or vision sensors for detecting the door width). The system composes various post configurations based on their sizes to determine the optimal join topology which may be based on mapping a semantic network (e.g. endpoint) model to areas to be covered by particular posts.

[0393] While the previous example may incorporate wheeled smart posts, alternatively, or in addition, it may incorporate drone type semantic posts comprising a copter module for lifting; it is to be understood that the smart post modules including the copter module may comprise motors / engines, propellers, servomotors, electronic speed controller, analog blocks, digital blocks and actuators.

[0394] In a wheeled-copter based application the system activates the wheeled module and / or copter module of the smart posts based on routing and semantic inference on the semantic model. The semantic network model may be mapped to land-based locations and / or aerial based locations.

[0395] The system may create a composite formation of posts / units (e.g. Fig 13 and 14) in order to improve sensing and / or capabilities. In an example, the system infers low count, low trust rating, unreliable and / or conflicting semantics by posts at a location. Further, the system may infer that the coverage of location and / or a mapped semantic network model in the field of sensing is not adequate. Thus, the system composes the smart posts to improve coverage and / or reliability of semantic inference. In further examples, the system combinessmart posts in a formation based on their capabilities; in addition, it may use a goal or missionbased inference to form the composite based formation.

[0396] The antenna elements module 7 (see also Fig. 3) may comprise panels of multi-array antenna elements 22; the panels may be disposed on the exterior of the trunk in a specific pattern (e.g. hexagonal). While in some embodiments the panels are fixed, in other embodiments the panels are automatically movable and composable and can be moved and organized in various patterns on the exterior of the trunk (e.g. two panels on two sides of the hexagon combine in a larger panel that can be oriented as well in various directions). The antenna elements and panels may incorporate RF and optical frontends, transmi t / receive modules, ADC, DAC, power amplifiers, DSPs, semantic units and other analog and / or digital blocks and components. Other post modules might incorporate similar elements in some embodiments.

[0397] The vision, or optical, module 8 may incorporate arrays of camera and / or vision sensors 23 disposed in a circular pattern about the perimeter of an optical module such as in the example illustrated in Fig. 2B, or may be arranged within an upper dome in an array pattern, or may incorporate dome cameras or others, such as illustrated in Fig. 2A (showing the outer dome, with the optical elements or cameras not visible within the dome). The cameras and / or vision sensors may be of time of flight type comprising laser and / or photonic elements for emitting and receiving (e.g. laser diodes, photodiodes, avalanche photodiodes- linear / analog mode, Geiger-mode, etc., edge-emitting lasers, vertical cavity surface emitting lasers, LED, fiber laser, phototransistors).

[0398] The control module 5 is used to process the information of the robotic unit and for communication via the sensing and wireless modules (e.g. antenna modules). The posts may communicate with each other (such as depicted in Fig. 10B, showing three separate smart posts labeled posts 1, 2, and 3) or with the distributed computing infrastructure (as illustrated in Fig. 10A, also showing three posts, numbered 1, 2, and 3) using any wireless protocols. Alternatively, or in addition, the posts may communicate through wiring and / or cabling embedded in the connecting bands and / or clips while the latching and clipping mechanisms comprise cabling connectors (e.g. specialized connectors, RJ45, Ethernet, serial interface etc ). It is understood that the control module functionality may be distributed amongst other modules, posts, computers and computer banks.

[0399] As mentioned, the clipping and fixation mechanisms allow the posts to reconfigure in various setups, topologies, zones and settings. The robotic distributed infrastructure allows such reconfigurations based on semantic inference including localization,hierarchical network models and zoning. While various clipping and attaching modules and mechanisms have been presented and depicted it is to be understood that such clipping and attaching mechanism may be standardized in some applications.

[0400] The following example presents the embodiment of a port of entry operation using a combination of smart posts and real time semantic technologies.

[0401] Semantic IOT composable cloud and real time semantic technologies provide adaptive real time and just in time operational intelligence and control while aggregating disparate sources of information.

[0402] They function based on semantic engines which interpret semantic models and semantic rules and thus are highly adaptable to the operational or simulated context. They are highly suitable for integrating multi-domain knowledge including capabilities, interdependencies, interactions, actions and what-ifs scenarios. Real-time semantic technologies understand the meaning of data from various sources and take appropriate actions; they provide real time situational awareness and automation. A semantic engine performs semantic knowledge discovery7by using a set of adaptive artifacts including a semantic model which may be defined by a user, ingested or learned by the system. The semantic model comprises the representation and mapping of informational flows and groupings to meanings (e.g. linguistic based terms related to objects, states, control actuation, groups, relationships, routes etc.); the semantic system guides the inference in the semantic model based on semantic rules and routes which specify how the system should behave. The capacity7of a semantic system inference capabilities increases as the semantic model evolves through modeling and learning. The semantic model is defined as linguistic based operational rules and routes. Further, the semantic model may be associated with hierarchical semantic network models for further management of paths, fluxes / flows, routes and semantic inference. In a semantic network model, the semantics are assigned to artifacts in an oriented graph and the system adjusts the semantic network model based on ingested data and semantic inference. The semantic network graph comprises endpoints and oriented links in a potential hierarchical structure with graph components representing another semantic network graph. As data is ingested from the smart posts functional modules, the semantic engine is able to perform inferences in real time, providing semantic intelligence, adjusting the semantic model and potentially executing actions. Semantics and / or semantic attributes are language or symbol terms and structures that have a meaning. The meaning in particular contexts and circumstances is established by semantic models including semantic groups and semantic routes; whenassociated with a semantic network model they may be associated with artifacts in a semantic graph representation of the system.

[0403] A semantic group represents a grouping of artifacts based on at least one semantic relationship.

[0404] Semantic routes comprise a collection of semantic artifacts (e.g. semantics, semantic groups, semantic routes, semantic network model artifacts etc.) and potential synchronization times; the semantic routes may be represented as a semantic and / or as a semantic group of semantic artifacts. They may be also associated with semantic rules (e.g. time management, access control, factoring, weighting, rating etc.).

[0405] Semantic routes may be represented, associated and / or identified with semantic artifacts (e.g. semantic and / or semantic group) and as such they benefit from general semantic modeling and analysis.

[0406] Semantic routes may be organized in a hierarchical manner with semantic routes comprising other semantic routes. Such hierarchical structure may be recursive.

[0407] The semantic routes may be grouped in semantic groups and participate in semantic inference.

[0408] Semantic routes associated with a semantic network model may be used for artifact (e.g. traveler, smart post) routing within modeled environments.

[0409] In this disclosure we will refer as semantic rules to all rules that allow semantic inference comprising composition and management plans including time management, access control, weighting, ratings, rewards and other factors (e.g. risk).

[0410] Semantic routes may be used as and / or to implement operational rules and guidelines. For example, the system is provided with allowable, desired, non-allowable and / or non-desired routes. In an example a route specifies that HOT CROWDED SPACES ARE NOT PLEASANT and also that CLOSE TO SHOPPING IS NICE and thus semantic post units and / or groups provisioned with such routes when inferring a HOT CROWDED SPACE semantic (e.g. via semantic composition) for an area would select the previous rules and determine a further route comprising COOLING and / or DIVIDE crowds to areas encompassing (or closest) to SHOPPING locations. It is to be understood that in this example areas may be mapped to endpoints in a network model representation of a physical space and the system would execute the commands in the routes based on the existing or deployable capabilities at mapped endpoints (e.g. areas). In an example, the DIVIDE semantic may be achieved via further semantic inference comprising smart post routing / guidance topologies,semantic shaping, semantic orientation and / or semantic augmentation. Further, the COOLING semantic may be achieved if the areas comprise cooling capabilities and / or semantics (e.g. via a fixed air conditioning fan module which may be potentially attached to a smart post unit). Some semantic inference techniques are explained in a family of patent applications such as US20140375431, the content of which is incorporated by reference. In further examples, if the system infers that an area and / or endpoint is associated with semantic artifacts (e.g. HEAT related, etc.) which have high (entanglement) entropy, drifts, shifts and / or factors as related with COOLING then the system may pursue the COOLING leadership and / or capabilities. It is to be understood that the inference at an endpoint may be based on semantic profiles of the (semantic) identities at the area / endpoint and thus, the high shift and / or entropy semantics may be based and / or related with at least one (semantic) identity and / or (composite) profile. If the area and / or endpoint semantics are inferred based on multiple identities (during at least on a projected hysteresis, diffusion and / or semantic time interval) then the system may pursue COOLING capabilities (e.g. until the entropy, drift and / or factors adjust to sensible (composite profiling) (hysteresis) levels, health risk of HEAT decreases etc.).

[0411] In further examples, the system determines goals and further optimized semantic shapes of groups of posts (or cars) to be realized within particular semantic budgets (e.g. based on energy consumption / quanta, fuel related quanta, entropy etc.). Such shapes and / or zones may be based on semantic groups and / or presence at particular areas and / or endpoints. In further examples such shapes may be associated with areas, endpoints, trajectories and / or sub-models. It is to be understood that the shaping may take in consideration the fitting of the posts within an area or endpoint based on semantic inference on dimensions, mappings, semantics and / or further semantic analysis; further, the shaping may be based on semantic orientation and drift analysis between the goal group shape and the current group shape. Further, the system may use dissatisfaction, concern and / or stress factors in order to assess the fitting of posts within various areas.

[0412] The system may strive (or have a goal / subgoal) to affirmatively factorize likeability and / or utility based on orientations at various hierarchical (endpoint and / or route) levels. In examples, despite an orientation at a lower / higher level being not (particularly) likeable the system may prefer it due to affirmative likeable factorization and / or utility at a higher / lower level (at / within a semantic time). The system may use such techniques to factorize the likeability of (semantic) endpoints, routes, goals, subgoals and / or other artifacts. Thus, in some examples, the system may (affirmatively) factorize (a) (likeability) (indicator) based onsemantic times associated with likeable and / or affirmative inferences / orientations (of (other) (indicator) factorizations / drift / entropies).

[0413] In some examples, semantic shaping is used to optimize traffic flows where the system determines the best shapes, zones and endpoints for groups of vehicles at particular times or particular areas.

[0414] In other examples, semantic shaping and semantic analysis may be used to optimize container and / or artifact storage in particular areas and / or volumes (e.g. mapped to semantic models).

[0415] Semantic inference uses semantic analysis comprising semantic composition, semantic fusion, semantic routing, semantic resonance, semantic indexing, semantic grouping, semantic time and / or other language based semantic techniques including semantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, homonymy.

[0416] In an example, a semantic group containing all the synonyms for "great ’ is stored and used in semantic inference. In some cases, the group comprises semantic factors assigned to semantic components to express the similarity' within a group or with the semantic attributes defining the group. In further examples, the system stores a semantic group for the same semantic (e.g. (“running”, “runnin”) etc.). In another example, the system stores separate identities and / or groups for “cat” and / or “c.a.t.” as they are associated with different semantics; further, during semantic inference the system infers leadership to “c.a.t.” over “cat” or vice- versa based on exact semantic identification (e.g. match the exact semantic form and / or identity) and / or semantic view. In the examples, the system may have inferred from ingested data that artifacts (e.g. “cat” and “c.a.t.”) have and / or are associated with different semantics (e.g. semantic identities) and thus the system is able to identify and / or create such semantic identities and / or semantic groups. Analogously, the system may infer that the ingested artifacts are associated with the same semantic (e.g. (“running”, “runnin' ” and thus the system may create a semantic identity and / or group to reflect the association and for further optimization.

[0417] It is to be understood that the leadership may be determined by coupling of semantic analysis and / or circumstances (e.g. location / localization, language, semantic profiles, roaming etc ).

[0418] The semantic analysis comprises semantic techniques such as synonymy, semantic reduction, semantic expansion, antonymy, polysemy and others. In an example, the user specifies lists of synonyms, antonyms and other lists that are semanticallyrelated. The elements in a list are by themselves related through semantic groups via semantic attributes or semantics (e.g. SYNONIM, ANTONIM).

[0419] Real time semantic technologies optimize processes and resources by considering the meaning of data at every level of semantic Al inference. Real time semantic technologies are well suited for providing situational awareness in ports of entries while further providing a framework for adaptive integration.

[0420] Semantic IOT infrastructure based on smart posts / robots and real time semantic technologies can provide precise counting, times and routing at the port of entries.

[0421] The ports of entry layout may be modeled through hierarchical semantic network models wherein the endpoints are associated with smart post sensing and locations in the layout; further, oriented links between endpoints represent the flows, transitions and the semantics of traffic at the modeled / instrumented points. The area, location and sensing based semantic network model is recursive and thus can be used to achieve the desired level of granularity in the mapped environments.

[0422] Semantics may be associated with sensing / data flows, checkpoint attributes, traveler attributes and further, the semantic model comprises semantic routes and how semantics compose. Flows / fluxes semantics and interdependencies may be modeled and learned via semantic modeling and inference.

[0423] The counting of people in monitored queues, areas or endpoints may be based on the traveler-based semantics inferred based on transitioning of links in the semantic layout / sensing model. Further, the system guides the semantic inference for traveler waiting times using semantic time and semantic intervals. The semantic time and semantic intervals allow time inference based on semantics. Further, a semantic time is indexed based on the context of operation. Thus, semantic time and semantic intervals ensure that the time inference takes places in the most accurate context of operation. By using semantic intervals and adaptive semantics for inference a semantic system achieves predictive semantics.

[0424] It is to be understood that, throughout the application, when mentioning (a) semantic time(s) this may be substituted for (a) semantic interval(s) and / or vice-versa.

[0425] In an example, a checkpoint for foreign nationals is timed based on the transitions in the semantic network model. In simplest terms, for example, at one checkpoint gate it may take a foreign national from country A (Fa) 1 min to be cleared by an officer and a foreign national from country B (Fb) 2 min. Thus, every time when the systems infers, potentially based on semantic interval contexts (e.g. arrival of a flight and arrival at the checkpoint), that there are foreign nationals from country B at the checkpoint, it may index thewaiting time accordingly. While the previous time indexing has been based on a single attribute (citizenship), other attributes or categories can be used for indexing the time (e.g. age of travelers, traveler status, visa ty pe, system speed, network speed etc.). This kind of operational inference and analytics is hence very’ accurate and performed in real time without the need of storing large amounts of data or continuously utilizing large compute resources. Further, patterns in time and space are learned by semantic IOT through semantic intervals.

[0426] Similarly, the system may project travel waiting times on various traveling (road) segments.

[0427] A semantic system also groups artifacts based on semantic inference and use those groups in further semantic inference. In our example the system may detect object types or complex semantics based on such semantic groups (e.g. group sensors, settings and detections and infer meanings, infer travelers by detecting flows of grouping of detections, features, clothing items and belongings; infer that a person is carrying a red bag etc ). It is to be understood that the Semantic IOT is a distributed composable cloud and as such it distributes, groups, compose and fusion various modalities detections in an optimized manner; as mentioned, the modalities may comprise a diverse spectrum of electromagnetic sensing.

[0428] In our example, the counting may be based on the transitions in the semantic network model; thus, when a link in the semantic network model is transitioned as detected by the smart posts and their modalities, the system infers a particular semantic (e.g. TRAVELER ENTER CHECKPOINT 1 or TRAVELER EXITS CHECKPOINT 1). Semantic composition and fusion of such semantics allow the coupling of detected semantics in and with time (e.g. counting the number of semantics / travelers at checkpoints, estimating waiting times or other general or personalized semantics) in the most flexible, efficient and optimized manner and utilizing a minimum amount of resources thus decreasing system costs. Other systems may not employ such flexibility, optimization, fusion and modeling techniques and hence they are not able to provide the same capabilities, coherence, accuracy and cost effectiveness.

[0429] The system will use adjustable inferable model semantics for mapping the type of service (e g. CITIZENS AND PERMANENT RESIDENTS mapped to transition links from the checkpoint inbound to checkpoint outbound), for counting (e.g. derive the number of people based on the transitions in the semantic network model), for speed of processing (traveler rate in an interval of time), to derive general or personalized sentiment inferences (e.g. VERY FAST, FAST, SLOW), for traveler semantic routing, experience rating, personalization and so forth.

[0430] Semantic automation and augmentation ensure actions in various domains; in an example, the coupling of the command and control model to semantic automation and augmentation may implement automatic or semi-automatic guiding, routing and access control in port of entry environments.

[0431] Based on the level of the autonomy employed through semantic automation and semantic augmentation the technology may be used to automate various tasks and provide semantic intelligence in various forms including display, sound, actuation, electric, electromagnetic, etc.

[0432] Solutions for port of entries (e.g. airports) includes developing semantic network models to be deployed on the distributed semantic cloud and mapped to a semantic sensing infrastructure. The semantic sensing infrastructure may include smart semantic posts / appliances comprising sensors, batteries and semantic sensing units which can be deployed throughout the port of entry.

[0433] The assumption in this example is that there are no available sensors at the monitored locations and as such the system uses semantic sensing for feeding the semantic network model. Semantic systems provide semantic fusion and as such, the system may integrate various data sources and / or additional sensing infrastructure for contextual accuracy and more precise inference. One example is when the smart posts comprise one or more of radiofrequency, camera / optical / infrared sensors. It is to be understood that camera / optical / infrared sensors can be selected from cost effective solutions such as low-cost ones designed for mobile devices. The radiofrequency devices / sensors may function in microwave frequencies range (e.g. 2.4Ghz to 80Ghz) or higher.

[0434] It is preferred that such sensors be easily deployable and reconfigurable in various environments and as such they may be one or more of the following: mobile post deployed sensors and fixed posts deployed sensors. While the smart semantic posts / appliances may be mobile in some environments, they can deploy as fixed on walls or other structures.

[0435] The smart posts may comprise Li-Ion batteries which may provide extended functioning time for the attached sensors and semantic units. The battery posts provide real time awareness of their charging status which allow easy maintenance whether manual or automatic for charging and / or batten- replacement. Alternatively, they may be plugged in at any time at a permanent or temporary supply and / or charging line. For easier maintenance of the battery powered devices, they may be deployed in a mutual charging and / or external charging topology comprising RF and / or robotic charging components.

[0436] For a composite post and / or (comprised) post groups, the system may route power between the component / member posts. As such, each post in such power (routing / feeding) configuration may comprise switching components to allow the power to flow between posts as per goals. In some examples, a composite post S2P routes power from S2P1, S2P3 via S2P10 and S2P11 to S2P2 based on a goal to have S2P2 available for an activity (e.g. (Jane’s) luggage handling) and / or charged / credited to (a budget of ) 82%. As such the system switches and routes the power within and / or between the posts to form the required power lines and / or routes.

[0437] In further examples, the electric / electromagnetic power is conditioned and / or routed within / between / through semantic units. In some embodiments, the multiplexers (MUX) in the semantic units comprise MEMS / analog switches which are commanded to switch the loads and / or couple the MUX inputs / outputs. Furthermore, they may comprise (high voltage) MOSFETs for voltage / current / power conditioning and / or conversion.

[0438] It is to be understood that in other embodiments the posts may be substituted with / for any other robotic devices and / or modules for the purpose of projecting, conditioning and / or routing power.

[0439] The microwave devices / sensors may comprise multiple sensing elements (e.g. 4 to 256) which allow the sensors to detect steer and optimize the beam, frequency, detection and communication patterns. More antennas may be present thus providing more scene interpretation capabilities and data that can be fused for knowledge discovery' (e.g. adapting and changing radiation patterns, adapting frequencies and polarizations).

[0440] In the simplest case, post sensors are disposed to capture transition patterns in at least one semantic network model which may be stored at each post comprising control module logic. Thus, with each transition in the model, the system detects and counts semantics of objects depending on the determined semantic of travel (e.g. PERSON IN CHEKPOINT GATE 2, PERSON OUT CHECKPOINT etc ). These deployments are straightforward in control areas and boarding sterile corridors where the flow is guided through lanes and corridors thus allowing for less shadowing and multipath effects. Thus, the counting in these areas can be very precise by instrumenting the lanes and / or corridors with smart posts or other sensing artifacts. For example, in a checkpoint lane the system uses one or two posts for lane ingestion and one or two posts for departure detection.

[0441] In such lanes and corridors, the location based semantic network models comprise fewer artifacts than in non-lane-controlled areas, thus minimizing the processing andoptimizing power consumption. Also, the relevant detection happens in near field for both optical and microwave and as such the data interpretation would be straightforward. Further, semantic system’s capability of changing and adapting the sensing patterns allows the reduction in the number of collection points and the number of sensors and thus maximum flexibility in deployments.

[0442] In non-lane-controlled areas and corridors the system may employ a more complex near to far field semantic model of locations which are mapped to semantic sensing detection techniques. The semantic engine fuses the information in the semantic network model.

[0443] In an example, the system uses radio frequency polarization diversity to improve detection in multipath environments. The smart semantic sensors may employ diversity antennas and / or use coupling of antenna elements to adjust electromagnetic radiation, polarizations, optimize frequencies and so forth.

[0444] Further, based on inferred topologies the system may reposition the smart posts in the environment and coordinate them to clip to each other in order to delimitate and realize the semantic zones and topologies required for traffic flow control.

[0445] In Figs. 8A and 8B, posts are disposed in a guiding lane configuration. In Fig. 8A, a first series of posts labeled a-f are on a left side of an entry point 40 and a second series of posts g-n are on a right side of the entry point. The entry point may be a location of passport control, boarding a craft, check-in, or any other point at which persons are processed or allowed to pass. Initially, the posts are arranged closely adjacent one another, and preferably with their associated ropes or belts attaching adjacent posts to one another but with the belts either retracted within the respective post or hanging in a slack fashion. In Fig. 8B, some of the posts have moved and been extended to increase the length of the traffic lane between the posts. Specifically, posts d, e, and f have moved, as has post n, as indicated by the arrows and the visibility of the belts that have been extended. In Fig. 8C, the posts have extended to the fullest extent, forming the longest line possible for the assembled collection of posts.

[0446] At the setup of Fig. 8A, one or more of the sensors (cameras, antennas, analog and / or digital blocks / devices etc.) of one or more of the posts scans the region between the posts, indicated as region 41. Upon the detection of persons standing in the region, the system determines that an extension is required. The particular logic may vary and be determined as above, but for example may require a plurality of posts a-f and / or g-n to detect static persons in the area, waiting but not moving quickly.

[0447] In Fig. 8B, one or more of the posts continues to scan the area, including region 42 occupying the terminal end of the lane 50 defined by the opposite pairs of posts. Most preferably, at least the end posts f and n provide input indicating the presence of persons standing in that region. In other versions, all of the posts, or at least a larger subset, also provide such an input which is used by the controller to determine whether to extend the posts yet again and thereby form a larger line. Finally, as shown in Fig. 8C, the posts have exhausted their reach. Most preferably, the controller is programmed with a map of the area surrounding the entry point, and also tracks the location of each of the posts, in order to direct the individual posts whether to move in a direction linearly away from a prior post (for example, with reference to Fig. 8C, in a direction from post I to post k), or to move at an angle with respect to at least a pair of prior posts (for example, in a direction from post k to post 1, or from m to n).

[0448] In Fig. 9 we show a perimeter delimitation configuration. The perimeter in the illustrated example is defined by posts a-d, though a different number of posts may be used. The posts combine to define a perimeter 51 having an internal area 52. In an example, the system infers and / or a user specifies an area and / or a semantic associated with it. The area may be delimited based on anchor points and / or the edges.

[0449] In Fig 10 we show various deployment options in which the posts communicate wirelessly and / or process information in a distributed cloud infrastructure. While in embodiment A they may use an external distributed cloud infrastructure, in embodiment B they use their own internal processing capabilities in a distributed cloud mesh topology; it is to be understood that the system may use any capabilities, whether internal and / or external to infer and configure composable cloud topologies. Also, their movement, positioning and coupling may be based on semantic network models whether at sensor, post, semantic group, infrastructure or any other level. It is to be understood that the grouping of smart posts in various topology, processing and cloud configurations may be based on semantic grouping based on semantic inference on inputs, outputs, sensing etc.

[0450] Any one or more of the posts may travel independently about a region, such as generally indicated with reference to posts 1, 2, and 3 shown in in Figs. 10A and 10B, without being tethered to one another. In such a configuration, the posts collect the optical, audio, or other information from sensors, cameras, antennas, analog and / or digital blocks and / or devices, front-ends etc., which may then be passed along directly to other posts as indicated in Fig. 10B, and / or to a central or distributed control infrastructure 100 as shown in Fig. 10A. The control infrastructure 100 may be a central computer communicatively coupledwith the plurality of distributed devices. It should be appreciated that any of the features described in this disclosure as being performed by “the system” may be performed by the control infrastructure in a centralized fashion, or may alternatively be performed in a distributed fashion by a distributed system including a plurality of control structures and / or computer components on the posts or robotic devices.

[0451] In other embodiments the posts may comprise master-slave configurations. In such configurations the master posts controls at least one slave post. The slave posts may comprise less functionality and / or be less capable than the master post (e.g. lacking full suite of sensors and / or actuators, smaller batteries, lacking displays etc.). The master post may control the movement and / or deployment of slave posts. In some examples the master post detects and control the positioning of slave posts. For example, an airport may use units of groupings of master and slave posts (e.g. groupings of at least one master and at least five slaves). Such units may be deployed and yield composable topologies and formations.

[0452] In further examples, the robotic posts formations and / or components thereof may be based on semantic groups which may comprise leadership semantic artifacts.

[0453] Master-slave configurations may be represented as semantic groups with the master units attaining leadership in particular configurations and / or environments.

[0454] The smart posts may comprise billboards, displays, actuators, speakers and other forms of semantic augmentation allowing them to convey information.

[0455] In a further example of utilization, the smart posts may be deployed in key areas and provide guidance via semantic augmentation. The semantic augmentation may comprise advertising. In some embodiments the smart posts and / or groups may be designed as for general use, however, when they receive a mission and a target they may adapt to the mission and target. In the airport example a unit of posts may receive the mission to provide guidance and / or lane formation to a particular airline. Thus, the posts may deploy to the targeted airline airport area and provide the semantic augmentation related to the airline; such information may comprise airline name, flight information, airline specific advertising and so on. The specific information may be received and / or downloaded from a specialized advertising sendee and / or cloud (e.g. airline cloud). The deployment of the post to the airline area may be based on the previous knowledge on the location of the airline, sensing and guidance.

[0456] In other examples the posts may deploy in areas that are inferred as of high risk and / or congested. Thus, once the distributed cloud infers such conditions it automatically initiates the deployment of units and / or topology' reconfiguration; theinitialization of operations may take place based on semantics inferred at any inference capable post. For example, in the high-risk areas the posts may be deployed for achieving a topology that reduces the overall risk (e.g. guiding the travelers through lower risk areas and / or routes, dividing the crowds based on boarding zones, traveler / visa status, risk etc.).

[0457] In some embodiments the posts are deployed in location and / or areas for which the system infers particular semantics. For example, for a location the system may infer a semantic of HAZARDOUS or SHOPPING TOO CROWDED and thus the system may dispose posts and / or units to contain those zones and / or guide travelers to other routes that do not contain such areas. Thus, posts deployed for such purpose may indicate via semantic augmentation (e.g. display and / or audio, wireless beaconing) the zone semantics and directions to follow by travelers in proximity; it is to be understood that proximal semantic augmentation may be triggered when travelers are detected in proximity'. The travelers may include people, vehicles and any other moving artifacts considered by the system.

[0458] While we refer to inference, it is to be understood that it may be based on inference at a single post / unit, a group of posts / units, distributed cloud and any combination of the former. The semantic system functions as a distributed architecture in various configurations comprising but not limited to semantic group computing, edge computing, cloud computing, master-master, master-slave etc.

[0459] In some embodiments, the system issues missions and / or commands to posts that are in particular locations, areas and / or endpoints and have inferred specific semantics. For example, the system issues commands to the posts that have been deployed to HAZARDOUS semantic areas and have associated semantics of MASTER POST, BATTERY HIGH and / or STAND POST UNIT DISPLAY TIME 1 HOUR. For example, such commands may be used to display flight information, routing information (e.g. for guiding out of hazardous area), advertisements and any other type of augmentative information. In the previous example the selection of posts may be associated with a semantic group defined by composite semantics determined by a semantic route (e.g. STAND POST UNIT DISPLAY TIME). It is to be understood that the system may select and / or command a semantic group of posts based on compositional semantics (e.g. STAND POST UNIT) and other sematic group hierarchies formed based on semantic composition.

[0460] It is to be understood that the previous exemplified semantics, semantic groups and / or semantic routes may be evaluated and / or inferred by the system on a linguistic relationship basis including semantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy, polysemy. Thus, in an example, a HAZARDOUS semanticinference may be based and / or reinforced (e.g. higher weights) using synonyms and / or related semantic groups (e.g. UNSAFE). In other examples, the HAZARDOUS semantic may be coupled and / or reinforced (e.g. lower weights) using antonyms and / or related semantic groups (e.g. SAFE). Alternatively, or in addition, H / ENT may be applied.

[0461] Hazard and / or safe indicators may be factorized and / or assigned to / for goals. In some examples, the system has a goal to keep S2P2 and / or its carried luggage / container in a likeable and / or intrinsic posture (at endpoints); as such, the system may project non-likeable conditions, activities, interactions and / or hazards which can non- affirmatively affect the likeability of the posture and / or (further) goal orientation (at endpoints). Alternatively, or in addition, the system may project hazard / safe indicators associated with goal (projections). Alternatively, or in addition, the system has / discovers / infers / stores and / or is configured with a / an (intrinsic) goal to monitor / observe / keep a part, module, device, item, asset, agent and / or group (e g. S2P2; (John’s / Does) (tulip) container / carrier / vehicles; green recycling container(s), S2P2 arm(s), worn-out wheels, suction pods, clean camera(s) (modules) etc.; tulip carrier(or vehicle) headlights, fenders, wheels, doors, bumpers etc.) (under likeable / affirmative inferences and / or against non-likeable / non-affirmative inferences).

[0462] As the system detects that the inferred semantics in rapport with the part, module, item, asset, agent, post, drone, vehicle / carrier and / or group are (likeable / non-likeable) entropic with their intrinsic / configured purpose, posture and / or orientation it may augment a / an owner / user / supervisor (at endpoints / links).

[0463] Alternatively, or in addition, if the system infers that an encompassed part / module / component / device / item is (readiness) non-affirmative with its (intrinsic) purpose / posture and / or unable to perform as designed and / or its readiness is non-affirmative due to inferred non-likeable / high entropy then, the system may infer (based on posture and / or the unavailability and / or (readiness) impairment of the part / module / component / device / item (capabilities)) that the encompassing part / module / component / device / item (comprising the part / module / component) may be (readiness) non-affirmative with its (intrinsic) purpose and / or unable to perform as designed and / or its readiness is non-affirmative. Alternatively, or in addition, (by H / ENT) if the system infers that encompassed parts / modules / components / devices / items are (readiness) affirmative with their (intrinsic) purpose / posture and / or are able to perform as designed and / or their readiness is affirmative due to inferred likeable / low entropy then, the system may infer that the encompassing part / module / component / device / item (comprising the part / module / component) may be(readiness) affirmative with its (intrinsic) purpose and / or able to perform as designed and / or its readiness is affirmative.

[0464] In some examples, the system infers that the S2P2 anu gripper is twisted and / or missing(and / or having a disabled and / or not-ready) gripper (capability) and / or an essential part / capability which impairs S2P2’s (due to (being) non-affirmative (high entropy) with the (intrinsic) arm posture and / or purpose) (readiness) of carrying and / or manipulating items (at endpoint EPl). It is to be observed that a non-affirmative (posture) inference (readiness) (at a semantic time) may determine a high entropy (factorization) on purpose (at a / the semantic time).

[0465] In other examples, the system infers that the tulip carrier / vehicle bumper is damaged; further, the system may infer / project that the bumper damage may affirmatively / non-affirmatively affect the drive-ability (readiness) and / or safety (readiness) of the Tulip container / carrier / vehicle (based on and / or at (driving) (semantic) (route) endpoints / links); further, the system may explain and / or augment about the Tulip container / carrier / vehicle readiness (to a supervisor) (e g. based on factorizations, projections and / or semantic routes / rules (at endpoints / links)). In additional examples, the system detects that the Tulip carrier / vehicle’s bumper is unsightly which is non-affirmative resonant with John’s / Jane’s / Does goals of driving “safe a good looking Tulip carrier / vehicle”; alternatively, or in addition, the system detects that the bumper is dangling and / or is in a non-affirmative (high entropic) posture (from intrinsic ((securely / reasonably) fastened / horizontal)) and / or further, affects the safety and / or / of drive-ability (at (semantic) (route) endpoints / links). Thus, because the system detects a transition from affirmative (readiness) factorization to non- affirmative (readiness) factorization it may augment a supervisor (e.g. Jane, John and / or Does); it is to be understood that the augmentation may be based on a configuration and / or (flux) publishing by the / a / an (other) supervisor (at endpoints / links).

[0466] In further examples, the system distributes and / or orient items in order to transition from affirmative to non-affirmative and / or vice-versa in a likeable manner and by having an (optimal) likeable factorization at higher endpoints. In examples, the system receives 100 pair of boots and routes them to the outlets which optimally factorize the likeability' and / or satisfy the interests (wherein the (satisfaction of) interests are inferred, indicated and / or based on a plurality of factors / indicators).

[0467] Alternatively, or in addition, the system may infer / project(including factorizing) (access controlled) capabilities, measures / counter-measures, agents and / or serv icing endpoints which (when accessed based on access control) cause / steer / orient the non-affirmative (readiness) inferences towards affirmative (readiness) inferences and / or goals / interests. Alternatively, or in addition, the system may express (based on further challenges, inputs and / or augmentation from / to a supervisor) interest (at semantic times) in such capabilities, measures / counter-measures, agents and / or servicing endpoints and / or further orient / direct / drive / manipulates the item towards the affirmative capabilities, routes and / or inferences.

[0468] It is to be understood that affirmative inferences may comprise and / or be based (on steering / orienting towards) affirmative budgets and / or inferences (e.g. for supervisors, insurers, brokers, providers, groups etc.). In some examples, the budgets are projected based on undershoot / overshoot, worst / best case and / or offensive / defensive inferences. In further examples, the affirmative budgets are inferred based on flux data (and / or interest / capabilities) from insurance, brokerage and / or capability / interest providers. In examples, the system projects that the GREEN SHOP (repair provider) around the comer provides a likeable budgeted capability (for (Does (fluxes) (as supervisors) and / or a / an (grouped) (likeable / affirmative) insurance (flux) provider GREEN INSURANCE) and thus, the system may challenge / augment the insurance provider and / or Does to provide inputs, explanations and / or (further) confinn / infirm the selection; alternatively, or in addition, the system projects that the GREEN SHOP accepts 20KWh CHARGING (STATION) CREDITS / COUPONS based on (published) in (interest) budgets associated with its charging capabilities. It is to be observed, that such projections may entail groupings and / or compositions of the agents, users, supervisors and / or (associated) fluxes(e.g. Does and the GREEN SHOP / INSURANCE repair / insurance identity / provider / flux; (Does, GREEN SHOP (repair), GREEN INSURANCE) (flux) (group) etc.).

[0469] Once the system infers / pr ejects a transition from non-affirmative (readiness) factorization to affirmative (readiness) factorization based on the applied countermeasures it may augment a supervisor (e.g. Jane. John, Does etc.). Alternatively, or in addition, it may augment a supervisor that a (configured) (leadership) (applied) counter-measure doesn’t determine (projected) affirmative inferences (as projected) and / or further projects and / or challenges about other counter-measures to be applied. Alternatively, in addition, it may augment a supervisor that a (configured) (applied) counter-measure detemiines (projected) affirmative inferences (as projected).

[0470] In further examples, a supervisor and / or (affirmative) (group) leader may infer / configure / assign access control (e.g. block / allow / diffuse) (rules / routes) to (counter) measures of / to the supervised (group) objects and / or capabilities.

[0471] The system may augment supervisors on (projected) changes between readiness to non-readiness and / or vice-versa. Alternatively, or in addition, the system may augment supervisors on (projected) changes between affirmative to non-affirmative (projection) inferences and / or vice-versa. Alternatively, or in addition, the system may augment supervisors on (leadership) counter-measures(countering non-affirmative inferences / projections) and / or affirmative measures. The augmentation may entail any and / or any combination thereof among publishing / challenging / communicating on a flux, saving it in a memory, retrieving / sending / posting on a channel / account / chat / application associated with the user / supervisor. text / multimedia messaging. mobile device application rendering / notification and / or any other augmentation techniques such as explained in the application.

[0472] The augmentation may be configured / allowed / blocked / diffused based on a configuration and / or (hierarchical) (flux) publishing by the / a / an (other) supervisor(s) (at endpoints / links).

[0473] Real time semantic technologies and semantic analysis allow for adaptive intelligent systems that can be used for multi domain intelligence, automation and autonomy.

[0474] Those technologies are based on semantic analysis techniques of which some are explained in patent Pub No 20140375430.

[0475] Semantic analysis comprises semantic composition, semantic fusion, semantic routing, semantic orientation, semantic gating, semantic inference and / or other language based semantic techniques including semantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy.

[0476] In this disclosure we will refer as semantic rules to all rules that allow semantic inference comprising composition and management plans including time management, access control, weighting, ratings, rewards and other factors. Semantic artifacts include semantics, semantic groups, rules, semantic routes, semantic views, semantic view frames, semantic models and any other artifact used in semantic analysis.

[0477] Semantic technologies allow the interpretation of inputs and data streams into operational semantic knowledge which may comprise intelligent related outputs, user interfaces, control and automation. The inputs, data streams and operational semantic knowledge (discovery) may be related to sensing, signals, images, frames, multimedia, text, documents, files, databases, email, messages, postings, web sites, media sites, social sites, news sites, live feeds, emergency services, web sen ices, mobile services, renderings, user interfaceartifacts and other electronic data storage and / or providers. Further, ingested artifacts and / or semantic groups thereof may be linked and / or associated with semantic model artifacts. In some examples, paragraphs / sections / headers from email, markup formatted data / objects / files, chat or posting messages and / or web pages may be represented. Further, semantic identification of such paragraphs (e.g. attributing a news article to its author, newspaper, group etc.) may allow semantic profiling and factorization at any level of semantic identification. Thus, the semantic artifacts associated with the semantic identification and semantic profiles may be further factorized based on the semantic analysis of encountered tags, markups and / or their values (e.g. certain artifacts are associated and / or factorized based on an underlined and / or particular font, header etc. as detected based on tags and / or markups); further, such inferred factorized semantic artifacts may be used to modify and / or mask the associated tags and / or markup values in documents. In some examples, the summary content in some documents is masked, not showed and / or not rendered in preview mode in particular circumstances (e.g., when user not present or not looking at semantic device).

[0478] In further examples, content and / or documents may comprise (operating) manuals, ordinances, invoices, bills (e.g. of sales, government issued, of lading etc.), multimedia content and / or other content which may be semantically analyzed and / or semantically identified (e.g. “(Does) (house) ordinance to recycle items during freezing”, “recycling infrastructure bill’’, “(Does) sump pump type A troubleshooting section” etc). It is to be observed that such semantic identities and / or (identified portions) of content thereof may be considered (hierarchically) resonant affirmative / non-affinnative and / or likeable / not- likeable.

[0479] An integral part of the semantic knowledge discovery is a semantic model which represents a set of rules, patterns and templates used by a semantic system for semantic inference.

[0480] The capacity of a semantic system’s inference capabilities may increase as the semantic model evolves through semantic inference, modeling and learning.

[0481] A semantic field represents the potential of semantic knowledge discovery' for a semantic system through information processing and inference.

[0482] A system achieves a particular semantic coverage which represents the actual system capabilities for semantic knowledge generation. Hence, the semantic coverage can be expanded by adding new streams or inference artifacts to the operational semantic capabilities of the system.

[0483] In some examples the semantic coverage is related to the semantic network model coverage capabilities (e.g. the area covered, the resolution covered at the lowest or highest endpoint hierarchy, the number of hierarchical levels etc.). Further, the semantic coverage may be related to sensing and inference modalities available for given semantic network model artifacts (e.g. a semantic coverage is extended if a system comprises two sensing modalities as comparable to only one modality of similar capabilities).

[0484] The semantics may be assigned to artifacts in the semantic network model (graph) including endpoints and links. Dependencies between semantics and / or artifacts may be captured and / or determined by oriented links between the endpoints, hierarchy and / or path composition. As such, a group dependent semantic group may be represented as an oriented graph / subgraph with the causality relationships specified as oriented links (e.g. from cause / causator to effect / affected and / or vice-versa). Additionally, the elements in the model may be hierarchical and associated with any semantic artifacts.

[0485] The system may comprise symptoms - cause - effect semantic artifacts (e.g. semantic routes). In an example the system determines symptoms such as P0016 ENGINE TIMING WHEN COLD and 80% DIRTY OIL and as such infers a potential cause of 80% TIMING SOLENOID ISSUE and further projected semantic time and / or risk (e.g. IMMEDIATE. WHEN VERY COLD etc.) of ENGINE BREAKDOWN.

[0486] Semantic collaboration means that disparate systems can work together in achieving larger operational capabilities while enhancing the semantic coverage of one’s system semantic field.

[0487] A semantic flux is defined as a channel of semantic knowledge exchange, propagation and / or diffusion between at least a source and at least a destination. By using semantic information from semantic fluxes, a receiving system improves semantic coverage and inference.

[0488] A semantic flux connection architecture may be point to point, point to multipoint, or any combination of the former between a source and destination. Semantic fluxes may be modeled as a semantic network model whether hierarchical or not.

[0489] Semantic fluxes can be dynamic in the sense that they may interconnect based on semantic inference, semantic groups and other factors. In an example, a semantic flux A is connected with a (composite) semantic flux B at first and later it switches to a point to point configuration with (composite) semantic flux C.

[0490] A composite semantic flux comprises one or more semantic groups of semantic fluxes, potentially in a hierarchical and / or compositional manner; further all theinformation from the composite flux is distributed based on the composite flux interconnection, semantic routing and analysis.

[0491] Dynamic flux configurations may be based on semantic groups and hierarchies. For example, flux A and B are semantically grouped at first and flux A and C are semantically grouped later. In further examples semantic groups interconnect with other semantic groups and / or fluxes, potentially in hierarchical and compositional manner.

[0492] Semantic fluxes may transfer information between semantic engines and / or semantic units comprising or embedded in access points, gateways, firewalls, private cloud, public cloud, sensors, control units, hardware components, wearable components and any combination of those. The semantic engine may run on any of those components in a centralized manner, distributed manner or any combination of those. The semantic engine may be modeled in specific ways for each semantic unit with specific semantic artifacts (e.g. semantics, semantic groups etc.) being enabled, disabled, marked, factorized, rewarded and / or rated in a specific way.

[0493] Semantic fluxes may use any interconnect technologies comprising protocols, on-chip / board and off-chip / board interconnects (e.g. SPI, I2C, I / O circuits, buses, analog and / or digital blocks and components, diodes, varactors, transistors etc ), CAN, wireless / wired interfaces, optical interfaces and fibers and so on. Additionally, or alternatively, semantic fluxes connect via semantic sensing units comprising semantic controlled components, including those previously enumerated and others enumerated within this application. Protocols may include any network and / or communication protocols such as TCP, UDP, IP, 3GPP and / or any other protocols (as specified by the International Organization for Standardization (ISO), the International Telecommunication Union (ITU), the Institute of Electrical and Electronics Engineers (IEEE), and the Internet Engineering Task Force (IETF) and / or The World Wide Web Consortium (W3C)). Alternatively, or in addition, as known in art, such protocols may be connection-based (e.g. TCP (over IP) etc.) or connectionless (e.g. UDP (over IP) etc.) and / or may be based on same lower level protocols(e.g. IP etc.).

[0494] Semantic fluxes and / or streams may also connect other objects or artifacts (based on connecting (running) (hosted) software applications) including / on semantic display units, display controls, user interface controls (e.g. forms, labels, windows, text controls, image fields), media players, mobile devices, cloud tenants, (virtual) computers and so on; semantic fluxes may be associated and / or linked to / with (user interface) display controls in some examples; in further examples, such objects and / or artifacts may be (mapped to) D / M and / or UC1 / UC11 / UC2 / UC12 depicted in Fig. 54AC. It is to be understood that the semanticfluxes may connect (software) applications running on a / the particular hardware via network protocols and / or (comprised) interfaces. Such objects may benefit from the semantic infrastructure by publishing, gating, connecting, routing, distributing and analyzing information in a semantic manner. Such objects may use I / O sensing, authentication and rendering units, processes, components and artifacts for further semantic analysis, gating, routing and security. In an example, the semantic gating routes the information based on authentication and semantic profiles. In further examples, display control or user interface components and / or groups thereof are displayed / rendered / labeled, enabled, access controlled or gated based on semantic analysis, semantic profiles, semantic flux and gating publishing. As such, the system identifies the context of operation (e.g. comprising the user, factors, indicators, profiles and so on) and displays coherent artifacts based on coherent inference.

[0495] Semantic fluxes may connect the system with collaborators.

[0496] The system may interconnect / route (data from / to) semantic fluxes for semantic data routing and / or diffusion. In some examples, the system connects (composite) semantic flux of collaborator (CARRIER EXPERT) A to a (user interface) memory / control having associated a semantic (attribute) CARRIER (EXPERT) OPINION based on semantic analysis, profile / preferences and / or access control. Alternatively, or in addition, the system connects (composite) semantic flux of collaborator (CARRIER EXPERT) A to a (user interface) memory / control having associated a semantic (attribute) of MULTIMEDIA (CARRIER) CONTENT WITH A FANCY TWIST based on semantic analysis, profile / preferences and / or access control.

[0497] It is to observed that in some examples the system may determine based on semantic analysis, profile / preferences and / or access control and / or factonze (based on leadership) to which among the CARRIER (EXPERT) OPINION or MULTIMEDIA (CARRIER) CONTENT WITH A FANCY TWIST fluxes to connect (CARRIER EXPERT) A flux.

[0498] Alternatively, or in addition, the system may connect (CARRIER EXPERT) A flux to both CARRIER (EXPERT) OPINION and MULTIMEDIA (CARRIER) CONTENT WITH A FANCY TWIST fluxes based on semantic analysis, profile / preferences and / or access control.

[0499] Various types of controls and / or dashboards can be displayed based on semantic routes and / or semantic profiles (e.g. groups specific, semantic identity specific, user specific etc.).

[0500] Further, controls and / or user interface objects may be displayed in a hierarchical manner wherein the control and / or user interface data is displayed based on access control at and / or between various levels in the hierarchy.

[0501] The controls, user interface objects and / or their content may be upscaled and / or downscaled before / during (display / video) (memory) rendering and / or displaying. In some examples, the upscaling may be achieved by applying various transformations and / or algorithms to upsample lower-resolution image / video data to a higher resolution image / video data. The transformations and / or algorithms may be associated with interpolation, resampling, vectorization, deep (convolutional) networks and others.

[0502] Alternatively, or in addition, the system may indicate to the target device (e.g. via the robotic device reporting its capabilities to the target device via the (HDMI / USB / DisplayPort / Thunderbolt etc.) connection protocol) that it supports a / the high(er) / highest resolution and / or multiple monitors / displays and thus, the robotic device may arrange the display windows and / or user interface controls for high (maximum) size and / or high(est) resolution.

[0503] In some examples, the system indicates / reports to the target device (e.g. via a device descriptor etc.) that it supports 8k (or 7,680px x 4,320px) resolution and one or more (emulated) monitors / displays.

[0504] The system may connect to and / or launch (streaming) provider (application / service) A and (streaming) provider (application / service) B and manipulate the serv ices and / or applications user interfaces to select (multimedia content) widgets / controls and / to play (in (provider / services / application) media players) content from both A and B (applications / services) while (emulating) rendering / playing the content from A on a first (emulated / virtual) monitor / display / control (e.g. such as UC1 / UC11 in Fig. 54AC) and / or the content from B on a second (emulated / virtual) monitor / display / control(e.g. such as UC2 / UC12 in Fig. 54AC). Alternatively, or in addition, the system may store (captured) video / audio / sensing data captured / rendered from A to a first (emulated / virtual / physical) (video / display / multimedia) (emulated / virtual / physical) memory (address) (e.g. such as UC1 / UC11 in Fig. 54AC) and the video / audio / sensing data captured / rendered from B to a (emulated / virtual / physical) (video / display / multimedia) (emulated / virtual / physical) memory (address) (e.g. such as UC2 / UC12 in Fig. 54AC).

[0505] It is to be observed that the (captured) video / audio / sensing data captured / rendered from and / or associated with A may be stored and / or rendered in a memory(e.g. such as D / M). Furthermore, such data may be associated, linked, stored and / orrendered in (association with) UC1 / UC11 (memory) (UI) (objects). Similarly, (captured) video / audio / sensing data captured / rendered from and / or associated with B may be stored and / or rendered in a memory(e.g. such as D / M). Furthermore, such data may be associated, linked, stored and / or rendered in (association with) UC2 / UC21 (memory) (UI) (objects).

[0506] Alternatively, or in addition, the system may redirect the video / audio / sensing data captured / rendered on a first emulated / virtual monitor / display to a first coupled (group of) TV / PDS / UINT / augmentation devices and the video / audio / sensing data captured / rendered on a second emulated / virtual monitor / display to a second coupled (group of) TV / PDS / UINT / augmentation devices. Alternatively, or in addition, the system may store the video / audio / sensing data captured / rendered on a first emulated / virtual monitor / display to a (first) video / display memory (address) and the video / audio / sensing data captured / rendered on a emulated / virtual monitor / display to a (second) video / display memory (address).

[0507] In some examples, such as depicted in Fig. 54AC, the system renders / stores / sends (video / audio / sensing) (captured) (signal) data on at least one D / M (virtual / emulated / physical) (display and / or memory) and / or (display and / or memory) (stored / comprised / rendered) user interface control (e.g. such as UC1 / UC11 / UC2 / UC12).

[0508] It is to be understood that the system may connect to and / or launch (provider) software applications and / or services based on (discovered and / or inferred) capabilities semantics of such software applications and / or services. Furthermore, the system may (semantically) group software applications and / or services and / or semantically compose and / or publish their capabilities semantics and / or semantic identities.

[0509] Also, it is to be understood that, in order to launch the applications and / or access services and / or capabilities, the system may operate and / or manipulate a (target) controlled device (user interface) to install, download and / or operate such applications on / in the controlled device (memory) (by the target device processor). Alternatively, or in addition, the system may operate and / or manipulate a controlled device (user interface) to install, download and / or operate an application enabling access to capabilities and / or services.

[0510] In further examples, the system flows the information between semantic fluxes and gates based on semantic routing and semantic profiles.

[0511] In some examples, the system monitors the change of data (e.g. via analyzing a rendering, bitmap, user interface control / artifact, window, memory buffer analysis, programming interface, semantic inference etc.) in the user interface and / or D / M and perform semantic analysis based on the new data and the mapping of the changed data.

[0512] In further examples, the system infers and identifies display semantics artifacts (e.g. of an airport app window, messaging app, geographic information system window, input / output control etc.), activations, locations and a further semantics based on I / O data (e.g. touch / mouse click) on the window and the system maps and creates semantic artifacts (e.g. models, trails, routes etc.) from such inference. It is to be understood that the mapping may be hierarchical, relative to the activated artifacts in a composable manner. Alternatively, or in addition the mapping may be absolute to the display surface whether composed or not (e.g. comprising multiple display artifacts and / or sub-models).

[0513] For semantic systems the “time” may be represented sometimes as a semantic time or interval where the time boundaries, limits and / or thresholds include semantic artifacts; additionally, the time boundaries may include a time quanta and / or value; sometime the value specifies the units of time quanta and the time quanta or measure is derived from other semantic; the value and / or time quanta may be potentially determined through semantic indexing factors. A semantic time interval may be inferred and / or defined based on (the inference of) at least one first semantic; the semantic time interval is expired(or invalidated) based on the invalidation(or expiration) of the at least one first semantic (based on further inferences).

[0514] The semantic indexing factors may be time (including semantic time), space (including location semantics) and / or drift (including semantic distance / drift) wherein such indexing factors may be derived from one another (e.g. a semantic of VERY CLOSE BY might infer a semantic of SUDDEN or SHORT TIME with potentially corresponding factors). As such, a semantic system is able to model the space-time-semantic continuum through semantic inference and semantic analysis.

[0515] In further examples, the semantic indexing may be used to index risk factors, cost factors, budgets and so on; alternatively, or in addition, they may be used to index (associated) thresholds and / or intervals.

[0516] Semantic indexing represents changes in the semantic continuum based on semantics and / or semantic factors with some examples being presented throughout the application.

[0517] In an example, the system determines a first semantic at a first endpoint / link and a second semantic for an endpoint / link; further, the system determines a location for a new endpoint on an oriented link and / or endpoint determined by the first and / or second endpoint / link based on an indexing factor associated with a composite semantic which is a combination of the first semantic and the second semantic. In another example, thecomposite semantic is a combination between a semantic associated with a source model artifact (e.g. endpoint or link) and a destination model artifact and the indexing factor associates a new model artifact on the path / link between the source model artifact and the destination model artifact. The indexing factor may be associated with a semantic factor calculated / composed / associated with a semantic artifact; an indexing factor may be used to index semantic factors. Once the system infers an indexing factor for a semantic it may update the semantic model and add endpoints on all semantic endpoints and / or links associated with the semantic via semantic relations or semantic groups. Further the system may redistribute the existing or newly inferred semantics on the new determined endpoints and establish new oriented links and rules.

[0518] In an example the system determines an object / feature boundary based on indexing wherein the system indexes and / or merges / splits the on and / or off boundary7artifacts until it achieves a goal of inferring high-quality object semantics.

[0519] The system may map hierarchical semantic models to artifacts in the semantic field and infer semantics at various hierarchical levels, wherein higher hierarchical levels provide a higher semantic level of understanding of feature and identification semantics (e.g. nails, legs, hands, human, man, woman, John Doe, classmates etc.).

[0520] During inference the system maps semantic network models to objects artifacts and so on and performs further inference in the semantic field. In some examples the mapping is based on boundary conditions and detection.

[0521] In other examples the indexing is used in what-if and projected analysis, mapping and / or rendering the semantic model based on goals and forward / backward hierarchical semantic inference. In such examples the system may invalidate and / or delete related artifacts post indexation (e.g. first and / or second endpoints / links).

[0522] The indexing factors may be related with indexing values related with actuation and or commands (e.g. electric voltages, currents, chemical and biological sensors / transducers etc.).

[0523] The indexing factors may have positive or negative values.

[0524] Semantic factors and indexing factors may be used to activate and control analog or digital interfaces and entities based on proportional command and signal values. The system may use indexed and / or factorized analog and digital signals to control such electronic blocks, interfaces, other entities, electric voltages, currents, chemical and biological sensors and transducers etc.

[0525] The system may use variable coherent inferences based on at least one (variable) coherence / incoherence indicators and / or factors. In some examples, the semantic analysis of circumstances associated with the coherence / incoherence factors deem the variable coherent inference as coherent and / or incoherent based on the (semantic) factorization of the coherence / incoherence indicators and / or factors.

[0526] The semantic composition infers, determines and guides the context of operation. Semantic analysis may determine semantic superposition in which a semantic view frame and / or view comprises multiple meanings (potentially contradictory, high spread, high entanglement entropy, incoherent, non-composable -due to lack of composability, budgets and / or block / not allowable rules, routes and / or levels) of the context. The inference in semantic views may yield incoherent inferences which determine incoherent superposition artifacts (e.g. semantic factors, groups, routes etc.). Alternatively, or in addition, the inference in semantic views yield coherent inferences which determine coherent superposition artifacts (e.g. semantic factors, groups, routes etc.). The semantic expiration may control the level of superposition (e.g. the factor of conflictual meanings or a sentiment thereof). The superposition is developed through semantic analysis including semantic fusion in which a combined artifact represents the composition and / or superposition of two or more semantic artifacts. Thus, semantic expiration may be inferred based on semantic fusion and superposition. In an example, the system performs fusion (e.g. potentially via multiple routes) and infers that some previous inferred semantics are not needed and therefore leams a newly inferred semantic time management rule which expires, invalidates and / or delete them and the semantic model is updated to reflect the learned rules and artifacts. Analogously, the system may use projections to associate and / or group ingested and / or inferred signals and / or artifacts with projected semantic artifacts; it is to be understood that such learned semantic groups, rules and further (associated) semantic artifacts may expire once the system perform further analysis (e.g. collapses them, deems them as nonsensical, decays them etc.). The system may factorize potentiality and / or potential scopes, activities, loses, gains, hazards and / or other semantics and / or indicators based on projections.

[0527] Inferred semantics may be used, diffused and / or composed hierarchically between semantic views (e.g. via flux). Alternatively, or in addition, the system diffuses and / or composes semantics at a group level. In examples, the system composes inferences of John’s and Jane’s semantic views and uses and / or diffuses them within / to Does semantic views and / or vice-versa. As such, the inferences within semantic views may be hierarchically applied based on semantic groups.

[0528] The system leams artifacts via multiple semantic routes. Further, the semantic routes are factorized by the multiplicity of associated semantic artifacts. In an example the system factorizes a semantic route based on an association with an inferred semantic; further, the inferred semantic is factorized based on the associated semantic routes.

[0529] Coherent semantic groups may be inferred based on coherent and / or safe inferences (with less need of evaluating blocking routes and / or rules on leadership and / or group semantics) comprising the members of the group.

[0530] The coherency and / or entanglement of semantic groups may increase with the increased semantic gate publishing, factorizations, budgets and / or challenges within the group. Further, increases in coherency and / or entanglement may be based on high factorized collaborative inferences including inference and / or learning of sensitive artifacts (e.g. based on a sensitivity and / or privacy factor, risk of publishing (to other groups), bad publicity, gating, weights and / or access control rules).

[0531] Factors and / or indicators (e.g. likeability, preference, trust, risk etc.) may influence the coherency and / or entanglement of semantic groups.

[0532] The increased affirmative coherency and / or resonance of (affirmative) semantic groups may increase likeability / preference / satisfaction / trust factors and / or further affirmative factors. Analogously, the decreased affirmative coherency and / or resonance of semantic groups may decrease likeability / preference / satisfaction / trust factors and / or further affirmative factors.

[0533] The system may prefer non-affirmative coherency and / or resonance of (non-affirmative) semantic groups in order to increase the semantic spread.

[0534] The affirmative factors may comprise affirmative-positive and / or affirmative-negative factors.

[0535] Affirmative-positive factors are associated with confidence, optimistic, enthusiastic indicators and / or behaviors. Analogously, affirmative-negative factors are associated with non-confidence. pessimistic, doubtful, unenthusiastic indicators and / or behaviors.

[0536] Affirmative-positive and / or affirmative-negative may be used to model positive and / or negative sentiments. Further, they may be used to asses, index and / or project (realizations) of goals, budget, risks and / or further indicators.

[0537] Coherent and / or resonant semantic groups exhibit lower entanglement entropy on leadership and / or group semantics while incoherent semantic groups may exhibit higher entanglement entropy. Semantic indexing may be used to implement hysteresis and / ordiffusion. Semantic indexing may be inferred based on diffusion (e.g. atomic, electronic, chemical, molecular, photon, plasma, surface etc.) and / or hysteresis analysis. Further, the system may use semantic diffusion to implement semantic hysteresis and vice-versa. Semantic superposition may be computed on quantum computers based on the superposition of the quantum states. Alternatively, other computing platforms as explained in this application are used for semantic superposition.

[0538] The system may budget and project superposition factors. In some examples, a user may specify the maximum level and / or threshold interval of superposition for inferences, views, routes, goals and other inference and viewing based artifacts; further, it may specify superposition budgets, factors and goals.

[0539] The semantic field comprises a number of semantic scenes. The system may process the semantic field based on semantic scenes and eventually the factors / weights associated to each semantic scene; the semantic scenes may be used to understand the current environment and future semantic scene and semantic field developments. A semantic scene can be represented as a semantic artifact. In some examples the semantic scenes comprise localized semantic groups of semantic artifacts; thus, the semantic scenes may be represented as localized (e.g. simple localized and / or composite localized) semantic models and groups.

[0540] A semantic group represents a grouping of artifacts based on at least one semantic relationship. A semantic group may have associated and be represented at one or more times through one or more leaders of artifacts from the group. A leader may be selected based on semantic analysis and thus might change based on context. Thus, when referring to a semantic group it should be understood that it may refer to its leader or leaders as well. In some examples, the leaders are selected based on semantic factors and indicators.

[0541] A semantic group may have associated particular semantic factors (e g. in semantic views, trails, routes etc.).

[0542] A semantic view frame is a grouping of current, projected and / or speculative inferred semantics. In an example a semantic field view frame comprises the current inferred semantics in the semantic field; a semantic scene view frame may be kept for a scene and the semantic field view frame is updated based on a semantic scene view frame. A peripheral semantic scene may be assigned lower semantic factors / weights; as such there may be less inference time assigned to it. Additionally, the semantic group of sensors may be less focused on a low weight semantic scene. In an example, a semantic scene comprising a person riding a bicycle may become peripheral once the bicycle passed the road in front of the vehicle / carrier just because the autonomous semantic system focuses on the main road. Asemantic view frame may be represented as a semantic group and the system continuously adjusts the semantic factors of semantics, groups, objects and scenes.

[0543] Semantic view frames may be mapped or comprised in semantic memory including caches and hierarchical models.

[0544] For a peripheral semantic scene, the semantic system retains the semantics associated with that scene (e.g. semantic scene view frame) longer since the status of the scene is not refreshed often, or the resolution is limited. In some examples the refreshment of the scenes is based on semantic analysis (e.g. including time management) and / or semantic waves and signals. A predictive approach may be used for the semantic scene with the semantic system using certain semantic routes for semantic inference; semantic routes may be selected based on the semantics associated with the semantic scene and semantics associated with at least one semantic route. In the case that the peripheral scene doesn't comply with projections, inferred predicted semantics or semantic routes the semantic system may change the weight or the semantic factor of that semantic scene and process it accordingly.

[0545] In an example, once the bicycle and the rider becomes peripheral the system may refocus the processing from that scene; if there is something unexpected with that semantic scene (group) (e.g. a loud sound comes from that scene, in which case the system may infer a “LOUD SOUND” semantic based on the sound sensors) the system may refocus processing to that scene.

[0546] In further examples, the system blocks / gates some sounds and / or factorizes others based on the perceived peripherality and / or importance (e.g. based on location, zone, semantic identity, semantic etc.). Further, the system may infer leadership semantic artifacts associated with the non-peripheral and / or peripheral scenes and use them to enhance the non-peripheral scenes and / or gate peripheral scenes.

[0547] Analogously with peripheral scene analysis the system may implement procedural tasks (e.g. moving, climbing stairs, riding a bicycle etc.) which employ a high level of certainty (e.g. low risk factor, high confidence factor etc.). Thus, the procedural semantic analysis and semantic view frames may comprise only the procedural goal at hand (e g. RIDING THE BYCICLE, FOLLOW THE ROAD etc.) and may stay peripheral if there are no associated uncertainties (e.g. increasing risk factor, decreasing confidence / weight factor etc.) involved in which case semantic artifacts may be gated to / from higher semantic levels.

[0548] The system uses semantic analysis, factors and time management to determine the reassessment of the scenes / frames and / or the semantic gating for each scene / frame (and / or semantic groups thereof).

[0549] In rapport with a semantic view, the semantic view frames which are peripheral, predictive and / or have highly factorized cues (e.g. based on low entanglement entropy) the semantic time quanta and / or budgets may appear to decay slower as they may require less semantic time and / or entanglement entropy budgets.

[0550] Semantic inference based on semantic composition and / or fusion allow for generalization and abstraction. Generalization is associated with composing semantic / s and / or concepts and apply ing / as signing them across artifacts and themes in various domains. Since the semantics are organized in a composite way, the system may use the compositional ladder and semantic routing to infer semantic multi domain artifacts.

[0551] Generalization rules may be learned for example during semantic analysis and collapsing artifacts composed from multiple semantic fluxes and / or gated semantics.

[0552] In some examples generalization rules learning comprises the inference and association of higher concepts and / or semantic artifacts (e.g. rules, routes, model artifacts etc.) in rapport with fluxes, signals, waveforms and / or semantic waves.

[0553] It is to be understood that particular semantics may be available, associated and / or inferred only within particular hierarchical levels, endpoints, semantic groups (e.g. of endpoints, components etc.) and / or stages. Thus, when a semantic signal and / or wave transitions in the semantic network, those semantics may be decoded and / or inferred only in those particular contexts.

[0554] A semantic group may comprise artifacts which change position from one another. The semantic engine identifies the shapes and / or trajectories of one artifact in relation with another and infers semantics based on relative shape movement and / or on semantic shape. The trajectory and shapes may be split and / or calculated in further semantic shapes, routes and / or links where the system composes the semantics in shapes or links to achieve goals or factors. The semantic engine may determine semantic drift and / or distance between artifacts based on endpoints, links, semantics assigned to artifacts (including semantic factors), indexing factors and / or further semantic analysis.

[0555] The system may infer sentiments for the distance and motion semantics based on the context. In an example, if the system is in a 75 % TAKEOVER FRONT VEHICLE / CARRIER drive semantic as a result of a 75 % SLOWER FRONT VEHICLE / CARRIER and it is in a semantic route of FRONT VEHICLE / CARRIER FAR, INCOMING VEHICLE / CARRIER FAR it may infer a REASONABLE RISK for takeover while further using a semantic trail of FURTHER APPROACH THE FRONTVEHICLE / CARRIER, PRESERVE VISIBILITY; as hence, the risk is reassessed based on the semantic trail, view inferences and further semantic routes (e.g. CLOSED GAP, FRONT VEHICLE / CARRIER 90 % SLOW, INCOMING VEHICLE / CARRIER 40 % FAST, CAN ACCELERATE FAST 70 % and thus the factor of the risk indicator for TAKEOVER FRONT VEHICLE / CARRIER is still within contextual and / or profile preferences, factorization / semantic (time) intervals and / or biases) and the drive semantic affects the semantic routing and orientation (e.g. takeover actions). It is to be understood that the system may adjust the factor for the drive semantics (e g. 25 % TAKEOVER FRONT VEHICLE / CARRIER) based on further inferences and risk assessment (e.g. 40 % SLOWER FRONT VEHICLE / CARRIER, 90 HIGH TRAFFIC -> NOT WORTH RISK) and / or delay and / or expire the drive semantic altogether; it is understood that the delay and / or expiration may be based on semantic indexing (e.g. time, space) and / or time management wherein the system uses existing and / or learned artifacts. In further examples, the system infers a VEHICLE / CARRIER CRASH associated with a semantic group identity in a semantic view and as hence it adjusts the routes, rules and / or model to reflect the risk factors associated with the particular semantic group (e.g. in the semantic view context). It is to be understood that the system may use semantic (view) shaping to infer and / or retain particular semantic artifacts reflecting contexts captured in (hierarchical) semantic views potentially in a hierarchical manner. The semantic system also groups artifacts based on semantic inference and use those groups in further semantic inference. In our example the system may detect object types or complex semantics based on such semantic groups (e.g. group sensors, settings and detections and infer meanings, infer travelers by detecting flows of grouping of detections, features, clothing items and belongings; infer that a person is carrying a red bag etc.).

[0556] It is to be understood that the semantic system is a hybrid composable distributed cloud and as such it distributes, groups, compose and fusion various modalities detections in an optimized manner. The modalities may comprise a diverse spectrum of electromagnetic sensing.

[0557] A semantic stream is related with a stream of non-semantical and semantic information. A semantic stream may transmit / receive data that is non-semantical in nature coupled with semantics. As an example, if a camera or vision system mounted on a first location or first artifact provides video or optical data streaming for the first artifact, the first artifact may interpret the data based on its own semantic model and then transfer the semantic annotated data stream to another entity that may use the semantic annotated data stream for its own semantic inference based on semantic analysis. As such, if a semantic scene in a videostream, frame or image is semantically annotated by the first system and then transferred to the second system the second system may interpret the scene on its own way and fusion or compose its inferred semantics with the first system provided semantics. Alternatively, or additionally, the annotation semantics can be used to trigger specific semantic drives and / or routes for inference on the second semantic system. Therefore, in some instances, the semantic inference on the second semantic system may be biased based on the first system semantic interpretation.

[0558] In some examples a semantic stream may be comprised from semantic flux channel and stream channel; such separation may be used to save bandwidth or for data security / privacy. As such, the semantic flux is used as a control channel while the stream channel is modulated, encoded, controlled and / or routed based on the semantics in the semantic flux channel. While the channels may be corrupted during transmission, the semantic flux channel may be used to validate the integrity of both the stream channel and semantic flux channel based on semantic analysis on the received data and potentially correct, reconstruct or interpret the data without a need for retransmission.

[0559] It is to be understood that the semantic stream may comprise semantic wave and / or wavelet compressed and / or encry pted artifacts.

[0560] In another example, the semantic flux channel distributes information to peers and the stream channel is used on demand only based on the information and semantic inference from flux.

[0561] Further, the system may use authorization to retrieve data from the flux and / or stream channel; in an example, the authorization is based on an identification data / block, chain block and / or the authorization is pursued in a semantic group distributed ledger.

[0562] The system may associate semantic groups to entities of distributed ledgers. The distributed ledger semantic group may be associated with multiple entities and / or users; alternatively, or in addition, it may be associated w ith identities of an entity, for example, wherein the distributed ledger comprises various user devices. Sometime the distributed ledger is in a blockchain type network.

[0563] Virtual reconstruction of remote environments, remote operation and diagnosis are possible based on semantic models and real time semantic technologies. The objects from the scenes, their semantic attributes and inter-relationships are established by the semantic model and potentially kept up to date. While such reconstruction may be based on transfer models, in addition or alternatively, they may be based on virtual models (e.g. based on reconstruction of or using semantic orientation and shaping).

[0564] Sometimes, the ingesting system assigns a semantic factor (e.g. weight) to the ingested information; the assigned factor may be assigned to fluxes / streams and / or semantics in a flux / stream.

[0565] Themes are semantic artifacts (e g. semantic, semantic group) that are associated with higher level concepts, categories and / or subjects.

[0566] The semantic routes may be classified as hard semantic routes and soft semantic routes.

[0567] The hard-semantic routes are the semantic routes that do not change. At times (e.g. startup or on request), the system may need to ensure the authenticity’ of the hard- semantic routes in order to ensure the safety of the system. Thus, the hard semantic routes may be authenticated via certificates, keys, vaults, challenge response and so on; these mechanisms may be applicable to areas of memory that store the hard semantic routes and / or to a protocol that ensure the authentication of those routes. In some examples the hard semantic routes are stored in read only memories, flashes and so on. Semantic routes may be used for predictive and adaptive analysis; in general, the semantic routes comprise a collection of semantic artifacts and potential synchronization times; the semantic routes may be represented as a semantic group of semantic artifacts including semantics, groups, rules etc.; they may be identified based on at least one semantic. They may be also associated with semantic rules (e.g. time management, access control, factoring, weighting, rating etc.).

[0568] While the semantic routes are used for semantic validation and / or inference they may be triggered and / or preferred over other semantic routes based on context (e.g. semantic view, semantic view frame).

[0569] Semantic routes may be represented, associated and / or identified with semantic artifacts (e.g. semantic and / or semantic group) and as such they benefit from general semantic modeling and analysis. Semantic routes may comprise or be associated with semantic artifacts, semantic budgets, rewards, ratings, costs, risks or any other semantic factor.

[0570] In some instances, semantic routes representation comprises semantic groups and / or semantic rules.

[0571] Semantic routes may be organized in a hierarchical manner with semantic routes comprising other semantic routes. Such hierarchical structure may be recursive.

[0572] The semantic rules may be grouped in semantic groups and participate in semantic inference.

[0573] Analogously with the (hard / soft) semantic routes the semantic rules may be classified as hard or soft.

[0574] The semantic routes and rules may encompass ethics principles. Ethics principles of semantic profiles and / or semantic groups may model “positive” (or affirmative) rules / routes (e.g. DO, FOLLOW artifacts etc.) and / or (H / ENT) “negative” (or non-affirmative) rules / routes (DON’T DO, DON’T FOLLOW artifacts etc.) and their associated factors; as specified the “positive” and “negative” behavior may be relative to semantic profiles, semantic groups, semantic views, endpoints / links and / or semantic times.

[0575] In further examples, the (affirmative / non-affirmative) rules / routes and / or DO / DON’T may be associated with (allowable / not-allowable) capabilities and / or (counter)measures at endpoints and / or semantic intervals / times (e.g. (AROUND DOES HOUSE) - DO -> ALLOW / APPLY WATERJETS (AGAINST DRONES) (AROUND DOES HOUSE) (DURING (DOES) PARTY), DRONES->COUNTER-MEASURES- >WATERJETS (AROUND DOES HOUSE) (DURING (DOES) PARTY); (AROUND DOES HOUSE) DON’T -> ALLOW / APPLY SHARP OBJECTS (AGAINST DRONES) (AROUND DOES HOUSE) (DURING (DOES) PARTY)). A such, the system may not allow and / or may not apply capabilities / (counter)measures associated with factorized hazardous (around DOES house and / or during (DOES) party) (sharp) objects (having a highly factorized sharp (leadership) indicator and / or leadership associated attribute) because is non-affirmative with the (supervisor) interests / goals (at endpoints around the DOES house) (during a (DOES) part ). As mentioned, by H / ENT, DON’T ALLOW is similar with BLOCK and / or vice-versa’ alternatively, or in addition, DON’T BLOCK is similar with ALLOW and / or vice-versa.

[0576] It is to be observed that a supervisor may simulate the system with some of the behaviors inverted (e.g. some positive behaviors switched to negative and / or vice-versa). However, the system may not implement the “negative” behaviors due to (high factorized) (brokerage) (supervising) hard semantic routes and / or (high factorized) (supervising) (brokerage) fluxes which deny and / or supervise the behaviors based on the (supervising) (higher levels) laws of the land.

[0577] Ethics principles may be based and / or relative to semantic profiles comprising ethics semantic routes and rules; in some examples, the ethics principles are comprised in hard semantic and / or highly factorized trails, routes and / or rules. Semantic analysis may use ethics principles for semantic factorization. In some examples, during inference, positive behavior artifacts within or as related with semantic profiles and / or semantic groups and associated circumstances would be preferred to negative behavior based on areward to risk ratio interval thresholding. The reward may be based on publicity (e.g. gating) of behavior based inference; further the risk may entail bad publicity (e.g. gating of semantics which would cause “negative” behavior inference (relative to the particular semantic identities, semantic profiles) in collaborative semantic fluxes and / or semantic groups.

[0578] Projections of publicity (e.g. positive or negative) may be inferred through propagation and / or diffusion of gated semantics through various leadership artifacts and / or semantic fluxes. Thus, because particular fluxes may act as leaders, it is important to project the propagation and / or diffusion based on goals. In some examples, in cases where the budgets are low, the system may diffuse semantics which will first reach a “positive influence” leader as opposed to a “negative influence” leader. In further examples, the system may perform semantic orientation, routing and / or gating in order to achieve the publicity and / or influencing goals. It is to be understood that a “positive influencer” leader is relative to the goals of publisher and not necessarily towards the goal of the influencer (e.g. the influencer may have a negative behavior towards (NURSE) (JANE) artifacts but because the influencer's negative factors / ratings on (NURSE) (JANE) artifacts propagate and / or diffuse in groups which have low ratings, high risk and / or are “negatively” factorized of routes comprising the influencer then the overall goal of generating positive ratings on those groups may be achieved.

[0579] The representation of semantic groups may include semantic factors assigned to each group member. In some examples semantic factors determine the leaders in a group in particular contexts generated by semantic analysis. Sometimes, membership expiration times may be assigned to members of the group so, when the membership expires the members inactivated and / or eliminated from the group. Expiration may be linked to semantic rules including time management rules; further factor plans with semantic factors and semantic decaying may determine invalidation or inactivation of particular members. The semantic routes may be organized as a semantic model and / or as a hierarchical structure in the same way as the semantics and semantic groups are organized and following similar semantic inference rules.

[0580] The system may infer semantics by performing semantic inference on the semantic groups. In an example, the system may compose and fuse two semantic groups and assign to the new group the composite semantics associated with the composition of the first group semantics and the second groups semantics. Group leader semantics may be composed as well besides the member semantics. In some cases, only the leader semantics are composed. By combining the leader semantics with member semantics, semantic timing and decaying the system may infer new semantic rules (e.g. semantic time rules).

[0581] Further, in an example, the system performs semantic augmentation while inferring and / or identifying a person (JOHN) performing an activity (BASEBALL); using semantic analysis based on multiple semantic trails and routes it infers that JOHN’S skills factors are high and pursues a goal to EXPRESS OPINION TO BILL of the inference based on a semantic route of IMPRESSED SO EXPRESS OPINION TO PAL. Thus, based on a route for an template of PRONOUN VERB ADJECTIVE and further, based on grouping of JOHN as a (THIRD, ( (3 RD), 3rd)) PERSON based on PRONOUN routing, the inference may establish that a leadership semantic is 3 RD PERSON; as such, when being routed within the semantic network it may select artifacts that comply with such leadership semantic in semantic groups and further routes. Further, the system may have semantic groups such as PRONOUN ( (1 ST PERSON, ALL GENDERS, “I”), (2 ND PERSON, ALL GENDERS, “YOU”), (3 RD PERSON, MALE, “HE”), (3 RD PERSON, FEMALE, “SHE”)); and further IS (3 RD PERSON, ALL GENDERS); and further GOOD (ALL PEOPLE (1 ST PERSON. 2 ND PERSON, 3 RD PERSON). ALL GENDERS (MALE. FEMALE)) and thus the system may determine a semantic augmentation of JOHN IS GOOD based on a leadership semantic of 3 RD PERSON and other semantic analysis as appropriate.

[0582] In a further example of abstraction learning, the system may infer from BILL'S voice signals that JOHN IS GOOD and because has semantic groups that associate IS with VERB and GOOD with ADEJCTIVE it may infer a semantic route, template and / or semantic group of PRONOUN VERB ADJECTIVE; and further, similar and / or other semantic artifacts and / or relationships whether factorized or not. Further factorization may occur on such learned artifacts based on further semantic analysis.

[0583] Semantic decaying occurs when a quantifiable parameter / factor associated with a semantic artifact decays or varies in time, most of the time tending to a reference value (e.g. null value or 0); as such, if the parameter is negative decaying is associated with increases in the semantic factor value and if the factor is positive decaying is associated with decreases in factor’s value. Sometimes, when the semantic decays completely (e.g. associate factor is at the reference value or interval) the semantic may be inactivated, invalidated or disposed and not considered for being assigned to an artifact, semantic route, semantic rule, semantic model and / or inference; further, based on the same principles the semantic is used in semantic group inference and membership. The system asks for feedback on group leadership, semantic factors and / or group membership. The feedback may be for example from users, collaborators, devices, semantic gates and other sources.

[0584] In some examples, the reference decaying value is associated with applied, activation / deactivation, produced or other voltages and currents of analog or digital components and / or blocks. In further examples such values are associated with chemical or biological components and mixing elements.

[0585] Quantifiable parameters such as semantic factors may be assigned or associated with semantics. The semantic factors may be related to indicators such as weights, ratings, costs, rewards, time quanta or other indicators and factors. In some cases, the semantic factors are used to proportionate control parameters, hardware, I / O, analog and digital interfaces, control blocks, voltages, currents, chemical and biological agents and / or any other components and / or interfaces. Those quantifiable parameters may be adjusted through semantic inference.

[0586] The semantic factors may be associated to a semantic (e.g. semantic identity) implicitly (directly) or explicitly via a semantic indicator in which a semantic specifies the type of indicator (e.g. risk, rating, cost, duration etc.) and the semantic factors are associated with the semantic via semantic indicators.

[0587] The semantic factors may be associated to a semantic via semantic groups which may comprise the semantic, the semantic indicators and / or the semantic factors in any combinative representation of a semantic group. As such, the semantic factors participate in semantic inference and analysis.

[0588] When a semantic factor is assigned directly to a semantic the system may associate and interpret the indicator associated with the factor implicitly based on context. Alternatively, or in addition, the factor is assigned to various indicators based on context.

[0589] The factors are associated with degrees, percentages of significance of semantic artifacts in contextual semantic analysis.

[0590] Implicit or explicit semantic indicators may be defined, determined and / or inferred based on a context. In an example an indicator is inferred based on goals. In other examples multiple indicators are determined for a particular goal inference. In some cases, the system may substitute an indicator over the other, may infer or invalidate indicators based on semantic inference. As with other semantic rules the system may comprise indicator rules that specify the interdependencies between semantic indicators based on time management, semantic time, weights, ratings, semantics, semantic groups, semantic routes, semantic shapes and other semantic artifacts.

[0591] Semantic indicator rules and any other semantic rules may be associated with semantic artifacts, semantic factors and indicators. As such the system may performrecursive inference which is controlled by factor rules, decaying and other semantic techniques. Further, the semantic rules are inferred, invalidated, learned and prioritized based on such factor techniques; in general, the semantic techniques which apply to semantic artifacts apply to semantic rules.

[0592] Semantic factors may be associated with symbols, waveforms and patterns (e.g. pulsed, clocked, analog etc.). The association may be direct through semantics or semantic model. Further the semantic factors may be used in hierarchical threshold calculations (HTC) algorithms to determine a mapping to an endpoint.

[0593] Decaying and semantic factors may be inferred and learned with semantic analysis. In some examples the system learns decaying and factor semantic rules and semantic routes.

[0594] The semantic learning may include inferring, linking and / or grouping a multitude of trails and routes based on variation of circumstances (e g. location, anchor, orientation, profile, environment, sensor, modality, semantic flux, route etc.).

[0595] In further examples, the system optimizes the inference by factorizing and / or learning relationships in the network semantic model. In some examples the system uses the semantic analysis (e.g. based on action / reaction, action / reward etc.) to reinforce routes and paths (e.g. based on rewards, goals etc.). As such, when the system infers artifacts that are not against the DO NOT guidelines (e.g. blocked semantics, rules, routes), it may collapse the semantic artifacts, link and / or factorize them. In further examples, the system may cache such routes and / or map them at lower or higher level depending on factorization and / or theme. Further, when the system infers semantic artifacts which are against DO NOT (BLOCK) rules and / or guidelines it may associate and / or collapse them with semantic artifacts based on DO semantics, artifacts and / or rules. It is to be understood that the DO and DO NOT semantic artifacts may be associated with time management rules (e.g. it may be allowed to DO a BATTERY DISPOSAL in a HAZARDOUS RECYCLING circumstance while in all other circumstances the DO NOT artifacts apply).

[0596] When the system infers a gating mle it may adjusts and / or invalidate rules, routes and / or further artifacts which may activate gating based on such rule. If the gating is a block / deny rule the system may decay such artifacts. If the gating is based and / or controlled on interval factor thresholding the system may adjust the semantic rules.

[0597] A semantic time budget may comprise a time interval or time quanta required to perform an inference; in some examples the semantic time budget is based on semantic time. Semantic cost budgets comprise an allowed cost factor for the semanticinference. Semantic budgets may comprise and / or be associated with other factors and indicators (e.g. risk, reward etc.). Semantic budgets may be based on predictions / projections based on a variety7of factors and may be associated with semantic composition, time management rules, access control rules and / or semantic routes. Also, they may be correlated with the hardware and software components characteristics, deployment and status in order to generate a more accurate budget inference.

[0598] Semantic budgets may include inferences about the factors to be incurred until a semantic goal or projection is achieved; also, this may comprise assessing the semantic expiration, semantic budget lapse and / or semantic factor decaying. Such assessment of factors may be interdependent in some examples.

[0599] Sometimes, the semantic thresholds and / or decaying are based on a bias where the bias is associated with particular semantics, factors and / or budgets.

[0600] In an example, semantic budgets may be specified by semantic time intervals. Further, semantic budgets may be specified based on decay ing, factor and indexing rules.

[0601] In further examples the semantic budgets may comprise and / or be associated with prices (e.g. utilizing 10 quanta budgets in a computing and / or energy grid environment comprises 0.4W power consumption and / or 0.05$ charge etc.). It is to be understood that the inferences may be based on any budget including time, price, risk, reward and / or other factors and indicators. Further, the system may comprise time management rules specifying that the utilization of 10 quanta budgets in particular circumstances (e.g. time management) may entail additional bonus budgets made available (potentially also having an expiration time management) to the user and / or flux and thus the system may associate and / or index budgets with particular components, units, fluxes, routes and further factorize them (e.g. factorize a PREFERRED indicator for the bonus provider flux in rapport with particular inferences).

[0602] Semantic (time) budgets enable crediting and / or rewarding providers for their capabilities (at a semantic time and / or used during a (published) semantic time). As such, a user / consumer of the capability7(at a semantic time) incurs a charge and / or is debited for the respective capability budget while the provider is credited with the budget for the respective capability7.

[0603] A creditor (or provider of credit / crediting and / or consumer of debit / debiting) may be associated with a provider (e.g. through a capability and / or asset) and / ora debtor (or consumer of credit / crediting and / or provider of debit / debiting) may be associated with a consumer through an interest.

[0604] The creditor / provider agent may be a higher-level supervisor to a capability and / or asset (handed over) (for lower (factorized) level temporary supervision) of a debtor / consumer agent in a (potential recursive) hierarchical manner. The temporary handover may be based on a contract comprising clauses and / or further associated semantic times.

[0605] While in the application we specify higher (-) level or similar it is to be understood that this may be substituted for / to higher factorized level. Similarly, lower (-) level or similar may be substituted for / to lower factorized level. Further, H / ENT of hi h / low may be applied to factorizations.

[0606] A higher-level supervisor may have access to higher (factorized) level and / or hard semantic routes and / or behavior configuration while a temporary' (lower level) supervisor may not.

[0607] A consumer may compose and / or publish capabilities under temporary supervision while potentially composing and / or indexing their associated budgets and / or (associated) semantic times (based on a set of rules and / or routes). In examples, the UNDOES have under temporary' supervision (e g. based on a contract comprising clauses and / or semantic times) S2P2 and / or its power (generation) unit / storage from DOES and S3P3 and / or its power (generation) unit / storage from SP3. As such, the UNDOES may combine and / or couple the power (generation) capabilities into a composable power (generation) capability and / or further adjust the semantic times and / or budgets.

[0608] The system may compose clauses of a contract, explanations and / or purpose associated with capabilities.

[0609] In examples, DOES / S2P2 provides to UNDOES 12V at 10A WHEN TULIP CARRIER PRESENT while S3P3 provide to UNDOES 12V at 6A and further 12V at 10A WHEN S2P2 / S3P4 PRESENT WITH 80% CHARGE. As such, the UNDOES capability may be fused and / or composed such as providing an intrinsic / default 12V at 6A and / or further 12V at 10A WHEN TULIP CARRIER / S2P2 / S3P4 PRESENT WITH S2P2 / S3P4 80% CHARGED and / or 12V at 6A at any other (high entropy) semantic time.

[0610] It is to be observed that based on semantic times the capabilities may compose (e.g. the power provided to UNDOES comprises power provided by S3P3 and S2P2; and / or the power generated by the TULIP CARRIER (and S3P3) (and S2P2) etc..

[0611] Further, the credits generated by the UNDOES power (generation) capabilities (e.g. through usage and / or possession by interested parties) may comprise creditsto higher-level providers (e.g. DOES, SP3) based on contractual clauses. As such, when the UNDOES capability is acquired, hand-over and / or possessed portions of credits may go to DOES and / or SP3 and / or further higher-level supervisors (agents / brokers).

[0612] The portions of the credits may be based on semantic times. In some examples, UNDOES is credited with supervision use of a / the TULIP CARRIER until the first snow and further, based on the clauses and / or profile preferences DOES / SP3 are / is credited with supervision use of the TULIP CARRIER within the credited UNDOES semantic time to first snow (e.g. until JANE arrives). Alternatively, or in addition, the system may apply indexing and / or factorization clauses to portion credits (e.g. 10% of credits and / or budgets, 90% of clean energy credits and / or budgets etc.). It is to be understood that such crediting may be hierarchical (e.g. because SP3 power generation capabilities are supervised by JOHN he may get portions of the SP3 credits generated by the UNDOES capability).

[0613] Similarly with crediting the system may apply and / or generate portions of debiting based on hierarchical consumer interests.

[0614] Credits and / or debits may be transacted and / or stored into a (user / device) digital wallet, blockchain, (virtual) (digital) bank / card account, on a device and / or on a tenant.

[0615] A capability liability is an (insured) obligation to provide / enable / allow a capability and / or perform / enable / allow an activity (at a semantic time) (within a budget) to a provider. In some examples, the semantic time may be based on inferences from the liable party related to a semantic flux associated with the provider. By H / ENT, a capability asset is an (insured) availability of the capability and / or the activity (at a semantic time) (within a budget) to the provider. It is to be observed that the provider may further barter / trade (portions of) his asset to an interested consumer; in some examples, the trade is based on a contract clause (comprising affirmative / non-affirmative (in rapport with the holder of liability / liable part} ) resonant destinations, semantic identities and / or semantic times) and / or approval from the holder of liability. As such, (portions of) the capability liability and capability assets may be distributed to multiple parties.

[0616] A capability' based on a liability may be (only) published and / or marked as being based on liabilities from other parties.

[0617] In some examples, a capability based on a liability may comprise traceability and / or semantic trails comprising all Hables’ parties non-distorting (blurring) semantic identities.

[0618] Alternatively, or in addition, a capability based on a liability may comprise the number of (hierarchical) liable parties and / or associated (routes / trails / chains of) transactions.

[0619] A transaction (document / snippet) may be stored in a (container) memory (and / or a communication enabled device / tag) as a record / block and may comprise the provider and / or consumer identities and / or further clauses and / or inferences. Parts of a transaction record / block / snippet may be blurred and / or encrypted. Alternatively, or in addition, a transaction document / snippet may be physically stored in a container; further, the document may be parsed based on inputs from (container) sensors (and stored in the memory / device / tag).

[0620] Transaction / contract information and / or semantic identities may be published / diffused from within the container (s) (hierarchy) (at / within (a hierarchy of) endpoints). In some examples, they may be associated with logistic laws, clauses and / or incoterms.

[0621] The system may check that stipulated transactions, clauses, constraints, protocols, semantic identities and / or handovers (at endpoints) match, are similar and / or not distorted between the inferred actual (at endpoints) and the (published) (carried) (documented) contractual clauses and / or further laws of the land. In case that they do not match the system may block (container) movement, route and / or diffuse to particular (likeable) endpoints / fluxes and / or perform semantic augmentation (to supervisors).

[0622] Alternatively, or in addition, the system my infer particular (transaction) (container) semantics (at endpoints) and the system routes, leaks and / or diffuses the items / containers to likeable endpoints (e.g. based on a drift between (published / configured / inferred) endpoint semantics and / or container (published / configured / inferred) semantics, projections etc.).

[0623] Alternatively, or in addition, the system may (use semantic discovery7to) extract, receive and / or become more informed about the contractual clauses by retrieving and / or parsing data from other sources such as documents, web pages etc.

[0624] Alternatively, or in addition, the system may project and / or factorize indicators for contractual clauses and / or augment supervisors in case of (non) affirmative / likeable inferences. Alternatively, or in addition, the system may generate and / or evaluate contractual clauses based on affirmativeness / likeability projections.

[0625] In further examples, the system may generate / identify leadership semantic routes, compositions, indicators and / or further inferences associated with affirmative / non-affirmative and / or likeable / non-likeable projections and / or factorizations (e.g.of clauses etc.). Alternatively, or in addition, the system may generate / identity leadership measures / counter-measures (indicated, associated with and / or based on semantic routes, compositions, indicators and / or further inferences) associated with (steering towards) affirmative / non-affirmative and / or likeable / non-likeable projections, factorizations and / or orientations.

[0626] Alternatively, or in addition, the system may challenge fluxes.

[0627] Protocols, transactions and / or clauses may comprise activities. As such, the protocols, transactions and / or clauses may have associated and / or be factorized on a readiness criteria / indicator inferred based on the comprised activities readiness.

[0628] In some examples, a constraint / contractual clause (on / between fluxes, at Does house (recycling) endpoint (s) etc.) specifies that the agent / provider asset (e.g. Tulip carrier, (hazardous) container manipulator) should perform sanitization (protocols) after picking up the a hazardous substance container at the Does house and thus, the system determines the likeable sanitization capabilities, (sub)protocols and / or endpoints based on the publishing, availability and / or constraints (e.g. DO NOT clauses / rules at endpoints) and / or further asset (semantic identities / interests / capabilities / attributes).

[0629] In further examples, consumers / containers (devices / fluxes) may publish interests on how (assets / containers) to be manipulated at endpoints and the system may further matches it with (provider / agent) capabilities.

[0630] Publishing / capabilities / interests may comprise and / or be associated / grouped with constraints (e.g. such as not likeable / unlikeable, DO NOT, NO, AVOID, NOT etc.). As such, while projecting, matching and / or factorizing capabilities / interests the system may factorize the constraints (which may be or not included / comprised / linked / grouped with a (published) capability / interest). Alternatively, or in addition, the system may (project) factorize the constraints with and / or without the capabilities / interests.

[0631] Semantic trails (hierarchy) comprise (s) the progression in the execution of a transaction, protocol, clause and / or contract; a semantic trail (hierarchy) may comprise the (inferred) semantics (which may have assigned / linked / grouped handover / readiness / transaction snippets, transactions and / or documents) associated with the protocol, activities and / or further movement / manipulations / handovers / readiness.

[0632] Alternatively, or in addition, semantics in the semantic trails may be assigned and / or linked (manipulation / activity) video / image snippets which may be associatedand / or linked with activities, transactions, readiness, handovers, documents and / or (further) clauses (at transaction / handover endpoints).

[0633] The system may semantically analyze the (likeability / resonance / drifts) between the semantic trails and the semantic routes of the protocol (goals) to infer likeable / resonant / drifted progression and / or readiness; further, the system may perform augmentation based on such inferences.

[0634] A capability based on liabilities may not publish a (full) traceability although publishing the number of liable parties and / or number of transactions (in a (block)chain). Alternatively, or in addition, partial traceability / trails may be published wherein particular semantic identities and / or (associated) transactions and / or chains are not published, blocked from publishing and / or blurred.

[0635] Alternatively, or in addition, a capability7based on a liability7may be published such as liable parties, transactions and / or (block)chains can be visualized and / or accessed as per publishing and / or access control. In similar ways, semantics and (further) linked artifacts in / with semantic trails may be published, diffused, gated and / or blurred.

[0636] Semantics and further assigned / linked / grouped artifacts in / with semantic trails may be published, diffused, gated and / or blurred.

[0637] In examples, semantic trails may be associated with movement of cargo and / or containers and the semantic trails comprise the semantics inferred during their movement (at / between endpoints). Furthermore, semantics in the semantic trails may be assigned and / or linked with / to a (occurring) transaction records, activities and / or chains (at an endpoint).

[0638] Alternatively, or in addition, semantics in the semantic trails may be assigned and / or linked (manipulation / activity) video / image snippets which may be (further) associated and / or linked with transactions, activities, goals and / or (further) clauses (at endpoints). Further, the assignment and / or linking may be based on semantic matching analysis between the trails (semantics) and routes (semantics) of transactions, activities, goals and / or (further) clauses (at endpoints). Alternatively, or in addition, the video / image snippets may be associated with the semantics in the trails / routes based on a semantic matching between the inferred video / image semantics and the semantics in the trails / routes (at endpoints).

[0639] In some examples, a semantic trail comprises conditions and / or inferred semantics and / or semantic times at endpoints. Alternatively, or in addition, they may comprise (transaction) semantic identities inferred at endpoints.

[0640] In some examples, (particular) liable parties and / or transactions are grouped and / or control accessed based on particular group semantics.

[0641] Particular semantic identities and / or transactions may be blurred as per semantic rules.

[0642] It is to be observed that a credit / debit (or (associated) debtor / creditor) and / or crediting / debiting and / or (linked / entangled) liability / asset are indicators and / or attributes in a high entropy relationship and thus, HENT inferences may apply to infer one from the other.

[0643] Further, based on semantic times a capability may be valued, debited and / or credited based on a particular semantic identity, profile, resonances and / or further circumstances. In examples, SOUP AT LUNCH (WHEN JOHN PRESENT OR PROJECTED TO ARRIVE (+ / - 10 MINS)) AND / OR (IN / FOR 30 MINS) may (be indexed to) resonate more and / or bear more credit and / or goodwill than SOUP AT DINNER AND / OR SOUP AT LUNCH IN / FOR 45 MINS and / or SOUP AT LUNCH AFTER JOHN LEAVES (e g. for presence of resonant artifacts with John and / or for a particular resonant semantic group indicative (e.g. via factors, factorized indicators, resonance etc.) that soup is preferred at lunch vs dinner). Further. BEEF SOUP AT DINNER may bear no credit in case of a goal of EVERY DINNER WITHOUT MEAT (within particular semantic views); alternatively, or in addition, BEEF SOUP AT DINNER may be value indexed based on the (factorized) urgency / pressure / priority of goals and / or interests (e.g. is highly valued due / by 90 MEAT NEXT MEAL, LIKE / EAT MEAT etc.). As such, the system projects (group / goal) resonances and / or entanglements at / around endpoints and / or (further) routes (at / for semantic times).

[0644] The capabilities, interests and / or further semantic times may determine entanglements and / or semantic groups (at / between endpoints). In examples, a goal and / or capability of S4P11 (endpoint) of SUPPLY PREMIUM GAS and / or 110V AT 10A WHEN S2P2 ARRIVES / PRESENT / ABSENT may determine an / a (semantic time) affirmative entanglement between S2P2 (interest) and S4P11 / endpoint as S4P11 / endpoint provides a capability based on a semantic time (affirmative / non-affirmative) resonant / associated with S2P2’s arrival / presence / absence and / or further (in) interests. It is to be observed that the entanglement may be collapsed and / or observed in semantic views which comprise and / or project the goals and / or further routes of the entanglement and / or can infer the particular semantic times.

[0645] Alternatively, or in addition, an affirmative entanglement may comprise an affirmative grouping and / or resonance and / or (further) (associated) semantic identity based on a semantic time (e.g. associated with a S2P2 presence) and / or S4Pl l / endpoint.

[0646] In examples, as S3P10 doesn't know and / or cannot infer / project the entanglement semantic time it cannot observe the entanglement which may be (affirmatively / non-affirmatively) factorized as cloaked / random (in rapport with S3P10 semantic views). However, as S5P5 knows and / or projects that S2P2 is driving the Tulip carrier and / or is interested in PREMIUM CHARGING / GAS it can observe the entanglement and / or further (non-randomly) (affinnatively / non-affirmatively) factorizing it in (coherent) collapsible (semantic views) inferences.

[0647] The observing party of the cloaked entanglement (e.g. S5P5) requires energy to follow / collapse the entanglement. The non-observing part)7of the cloaked entanglement (e.g. S3P10) may have (dark) (entangled) budgets / energy tunneled based on the affirmative resonance with S5P5. In some examples, the tunneled (energy) budgets are provided / tunneled through flux via an / a (dark) (flow) agent; in further examples, the tunneled (energy ) budgets are provided / tunneled through quantum tunneling wherein an / a (dark) (flow) agent (e.g. associated with a (bonded) electron / atom / photon (flow) and / or further currents / beams) passes through an energy barrier and / or (associated) semantic divider / coupler gate.

[0648] In the example, S5P5 may have the capability (or routes and / or fluxes) and / or resonance to observe the cloaked entanglement of / to S3P10 (with S2P2 and S4P 11 / endpoint entanglement) and further (non-randomly) (coherently) collapsing it; such collapse may be achieved (hierarchically) via flux and / or affirmative resonance (with S3P10). As such, S5P5 is dark entangled with S3P10 and / or its cloaked entanglements.

[0649] Alternatively, or in addition, S5P5 may be (dark) entangled with (other) dark entanglements of S3P10. As such, dark entanglements may be hierarchically organized, accessible and / or collapsible.

[0650] S5P5 cannot observe and / or collapse a dark entanglement unless is affirmative resonant with S3P10. As S5P5 is or becomes non-affirmative resonant with S3P10 its inferences based on the dark entanglement and / or with S3P10 are invalidated / deleted.

[0651] It is to be observed that in rapport with a non-observing artifact (e.g. such as of S3P10) and / or associated non-informed semantic view the collapsing (or measurement) of the dark / cloaked entanglement can occur and / or be valued as random while for an observing party7(e.g. such as of S5P5) and / or associated informed semantic view thecollapsing may not be random. However, an informed party and / or semantic view within a higher / lower hierarchy (endpoint) may be uninformed within a lower / higher hierarchy (endpoint). As such, while in some circumstances S5P5 can non-randomly collapse a dark / cloaked entanglement (at an endpoint) in other circumstances S5P5 can only observe a dark entanglement as randomly collapsing (at an endpoint).

[0652] A semantic profde may encompass preferred capabilities and / or budget intervals at semantic times. Alternatively, or in addition, a preference comprises and / or indicates interests which further indicate and / or comprise preferred capabilities. As a user, device and / or vehicle / post is localized at endpoints it may communicatively couple and / or transfer profiles and / or preferences (e.g. selected based on inferred semantic (times)) and the system may assigns capabilities based on (further) matching (endpoint) capabilities with preferences, interests and / or profiles.

[0653] During matching between the capabilities and / or interests, the system may infer semantic groups, semantic identities, composite semantics and / or establish network model links (e.g. between capabilities and / or interests (endpoints / groups)). Alternatively, or in addition, the system infers semantic groups, semantic identities, composite semantics and / or assigns them to the established network model links. Further, the system may de-establish, invalidate(or cancel or delete) the links once the interests are completely and / or partially satisfied.

[0654] In some examples, the system matches and / or assigns S10P 20KWh J 1772 CHARGE PLUG capability and / or (CHARGING UNTIL FULL) interest to a Does house 20KWh J1772 CHARGER SOCKET (ALLOWING CHARGING FOR 30MIN) endpoint and / or creates a network model link between SI OP charging socket and the charger endpoint and further assigns them CHARGING (AT 20KWh THOROUGH ONE JI 772) (UNTIL FULL). Alternatively, or in addition, (at a higher semantic network model level) the system may link SI OP to Does house endpoint and further assigns it CHARGING UNTIL FULL.

[0655] Alternatively, or in addition the system groups a plurality of links into a single link and assign a composite semantic (attribute) inferred based on the group’s link’s semantics (attributes) (e.g. a link of a semantic 20KWh THROUGH ONE J1772 and another link of semantic 20KWh THROUGH ONE J3068 may be collapsed to a composite link of 40KWh CHARGING (AT DOES HOUSE)(UNTIL FULL)). It is to be observed that at a lower level an interest (e.g. CHARGING UNTIL FULL) may be only partially satisfied, while at ahigher level the interest is completely satisfied. Once an interest is (completely / partially) satisfied the system may invalidate the links at a higher and / or lower levels.

[0656] In some examples, the debiting and crediting happen at the same (semantic) time while in other examples happen at different (semantic) times (potentially comprised both within another semantic time in a hierarchical manner).

[0657] A broker may keep associations between crediting, debiting and / or associated semantic times. The crediting and / or debiting may be based on bargaining by the broker.

[0658] The bargaining (by the broker) and / or the other brokerage activities and / or capabilities may be based on crediting and / or debiting and / or capability / interest matching.

[0659] The bargaining may encompass and / or determine access control to endpoints. As such, the system may allow / block / diffuse access / ingress / egress (to endpoints / links / capabilities) based on affirmative / non-affirmative bargaining.

[0660] Similarly, the system may negotiate and / or bargain activities at endpoints. As mentioned, entities and / or semantic profiles may indicate particular likeable interest activities at endpoints / links. As such, the system may project the likeability of interest and / or bargained activities based on the goals assigned for the endpoints / links. Further, the negotiation may comprise augmentation challenges to the user.

[0661] Brokers may be (flux) coupled, organized, assigned and / or associated with endpoints and / or related artifacts / inferences in a hierarchical manner (e.g. such as resembling the endpoint hierarchy). As such, a broker may act as an intermediary between associated endpoints (and related artifacts / inferences and / or further crediting / debiting / bargaining) and further (higher level) broker (s)Zbrokerage (s).

[0662] The credits may be added and / or stored to a (credit / receivable) block and / or blockchain. The debits may be subtracted, marked (e.g. as debi t / 1 iabi 1 i ty, subtracted etc.) and / or added to a (debit) block and / or block chain.

[0663] In further examples, a trade system may be implemented wherein a user / consumer (e.g. Jane, a semantic group (comprising Jane) and / or associated semantic system (s)) bargains a projected ownership and / or supervision of an asset and / or (further) capability and / or budget (e.g. of an energy quanta, an issue of Health Affairs newspaper, a goodwill, an inventory etc.) (at sematic times) to incur a charge / liability for using an active capability of a provider / producer (e.g. a tree services provider, S2P2, John, semantic group (s) thereof etc.); in some examples, the capability may be current and / or projected. Further, theprovider / producer may know that at a semantic time (e.g. within / at Jane’s ownership and / or supervision) the possession and / or (further) supervision of the asset / item (e.g. handover of the asset by Jane and / or temporary' supervision under Jane’s supen ision / ownership) may be (affirmatively) factorized (for its goals). Such matching may occur based on semantic times and / or may further be insured by insurance brokers and / or providers and / or assets under their ownership and / or supervision at semantic times.

[0664] It is to be observed that the possession may be affirmatively / non- affirmatively factorized (based on goals). In some examples, a / an (intrinsic) goal at an endpoint (e.g. order dispensing) may be to ensure that “that (manufactured / released) items are handed- over and / or possessed by allowable and / or likeable (semantic) identities”. As such, the system may determine the ordering semantic identity and / or further match it with the handed over and / or possessing semantic identity after the item is manufactured / released. Further, the system may project and / or factorize risks / hazards / (non-)affirmativeness / (non-)likeability that the items may be picked-up, handed over and / or possessed by not-allowable semantic identities. It is to be observed that a composed semantic identity encompassing a (non-allowable) bonding / possessing semantic identity (non-affirmatively) possessing / bonding a / an (nonallowable) bonded / possessed semantic identity it may be non-affirmatively factorized (at endpoints).

[0665] The system may factorize the likeability / affirmativeness of (inferred) semantic routes / trails (semantic identities) and / or semantic groups. Such factorizations may comprise the semantics in the routes / trails and / or groups which may be further associated with endpoints and / or links.

[0666] Handover, pickup and / or possession allowability / non-allowability may be based on being affirmative with the goal at pickup endpoint and / or allowable semantic identities (in a hierarchical manner). In examples, Jane orders and picks up her latte; alternatively, or in addition, John and / or other semantic identities may pick up the latte (for Jane) (based on the Does grouping and / or Jane’s semantic profile and / or indications). A pickup and / or possession by other (non-affirmative / non-resonant) entity (e.g. S0P97 etc.) may be deemed as not likeable and thus, it may perform augmentation to Jane, Does and / or at the endpoints (e.g. to warn possessor, supervisor etc.).

[0667] It is to be observed that Jane’s pickup (or handover from the provider to consumer (Jane)) may be affirmative resonant at the endpoint based on her grouping and / or entanglement with an item. In examples, once Jane purchases the latte she is affirmatively entangled at the pickup endpoint with a (particular) semantic identity (e.g. latte for Jane, lattefrom CoffeeForU etc.) and / or late (or asset and / or provider 1 iabi 1 i ty) and / or non-affirmatively entangled with the other available drinks and / or semantic identities.

[0668] Semantic profiles may specify pickup allowable semantic identities and the system further matches the semantic identities at pickup endpoints. In examples, Jane specifies / indicates (in a profile and / or by a gesture) that she wants her "one shot lates” to be picked up (at semantic times) by herself and / or by “a person named / identified as John with a black tie”, “a person showing up a (red) tulip (on a device screen)”, "a person performing my pickup gesture”, “a person holding Health Affairs and showing up 9788 on a screen” etc.). Alternatively, or in addition, Jane specifies that "tw o shot lates” be picked up by herself and / or “a nurse with Health Affairs” etc.. As such, the system matches the semantic identities of the tendered / purchased / ready items with the semantic identities in the semantic profile (e.g. one shot late, two shot latte) and further the inferred (possessing) semantic identities at pickup endpoints with the allowable semantic identities for pickup as specified in the semantic profiles. As such, the system may allow and / or not generate alerts if the semantic identities match and / or are litle drifted and / or not allow- and / or generate alerts otherwise.

[0669] In some examples, semantic profiles may have associated accounts and / or further semantic identities from which the funds to be withdrawn and / or associated items to be paid for. Alternatively, or in addition, it comprises gestures indicating an / the account (s) and / or a semantic identity / identities -> account pair (s), group (s), endpoints and / or route (s). In some examples, Jane’s semantic profile specifies that she wants to pay with a / her credit wallet for “coffees with a model” at “libraries and / or school”, “Green POSs”, “when the luminescence is low” and with a particular account / card otherwise.

[0670] Further, Jane’s profile may have gestures associated with indicating the (credit) wallet and / or particular (virtual / physical) account / card (at POS / for purchases); alternatively, or in addition, Jane’s profile comprises a gesture indicating (“coffees with a model” ’’Green POSs”) “luminesce is low” -> “credit (chain) wallet”; “coffees with a model and / or green POSs when luminescence is low use / pay credit (chain) wallet” etc.) routes / groups etc.

[0671] Capabilities / interests may be matched based on semantic drift inference and / or semantic grouping. Further, the capabilities / interests may be composed and / or published based on semantic identities, semantic groups, endpoints, supervisors and / or associated hierarchies thereof.

[0672] Capabilities / interests may be published by operators and / or supervisors of semantic fluxes, endpoints and / or associated devices, modules, posts and / or carriers.Alternatively, or in addition, capabilities / interests may be enabled, activated and / or published by users of devices, modules, posts and / or carriers. Publishing and / or availability (for matching) of capabilities / interests may be indicated, configured and / or allowed / blocked / enabled / disabled / activated / inactivated pre-discovery (e.g. before being inferred) and / or post discovery (e.g. after being inferred).

[0673] The publishing may be configured and / or based on (inferred) semantic times. Alternatively, or in addition, the system infers a semantic and / or (further) semantic time and an operator / supervisor publishes based on the inferred semantic and / or (further) semantic time.

[0674] Publishing / capabilities / interests may comprise and / or be associated / grouped with constraints (e.g. such as not likeable / unlikeable, DO NOT, NO, AVOID, NOT etc.). As such, while projecting, matching and / or factorizing capabilities / interests the system may factorize the constraints (which may be or not included / comprised / linked / grouped with a (published) capability / interest). Alternatively, or in addition, the system may (project) factorize the constraints with and / or without the capabilities / interests.

[0675] The publishing may comprise and / or entail access control (e.g. to allow / block the publishing of a capability / (in / out)interest from / within an endpoint and / or link and / or (only) for particular semantics and / or semantic identities); further, the publishing may be associated with an oriented link and / or flux and thus, controlling the publishing from a first endpoint and / or flux to a second endpoint and / or flux. Further, the access control may entail applying an activation and / or enablement configuration to control the availability (within and / or outside an endpoint and / or link). In an example, an endpoint supervisor may configure (or indicate) the system to block / disable (projected) CT scan capabilities / interests at a first endpoint while allowing / enabling it at a second endpoint; thus, any (discovered, localized and / or inferred) CT scan capabilities or interests may not be discovered, published and / or matched at the first endpoint while at the second endpoint can. The block / disable (or similar) and / or allow / enable (or similar) may be based on an endpoint and / or further hierarchies (e.g. associated with supervisors, access control, compositional / composite (factorized) semantics etc.). In an example, Jane is factorized as a higher supervisor than John at a first endpoint and thus, the enablement by Jane of a tea pot capability / (in / out)interest "‘brew tea in 30 secs for 50 cents’’ may take precedence over John’s disablement of the same capability / (in / out)interest at the endpoint (and / or encompassing endpoints). However, if John is factorized as a higher supervisor than Jane at a second endpoint encompassing the first endpoint, then thecapability / (in / out [interest of '‘brew tea in / for 30 secs (for 50c / 50W (h))” is disabled within the second endpoint (but not within the first endpoint) as per John’s (and Jane’s) configuration.

[0676] Alternatively, or in addition, Jane is factorized as a higher supervisor than John at a first endpoint and thus, the publishing by Jane of a tea pot capability(in / out)interest "brew tea in 30 secs” may take precedence over John’s (publishing) blocking of the same capability / (in / out)interest at the endpoint (and / or encompassing endpoints). However, if John is factorized as a higher supervisor than Jane at a second endpoint encompassing the first endpoint, then the capability / (in / out)interest of “brew tea in 30 secs” may be invisible / unavailable (as published) within the second endpoint as per John's disable / blocking configuration. Alternatively, if John doesn’t disable / block the capability / (in / out)interest at the second endpoint, then the published capability / (in / out)interest may be visible / available within the second endpoint (and / or further outside the second endpoint if John publishes it further and / or Jane is delegated by John with the rights to publish). Alternatively, or in addition, John delegates Jane to supervise all the publishing / access control / enablement regarding “tea” (or teapot, brewing etc.) and thus, Jane’s publishing / access control / enablement at the first point may be further published at the second endpoint (by Jane).

[0677] Alternatively, or in addition, Jane is delegated as a (publishing) supervisor and / or owner for tea pots (brewing) (capabilities / interests) within particular endpoints and / or all endpoints. It is to be understood that the access control rules may comprise and / or be combined to with item ownership and / or supervision. Further, publishing may comprise and / or be combined with supervising hierarchies, access control and / or further factorization.

[0678] It is to be observed that the enablement and / or access control may be based on encompassing semantics and / or further more localized associated semantics (e g. “tea” encompasses more localized “tea brewing” etc.).

[0679] The enablement / disablement and / or allowed / blocked may be (hierarchically) intrinsic. In an example, if John disables / blocks “tea pot” capability / (in / out)interest at the second endpoint (as a second endpoint supervisor), then the first endpoint intrinsic status for the “tea pot” capability / (in / out)interest is disabled / blocked unless is enabled / allo wed by Jane (as a first endpoint supervisor).

[0680] The matching, access control and / or publishing (of activities, capabilities, interests and / or further semantics) may be multilingual. As such, artifacts in one language are matched against artifacts in another language. In an example, the brew tea capabilit / (in / out)interest which may be published in English may be matched against aninterest in another language (e.g. French, German, Spanish etc.). In addition, the availability of a semantic in a first language may be controlled by matching it with access control, publishing and / or enablement specified in other languages than the first.

[0681] Capabilities and / or interests may be access controlled (e.g. to control matching); thus, only particular semantics and / or semantic identities may have access to capabilities and / or interests. In examples, Jane publishes '‘brew tea in 30 secs for / at 50c / 50W (h)” to be accessible and / or available to a "person possessing and / or carrying Health Affairs”. As previously exemplified, John may control and / or override within his endpoint the accessibility, publishing and / or diffusion to / of the capability; the control and / or override may entail enable / disable / allow / deny and / or specifying more localized access control, diffusion and / or publishing encompassing more localized semantic identities (e.g. “a nurse earn ing Health Affairs”, “a nurse reading Health Affairs” etc.). It is to be observed that an interest associated with such a capability may index a goodwill and / or budget based on (projected) endpoint semantics and / or (semantic) time; as such, the 50c / 50W (h) budget may be indexed based on (semantic) time (e.g. 30 sec, MEETING JANE + 30secs etc.)

[0682] Semantic times may be specified, organized and / or published in a hierarchical manner. In some examples, the (semantics associated / identifying with) encompassed semantic times are associated with a more specific localized and / or lower drift semantics (e.g. associated with semantic identities, objects, artifacts, assets, agents, themes etc.) than the (semantics associated / identifying with) encompassing semantic times. Further, they may be published, accessed and / or inferred based on the semantic hierarchy of semantic groups and / or supervisory / ownership hierarchies.

[0683] Goal based inferences allow the system to determine semantic routes, trails and / or budgets.

[0684] Semantic routes are used for guiding the inference in a particular way. In an example, a user specifies its own beliefs via language / symbology and the system represents those in the semantic model (e.g. using semantic routes, semantic groups etc.).

[0685] The semantic inference based on semantic routes may be predictable and / or speculative in nature. The predictability may occur when the semantic routes follow closely the semantic trails (portions of the history of semantics inferred by the system). Alternatively, the system may choose to be more pioneering to inferences as they occur and follow semantic trails less closely. In an example, a vehicle / carrier may follow' a predictive semantic route when inferring “ENGINE FAILURE” while may follow a more adaptivesemantic route when inferring “ROLLING DANGER”. The predictability and / or adaptivity may be influenced by particular semantic budgets and / or factors.

[0686] Such budgets and / or factors may determine time management and / or indexing rules. In some examples, the system infers / 1 earns a semantic time rule and / or indexing factor based on low inferred predictability factor wherein the inference on a semantic artifact is delayed until the predictability increases.

[0687] Further, the system identifies threats comprising high risk artifacts in rapport to a goal. The system may increase speculation and / or superposition in order to perform inference on goals such as reducing threats, inconsistencies, confusion and / or their risk thereof; in case that the goals are not achieved (e.g. factors not in range) and / or confusion is increasing the system may increase dissatisfaction, concern and / or stress factors. The system may factorize dissatisfaction, stress and / or concern factors based on the rewards factors associated with the goal and the threat / inconsistency risk factors. It is to be understood that such factors and / or rules may be particular to semantic profiles and / or semantic views. In some examples the threats and / or inconsistencies are inferred based on (risk) semantic factors (e.g. risk of being rejected, risk of not finding an article (at a location) etc.).

[0688] When the sy stem follows more predictable routes and the projections do not match evidential inference the system may infer and / or factorize dissatisfaction, concern and / or stress factors based on semantic shifts and / or drifts.

[0689] Dissatisfaction, concern and / or stress factors may be used to infer semantic biases and / or semantic spread (indexing) factors and, further, the system may infer semantic (modality) augmentation in order to reduce such dissatisfaction, concern and / or stress factors. It is to be understood that the augmentation may be provided and / or be related with any device based on circumstantial inference and / or semantic profiles. In an example, a detected sound (e.g. from a sound modality) is too loud, repetitive and / or unusual pitch which indexes the concern and / or stress factors and further detennines the adjustment, composition / smoothing and / or cancelation of the sound; further, tactile (modalities) actuators may be inferred to be used to alter and / or divert the inference on the sound receptor trails to tactile trails and to further increase the semantic spread and thus potentially reducing the concern and / or stress factors. It is to be understood that the system may monitor the dissatisfaction, concern and / or stress factors correlated with the augmentation artifacts applied to reduce them and further perform semantic learning based on correlation.

[0690] The system may infer, adjust and / or factorize likeability', preference, satisfaction, trust, leisure and / or (other) affirmative indicators / factors / artifacts based on high(entanglement) entropy inference in rapport with (higher) dissatisfaction, concern, stress and / or (other) non-affirmative indicators / factors / artifacts and vice-versa.

[0691] Confusion may decrease as more semantic routes / trails and / or rules are available and / or are used by the system.

[0692] Confusion thresholds may shape semantic learning. Thus, lower confusion thresholds may determine higher factorizations for a smaller number of routes / trails and / or rules associated to (past and / or future) (projected) inferences. Higher confusion thresholds may determine lower factorizations for a larger number of routes / trails and / or rules associated to (past and / or future) (projected) inferences.

[0693] As the system comprises more semantic routes / trails and / or rules with similar factorizations (e.g. no strong leadership artifacts) the superposition may increase as the evidence inference comprises more semantic spread.

[0694] For lower confusion thresholds the assessment of evidence (e.g. truth artifacts (provided) in the semantic field and / or flux) may be more difficult as the existing highly factorized artifacts are fewer and they may shape fewer highly factorized inferences with less semantic spread and decreased superposition.

[0695] Dissatisfaction, concern and / or stress factors may increase if higher factorized semantic artifacts in the inferred (projected) circumstances do not match evidence and / or evidence inference leads to confusion.

[0696] Dissatisfaction, concern and / or stress factors may be used to index and / or alter factorizations of the semantic artifacts used in evidence inference, in order to decrease such factors in future inferences, based on evidence inference and / or challenges (e.g. flux, user etc.).

[0697] The system may infer goals such as maintaining and / or gaining leadership which might signify involvement and / or importance in (group) decision making and further factorizations of dissatisfaction, concern and / or stress factors.

[0698] Increase in dissatisfaction, concern and / or stress factors may signify that the (group) pursued goals where not optimal. Further, such inferences may determine adjustments of routes, rules and / or further artifacts including factorizations of leadership, groups and / or semantic fluxes.

[0699] Predictability and / or speculative factors inferences may be associated with factors related to dissatisfaction, concern and / or stress factors (e.g. they may alter semantic spread). Further, authoritative rules may affect such factors as they may determine high consequential risk and / or fear factors.

[0700] The semantic route may be represented as a semantic artifact (e.g. semantic, semantic group) and participate in semantic analysis and semantic modeling.

[0701] Semantic route collapse occurs when during an inference the semantic engine determines (through generalization and / or composition for example) that a semantic route can be represented in a particular or general context through a far more limited number of semantics that the route contains. With the collapse, the system may create a new semantic route, it may update the initial semantic route, it may associate a single semantic associated with the original semantic route. In certain conditions the system may inactivate and / or dispose of the collapsed semantic route if the system infers that are no further use of the semantic route (e.g. through semantic time management and / or expiration). The semantics that may result from a route collapse may be compositional in nature. Additionally, the semantic engine may update the semantic rules including the semantic factors and as such it loosens (e.g. decaying) up some relationships and strengthen (e.g. factorizing) others.

[0702] The system creates and / or updates semantic groups based on semantic route collapse. Further, the system may collapse the semantic model artifacts (e.g. endpoints and / or links associated with the semantic route to a lesser number and / or to higher level artifacts).

[0703] Semantic route collapse may determine semantic wave collapse (e.g. low modulated semantic wave) and vice-versa.

[0704] Semantic wave collapse may depend on the frequency of electromagnetic radiation received by semantic systems, components, endpoints and / or objects. In an example, composition and collapse doesn’t happen unless the electromagnetic radiation frequency reaches a threshold which further allows (the semantic unit, object’s semantic wave) the gating / outputting of semantics. In some examples the threshold frequency is associated with the minimum electromagnetic frequency generating photoelectrons emissions (e.g. by photoelectric effect). It is understood that by tuning the composite, absorptive, dispersive, diffusive and / or semantic artifacts of (nano) meshes the threshold frequency at a location may be tuned and thus allowing fast hyperspectral semantic sensing.

[0705] The system builds up the semantic routes while learning either implicitly or explicitly from an external system (e g. a user, a semantic flux / stream). The build-up may comprise inferring and determining semantic factors. The semantic routes may be used by the semantic system to estimate semantic budgets and / or semantic factors. The estimate may be also based on semantics and be associated with weights, ratings, rewards and other semantic factors.

[0706] The semantics that are part of the semantic route may have semantic factors associated with it; sometimes the semantic factors are established when the semantic route is retrieved in a semantic view frame; as such, the factors are adjusted based on the context (e.g. semantic view frame factor). While the system follows one or more semantic routes it computes semantic factors for the drive and / or inferred semantics. If the factors are not meeting a certain criterion (e.g. threshold / interval) then the system may infer new semantics, adjusts the semantic route, semantic factors, semantic rules and any other semantic artifacts.

[0707] Sometimes the system brings the semantic route in a semantic view frame and uses semantic inference to compare the semantic field view and the semantic view frame. The system may use semantic route view frames to perform what if inferences, pioneer, speculate, project and optimize inferences in the semantic view. At any given time, a plurality of routes can be used to perform semantic inference and the system may compose inferences of the plurality of routes, based on semantic analysis, factors, budgets and so on. The analysis may comprise semantic fusion from several semantic route view frames. Sometimes the semantic route does not resemble the expected, goal or trail semantics and as such the system updates the semantic routes and trails, potentially collapsing them, and / or associate them with new inferred semantics; additionally, the system may update the semantic factors, update semantic groups of applicable semantic routes and any other combinations of these factors and / or other semantic techniques.

[0708] The system learning takes in consideration the factorization of semantic rules and / or routes; thus, the learned semantic artifacts may be associated with such rules and factors (e.g. “DRIVE IN A TREE” has a high risk and / or fear factor etc.). In some cases such semantic artifacts are compared and / or associated with the hard semantic routes and / or artifacts; the inferred semantic artifacts may be discarded instead of learned if they make little sense (e.g. prove to be incoherent and / or highly factorized in relation with particular stable, factorized, high factorized semantic trails / routes, semantic drift too high etc.).

[0709] In further examples, the system receives and / or infers a composite semantic comprising a potential semantic goal and an associated entangled (consequence) semantics (e.g. having high / low undesirability / desirability factors) for pursuing / not-pursuing and / or meeting / non-meeting the goal (e.g. JUMP THE FENCE OR GO BUST. JUMP THE FENCE AND GO TO EDEN, JUMP THE FENCE AND GO TO EDEN OR GO BUST); further, the entangled semantic artifact may determine adjustment of the goals factors (e.g. risk, weight, desirability' etc.) and further projections. It is to be observed that in the example theentanglement entropy is high due to consequences having a high relative semantic entropy (in rapport with the goal and / or in rapport to each other, they are being quite different even opposite or antonyms). In further examples, the entangled consequence can be similar and / or identical with the goal (e.g. GO BUST OR GO BUST) and as such the entanglement entropy is low. It is to be understood that the entanglement entropy may be associated with the semantic factors inference (e.g. when the entanglement entropy is high the factors and / or indexing may be higher).

[0710] In the previous example, it is to be understood that EDEN may activate different leaderships based on semantic analysis and / or semantic profiles. For example, the previous inferences and / or profiles may have been related solely with EDEN a town in New York state and hence the semantic route associated with EDEN, TOWN, New York may have a higher semantic leadership than EDEN, GARDEN, GODS. However, for particular semantic profiles the EDEN, GODS may bear a higher semantic leadership than EDEN, TOWN. As mentioned before where there is a confusion factor the confused system may challenge the user and / or other fluxes (e g. such those initiating / challenging the goal of JUMP THE FENCE and / or consequences) for additional information (e.g. which EDEN?).

[0711] When the confusion is high the system may decay and / or invalidate the semantic artifacts (e.g. routes . rules etc.) which generated confusion. Alternatively, or in addition, the system may factorize such artifacts in rapport with a a / the collaborator and / or (associated) semantic views.

[0712] The leadership semantics may be based on inferences and / or semantics associated with endpoints, links, locations, semantic groups and / or further semantic artifacts associated with the subject (e.g. challenger, challenged, collaborator, user, operator, driver etc.).

[0713] Semantic drift shift and / or orientation may be assessed based on semantic entropy and / or entanglement entropy. Analogously, semantic entropy and / or entanglement entropy may be based on semantic drift, shift and / or orientation.

[0714] During a semantic collapse the system may assess whether the collapsible semantic is disposable possible based on semantic factors and decaying; if it is, the system just disposes of it. In the case of semantic wave collapse it may reject, filter or gate noisy and / or unmodulated wave signal.

[0715] Sometimes the disposal is deferred based on semantic time management.

[0716] The system continuously adjusts the semantic factors and based on the factors adjusts the routes, the semantic rules, semantic view frames and so on. If the factorsdecay (e.g. completely or through a threshold, interval and / or reference value) the system may inactivate, invalidate and / or dispose of those artifacts.

[0717] In further examples, new semantic artifacts may be associated with highly factorized routes based on the activity associated with the route and thus the new semantic artifact may be also highly factorized and / or retained longer (e.g. in semantic memory). Analogously, a highly factorized semantic artifact when associated with a semantic route determines the higher factorization and / or longer retainment of the semantic group.

[0718] Semantics are linguistic terms and expression descriptive and indicative of meanings of activities on subjects, artifacts, group relationships, inputs, outputs and sensing. The representation of the semantics in the computer system is based on the language of meaning representation (e.g. English) which can be traced to semantics, semantic relationships, and semantic rules. Sometimes, when the system understands more than a language and symbology, the relationship between the languages is represented through semantic artifacts wherein the second language components are linked (e.g. via a first language component into a semantic group) with the first language; sometimes, the system choses to have duplicated artifacts for each language for optimization (e.g. both languages are used often and the semantic factors for both languages are high) and model artifacts are linked and / or duplicated.

[0719] In an example, the system has a semantic group of associated to VEHICLE / CARRIER comprising GERMAN AUTO, SPANISH COCHE, FRENCH VOITURE. When performing translation from the language of the meaning representation to GERMAN the system uses the GERMAN as a leadership semantic and thus, the system performs German language narrative while inferencing mostly in the language of meaning representation (e.g. English). However, the system may optimize the GERMAN narrative and inference by having, learning and Reorganizing the particular language (e.g. GERMAN) semantic waves, semantic artifacts, models and / or rules as well so that it can inference mostly in German as another language of meaning representation (e.g. besides English). It is to be understood that the system may switch from time to time between the language drive semantics in order to inference on structures that lack in one representation but are present in another and thus achieving multi-lingual, multi-custom, multi-domain and multi-hierarchy inference coverage. The system may infer and / or use multi-language and / or multi -cultural capabilities of collaborative fluxes (e.g. monocultural, multicultural) and / or associated factors.

[0720] The system may maintain particular semantic artifacts for particular contexts. In an example, semantic artifacts associated with a drive semantic of BEST FRIENDSFROM SCHOOL may have associated slang and / or particular rules and artifacts that drive semantic inference and narrative in a particular way.

[0721] The semantics may be associated with patterns, waveforms, chirps.

[0722] The semantics may be associated with parameters, inputs, outputs and other signals.

[0723] In an example semantics are associated with a parameter identifier (e g. name) and further with its values and intervals, potentially via a semantic group.

[0724] The semantic factors may quantify and / or represent quantitative indicators associated to semantics.

[0725] The semantic system may use caching techniques using at least one view frame region and / or structure to store semantics. In semantic expiration, the semantics may expire once the system infers other semantics; that might happen due generalization, abstraction, cross domain inference, particularization, invalidation, superseding, conclusion, time elapse or any other process that is represented in the semantic model. Processes like these are implemented through the interpretation of the semantic model and semantic rules by the semantic engine and further semantic analysis. The semantic inference may use semantic linguistic relations including semantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy, polysemy.

[0726] Semantic techniques and interdependencies may be modeled within the inference models and semantic rules. In some examples polysemy is modeled via semantic composition where the meaning of a poly seme is inferred based on the compositional chain. Further, semantic groups, semantic rules and semantic models may be used to represent semantic dependencies and techniques.

[0727] Semantic techniques may be implemented via semantic models including semantic attributes and semantic groups. In an example, a semantic group containing all the synonyms for "great" is stored. In some cases, the group comprises semantic factors assigned to semantic components to express the similarity within a group or with the semantic attributes defining the group.

[0728] In both semantic flux and semantic streams, the source of information may be assigned semantic factors (e.g. associated with risk) and as such the inference by a system that consume semantic information from the source may be influenced by those factors. More so, the factors can also be assigned to particular semantics, type of semantics (e.g. via semantic attributes), themes and so forth that can be found in the fluxes and streams. Semantic fluxes and streams may be represented as identifiers and / or semantics (e.g. based on annotatingthem in particular or in general based on a characteristic by a user) and / or be organized in semantic groups as all the other artifacts.

[0729] The system may use semantic time management (e.g. rules, plans etc.) to manage the semantic factors for the semantic fluxes and streams.

[0730] It is therefore important that the information from various semantic sources including fluxes, streams, internal, external be fused in a way to provide semantic inference based on the model at hand.

[0731] It is desirable that systems be easily integrated in order to collaborate and achieve larger capabilities than just one system. The advantage of semantic systems is that the meanings of one system behavior can be explained to a second collaborative system through semantic means. As such, if for example system A provides and interface and is coupled to system B through some means of communication then the semantic coupling may consist in making system A operational and explaining to system B what the meaning of the inputs / outputs from system A in various instances is. The system B may use sensing and semantic inference to infer the meaning of the received signal from system A. Alternatively, or in addition the system A and B can have one common semantic point where the systems can explain to each other what the meaning of a certain input / output connection mean at some point. For example, if system A and system B are coupled through a common semantic point and also have other signaling and data exchange interfaces between them then when a signal is sent from A to B on an interface, the common semantic point from A to B will explain the meaning of the signal from A to B. In some cases, the systems A and B are coupled through a semantic stream wherein the common semantic point comprises the semantic flux. As such, the system B may use its own inference model to learn from the ingested data from system A; further, the system B may send his interpretation (e g. via model) back to A; the system B may just use the semantic meaning provided by system A for interpreting that input / output signal / data or use it for processing its own semantic meaning based on semantic inference, processing and learning techniques. In other instances, the system B will ask / challenge the system A about what the meaning of a signal is. In some cases, the semantic fluxes that connect A to B make sure that the semantics are requested on system B from system A when their validity expire. The system B may be proactive in sending those requests and the system A may memorize those requests in semantic routes groups and / or views and process them at the required time. The system may use the semantic budgets for transmission through the semantic network and the semantics may expire in the network once budget is consumed.

[0732] In further examples, semantic group resonance may be applied for faster learning (e.g. of semantic groups and / or leadership), safety, communication and / or further inferencing.

[0733] In semantic group resonance, system A induces coherent inferences at B (e.g. affirmative toward the goals of B): further, system B induces coherent inferences at A (e.g. affirmative towards the goals of A). Thus, semantic group resonance allows (continuous) coherent inferences with potential low / high (entanglement) entropy of A and B while increasing superposition. Semantic group resonance with low (entanglement) entropy is associated with affirmative factors; analogously, semantic group resonance with high (entanglement) entropy is associated with non-affirmative factors. Semantic group resonance factors may be quantified in an example through low confusion, dissatisfaction, concern and / or stress factors between the members of the group and it may collapse when decoherence (e.g. high incoherence, confusion, dissatisfaction, concern and / or stress between the members of the group) occurs.

[0734] Semantic groups resonance determines and / or is associated with low confusion, dissatisfaction, concern and / or stress factors.

[0735] In semantic systems the semantic time between resonance and decoherence may be used to infer coherent artifacts and / or operating points / intervals. The system may leam causality (e.g. of resonance, decoherence) comprising semantic routes / trails, rules and / or other semantic artifacts. In some examples the system infers DO / ALLOW rules and / or further rules (e.g. time management / factorization / indexing etc.) when affirmative resonance occurs, and / or DO NOT / BLOCK rules and / or further rules when affirmative decoherence occurs. Analogously, the system infers DO NOT / BLOCK rules and / or further rules (e.g. time management / factorization / indexing etc.) when non-affirmative resonance occurs, and / or DO / ALLOW rules and / or further rules when non-affirmative decoherence occurs. Further, damping may be learned by the system; as such, indexing and / or decaying factors and further rules may be learned based on resonance and / or decoherence (factors) and be associated with damping semantic artifacts.

[0736] In some examples, the system learns damping factors and / or rules within the semantic mesh associated with the absorption and scattering of electromagnetic radiation in elements and / or (semantic) group of elements.

[0737] Damping rules and artifacts are used to infer hysteresis and vice versa. They may be used for adjusting factors, budgets and or quanta in order to control the damping towards goals and / or keep (goal) semantic inference within a semantic interval. Damping rulesmay be used for example to control the damping components (e.g. of shocks, electromechanical dampers etc.) of a drivetrain (e.g. of posts, vehicles etc.).

[0738] In some examples, system A uses semantic artifacts associated with system B (e.g. (portions of) semantic trails, routes, rules, drives, goals and / or orientations etc.) to induce coherent and / or resonant inferences at B and / or reduce confusion at B; this pattern may associate A as a (group) leader.

[0739] Semantic resonance is high for coherent semantic groups (e.g. the resonant inference in the group does not incoherently collapse). Semantic resonance is low for incoherent semantic groups and / or low coherency semantic groups. The system may infer highly coherent composite goals for coherent semantic groups. The system may use projected resonance on (target) artifacts (e.g. flux, user, patient etc.) and / or groups thereof in order to diffuse, attract, group, increase positiveness and / or to decrease dissatisfaction, concern, stress etc.

[0740] Projected resonance between (high entanglement entropy) semantic groups may be used to leam damping, hysteresis and / or further rules.

[0741] Model and sub-model distribution / exchange may occur between system A and B. This exchange may be controlled (e.g. allowed, blocked, blurred and / or diffused) via semantic access control and gating. In an example particular semantics and / or associated semantic artifacts are blocked. In another example, semantic groups related to MRI EXAMS may be blurred; while the system may blur the entity / object groups (e.g. patients, images, patient-images etc.), other semantic groups (e.g. related with language interpretation) may be allowed to pass; alternatively, or in addition, the system may use semantic diffusion in order to convey information in a controlled fashion. In other example the semantic gating is based on semantic budgeting inference and / or speculative inference. Thus, a semantic flux B might expose to flux A the semantics (e.g. potentially marked semantics) and the semantic capabilities potentially with estimated budgets and the flux A performs semantic inference on gated semantics and flux B exposed semantics. If the semantic inference doesn’t meet required budgets, then the system A may choose to filter or reroute the semantics that do not meet the requirements. Entity and language filtering and semantic gating may be combined in any way to allow / deny transfer of information between systems.

[0742] In general, two communicating systems may use explanatory protocols and / or interfaces; as such, a memory conveyed through a first mean is explained and / or reinforced through another mean.

[0743] The system B may maintain semantics from A and the system keeps semantic factors associated with them that may decay in time. Sometimes, the system B sends the requests to system A when the factors decay, reach a specific threshold and / or based on semantic budgets.

[0744] In many computer systems data is exchanged via objects, sometimes represented in JSON or other obj ect streaming formats. The exchanged data is interpreted based on a static interpretation of JSON object properties or based on JSON schema parsing.

[0745] The interfaces may be statically coupled, and the operations and / or functions established a-priori and / or they may be encoded / explained in a dynamic way in the JSON objects (e.g. one field explains another through semantic means such as semantic augmentation, synonym and / or antonym. These interfaces are not very adaptive due to semirigid implementation of the coupling between the systems.

[0746] An adaptive approach of communication learning may involve a system B learning at first from a system A about the data is conveying and updating its semantic model in order to be able to infer semantics based on that data. In some examples, the system B leams a new language based on learning interfaces. In such an example, the learning interface relies on common system A and B observations (e.g. sensing, semantic wave) and potentially basic rules and models for inference learning.

[0747] The implementation of interface learning may be achieved via a semantic point where the interface is described via a language or semantic wave. Alternatively, or additionally the semantics of the interface and the relationships can be modeled via a tool that will generate a semantic plug-in model for the interpretation of the interface inputs. The semantic tool and / or plug-in allows the description of the interface based on semantic rules including management rules. The plug-in model may then be deployed to the connected systems and the connected systems use it for semantic connection. The plug-in model may be deployed as part of a separate block circuit and / or semantic unit that connects the systems. Alternatively, or in addition, the plugin may be deployed in a memory (e.g. flash. ROM, RAM etc.). Further, the plugin modules may comprise encryption capabilities and units whether semantic or not. In some examples the plugin modules are used to encrypt and / or modulate semantic waves. The encryption and / or modulation can be pursued in any order using semantic analysis techniques.

[0748] The semantic connection (e.g. semantic flux) may be controlled through a semantic gate that allow controlled ingestion or output of information, data and / or signals through semantic fluxes and / or semantic streams.

[0749] In Fig. 16 and 20 we multiple elements (e.g. semantic units) coupled through links / semantic fluxes. As illustrated in Fig. 16, a plurality of elements (semantic units) are labeled with letters A through W. Each of the elements may comprise computing and / or memory components. Fig 16 further depicts semantic groups of elements in a hierarchical structure (e.g. Group 1: 1 (which is defined by the perimeter formed by G-H-I-J-K-L), 1:2 (formed by elements A-B-C-D-E-F), 1:3 (formed by elements M-N-P-O), 1 :4 (formed by N- V-W-0) at level 1; Group 2: 1 (formed by N-V-U-T-S-R-Q-O, further indicted by thicker connecting perimeter line), 2:2 (indicated by thicker connection line joining A-F-G-H-I-J) at level 2); it is to be understood that while only two hierarchical levels are depicted, more levels may be present.

[0750] In some examples semantic fluxes and / or semantic streams are ingested by systems and possibly interpreted and / or routed based on semantic analysis. Fig. 20 illustrates one example, and as discussed further below a plurality of semantic units may be arranged such as semantic units SU1 through SU9. One or more external signals, e.g. 68a, 68b may be received by one or more of the semantic units. The semantic units are linked to one another in a mesh through semantic flux links, e.g., LI through LI 9.

[0751] The semantic gate may filter the semantics in exchanges. The semantic gate may be controlled and / or represented by a set of access control, time management, rating, weighting, reward and other factor rules collectively named semantic management rules; access control, time management, rating, weighting and reward rules are comprised in patent publication number 20140375430. As such, the semantic gate may allow adaptive control of the exchange of information anywhere between a very fixed controlled environment and a highly dynamic adaptive environment. The semantic gate may contain rules that block, allow or control the ingestion of particular semantic artifacts based on access control rules. The endpoints of a semantic flux (e.g. source and destination) may be represented in a hierarchical semantic network graph and the semantic flux being associated with links in the graph. The source and destination may be associated with semantics and the semantic gate control rules are specified based on these semantics; in an example, such semantics are associated with activities and / or locations and they may be collaboratively or non-collaboratively semantically inferred. Such semantics may be assigned to various artifacts manually, through semantic inference, through authentication or a combination of the former.

[0752] We mentioned the use of hierarchical semantic network graphs for meaning representation. The semantic gate may be used to control the information flow between any of the elements of the graph and / or between hierarchies. The graph elements andhierarchies are associated with semantics and as such the semantic gate controls the semantic flow based on such semantics.

[0753] In an example, the access between hierarchies is based on access control rules; as explained above the hierarchies may be associated with semantics and / or be identified by semantics. Further, access control rules may be associated with semantic identities and / or further identification and authentication techniques. In some examples, the identification and authentication are based on semantic analysis and / or sensing comprising data ingestion, image / rendering / display capture, radio frequency, electromagnetic modalities and / or other modalities / techniques.

[0754] Information flows and / or (agent) diffusion within and / or between semantic network model artifacts are controlled based on semantic gating. In some examples, information transfer flow between linked endpoints mapped to display interface areas, semantic groups and / or user interface controls is enforced this way. In further examples, the gating is coupled and / or based on the hierarchical inference within the semantic network model and / or semantic views which provide contextual localization pattern, access control and semantic intelligence pattern of the mapped areas, semantic groups and / or user interface controls. The mapped areas may comprise for example displayed text, user interface artifacts, controls, shapes, objects and / or a combination thereof; also, they may comprise and / or be associated semantic groups, semantic identities and / or patterns of displayed text, user interface controls, shapes, objects and / or a combination thereof. Thus, the system may create groups, use fluxes and / or allow the flow and / or assignment of information from one mapped artifact to the other only if the semantic gating would allow it. In further examples, the system performs projected compositional semantic analysis on the semantics assigned to the linked artifacts and based on the projected analysis perform the semantic gating.

[0755] Linked semantic artifacts may be inferred based on semantic analysis. In an example the system infers the purpose and / or goal of artifacts and / or semantic groups in at least one semantic identified area (e.g. window) and may link such artifacts based on similarity of purpose, goal and / or further inference. It is to be understood that the linked artifacts may be inferred and / or mapped by selecting, dragging and / or overlaying the semantic areas and / or mapped artifacts on top of each other via any I / O (e.g. touch interface, screen, pointing device etc.); further, in some examples the system provides feedback on such operations (e.g. deny the operation, inform the user, pop up an image control and so on). In further examples, semantic groups of artifacts are created by selecting, dragging and / or overlaying the semantic areas and / or mapped artifacts on top of each other and the user isprompted with selecting and / or confinning the (composite) semantic artifacts (e.g. semantics, semantic gating rules, semantic routes, profiles and / or further artifacts) for such semantic groups (e.g. between the group members or with group external artifacts).

[0756] Alternatively, or in addition, the system projects and / or determines whether the positioning and / or rendering of semantic artifacts comply with the rules, routes and / or that further (composable) inferences are affirmative and / or likeable.

[0757] A received input may not be ingested or partially ingested if the semantic engine infers a semantic that is forbidden by the semantic gate. A partial semantic determination occurs when some of the semantics are partially inferred on a partial analysis of a semantic route, goal and / or budget; sometimes those semantics are discarded and / or invalidated. However, other times those semantics may not be discarded or invalidated; instead they may be assigned a factor and / or time of expiration or a combination of those. Such partial inference may be useful for example in transfer inference and learning. In some examples semantic trails and / or routes associated with semantics in a domain may be partially applied and / or associated to semantic artifacts in other domains based on higher hierarchy inference on the semantic model.

[0758] Decaying and semantic expiration may be used for controlling a semantic gate. The semantic analysis may be used to update the semantic factors and time management and update the dynamic of semantic gates.

[0759] The semantic gates may be plugged in to the semantic analysis and / or utilize semantic network models where endpoints represent the source (or a source group) and destination (or a destination group) of semantic fluxes. Source groups and destination groups are represented as semantic groups.

[0760] A semantic group consists of at least two entities each being monitored in the semantic field that share a semantic relation or commonality7via a semantic (e.g. semantic attribute). A semantic group can be semantic dependent when a semantic attribute is assigned to specify a dependency or causality relationship within the group (e.g. A INFECTED B, JOHN PERFORMED MRI EXAM) or, semantic independent when there is no apparent relationship between the obj ects other than a classification or a class (e.g. A and B are INFECTED systems). In further examples, A, B, MRI EXAM may be on their own assigned to semantic groups, for example for storing signatures of viruses, images from MRI-EXAM etc.

[0761] It is to be understood that the causality relationships and learning may depend on the semantic view and semantic view frames; further, they may depend on semantic field orientation and / or anchoring. In an example, the observer's A semantic view sees theeffect of the sensor blinding on B as a result of a laser or photon injection at a later time than the system’s B semantic views detects such blinding effect. The inference time and / or propagation (and / or diffusion) may be circumstantial at / between A and B, and thus, while the order of those collapsed inferences may be more difficult to project, they may be considered as entangled from particular semantic views (e.g. of an observer C). Further, systems’ projected inferences in regard to action / command / obsen ations might comprise a high degree of certainty in relation with semantic artifacts which may be used as anchors for semantic orientation. For observer's A semantic view, the cause of the attack was that sy stem B is a “slacker flimsy protected” while for system's B semantic view the cause of the attack was because “A is a bully”. Thus, causality relationship may comprise additional information at a (hierarchical) level associated with the two entities (e.g. a link from A to B “sent malware because it is a slacker” and a link from B to A “this is a bully who’s probing me”, “this is a bully who infected me” etc.). While at a different level and / or semantic view, of A, B and / or a third observer C, the causality specifies the cause effect of A INFECTED B; it is to be understood that this higher causality may be comprised, inferred, acknowledged and / or represented only for particular views and / or observers (e.g. B might not acknowledge or infer that it has been infected by A probing). It is to be understood that the cause-effect relationship (e.g. infected “because” is a bully) may be modeled as oriented links and used to explain “why” type questions (e.g. why A infected B ? - because A is a 80% bully and B is a 70% little 20% flimsy slacker; why is A bully ? - because it infected B and C and D and I 100% think is wrong). In further examples, the propagation and / or diffusion between a first and a second endpoint is based on assessing the semantic drift and / or shift of / between the semantic artifacts associated with the endpoints; thus, the system may infer propagation and / or diffusion semantic rules (e.g. time management, access control, indexing, factoring etc.).

[0762] It is to be observed that the explanatory' type inferences (e.g. why, how etc.) may be based on particular semantic views (e.g. of A and / or B); further, the system may determine the particular (high entropy) (leadership) semantic trails and / or routes which are relevant to explain and / or respond to the explanatory type inferences; further, the system may show and / or render side by side explanations comprising (profile) configured / inferred semantics, semantic identities and / or (associated) UI controls. Alternatively, or in addition, the system may highlight, show and / or render (side by side) high entropy (leadership) semantic artifacts which are relevant to explain how (high entropy) (factorization) inferences have occurred and / or to highlight the high entropy (and / or differences in) factorizations (inferences) between semantic views.

[0763] Semantic anchoring allows the system to determine a baseline for inference (e.g. an observed object, high factorized artifacts, semantic groups, semantic identities, themes of interest etc.). The anchoring may be based on a collection of artifacts and the system uses projected inference and semantic analysis based on such anchors. Further, the anchoring semantic artifacts may be determined by mapping and / or overlaying a semantic network sub-model, layer, shape, and / or template to a semantic network model (e.g. based on similar semantic based artifacts, artifacts with particular semantics -e.g. goal based, antonym, synonym, orientation based etc. - in both the base and the overlaid network model). The anchors may map and / or project into various hierarchies, semantic views and / or frames. Anchoring may expire based on semantic analysis; once the anchors expire the system may invalidate corresponding semantic views, frames and / or regions. Semantic anchors may be inferred based on leadership inference; further semantic diffusion and / or indexing may be used to expand or contract the anchors.

[0764] In examples, the system determines a plurality of (hierarchical) (endpoint) anchors based on semantic entropy / drift between inferred leadership semantics and the anchor semantics (attributes). Further, the system uses semantic routes, rules and / or diffusion at / from / to the (hierarchically) identified anchors to project and / or factorize (further) (leadership) semantics. In further examples, while determining the level of golf expertise for John the system may select anchors associated with GOLF (and / or further) -> PUTTING / PUTTER, GOLF (5-9) IRON, GOLF WEDGE etc.

[0765] Semantic anchoring, drifts and / or indexing may change based on the orientation and / or intensity of the gravitational field within and / or associated with the semantic field and / or endpoint. In further examples the semantic field is a higher hierarchical endpoint associated and / or comprising particular gravitational fields. Semantic drifts may be inferred and / or associated with gravitational fields / waves and / or vice-versa; further, they may be associated with semantic time management. Semantic anchoring may be indexed and / or change based on semantic drifts, semantic fields (and / or endpoints), gravitational fields and / or waves. In some examples the gravitational fields and / or waves are inferred using semantic sensing analysis.

[0766] In some examples the system represents the semantic groups in the semantic network model. In some example’s entities are stored as endpoints and relationships between entities are stored as links. The system may create, activate, block, invalidate, expire, delete endpoints and links in the semantic network model based on semantic analysis and semantic group inference.

[0767] The system may use specific hierarchical levels to represent semantic groups of specific and / or leader semantic artifacts.

[0768] During semantic inference the system may activate various hierarchical levels in the semantic network model based on semantic analysis, drive and leadership semantics.

[0769] A semantic gate may control the flux between sources and destinations. A semantic flux is an oriented flow which may be assigned to an oriented link.

[0770] A semantic gate and a semantic flux may be identified by at least one other semantic artifact (e.g. semantic).

[0771] Additionally, if the semantic gating detects or infers a semantic that is not allowed then the semantic gating may update the semantic model and management rules (e.g. collapse the semantic route and associate the collapsed semantic to a semantic rule). In an example, if the system interprets an input (e.g. semantic) from a particular flux as being questionable maybe because it doesn’t fit the semantic inference and / or theme of the semantic flux, the system may discard and reroute the semantic artifact, update / create a semantic rule (e.g. for source, factors); it also may infer additional semantics (e.g. associated with cyber securi ty features for example). In other examples the system asks for feedback from a user or from other semantic hierarchies, domains and / or themes; in some examples it may use further semantic analysis of the semantic before feedback request (e.g. synonymy, antonymy etc.). In an example, a semantic unit may ask a semantic flux cloud if a particular cyber physical entity is associated with HAZARD and / or, in other examples if the entity is associated with POISONED WATER. Thus, the system may search or provide inference on semantic areas, domains and / or groups associated with semantic routes of HAZARDOUS POISON WATER and / or POISON WATER and / or HAZARDOUS WATER and / or HAZARDOUS POISON and / or further combinations of the semantics in the semantic route.

[0772] At a hardware level the interface between various components can be achieved in in a semantic way. As such the connection points and / or signals transmitted between various components can be semantically analyzed and / or gated.

[0773] A semantic gate may be represented as a circuit or component. As such, the semantic gate controls the signals received and / or transmitted between semantic components. A semantic gate may allow only specific semantics / artifacts / themes / signals to pass through.

[0774] Semantic gating and flux signaling may be achieved by diffusive processes. Further quantum tunneling phenomena may be used.

[0775] A semantic cyber security component deployed on a hardware layout may be able to infer, identity, deter and block threats. Further, by being connected to a semantic flux infrastructure and / or cloud is able to challenge (or ask for feedback) on particular cyber physical systems, semantics, semantic groups etc. and perform access control based on such information. It is to be understood that instead of challenging or asking for feedback about a particular cyber-physical system alternatively, or in addition, it may ask for feedback about a semantic and / or semantic group associated with the cyber physical system.

[0776] In some examples the system may detect that the inferences related with at least one collaborator and / or semantic group determine incoherent superposition. Thus, the system may ask for feedback from other collaborators and / or semantic groups: the system may prefer feedback from entangled and / or conjugate collaborators and / or semantic groups (e.g. having particular entanglement entropies of composite semantic analysis). Further, the system may decay specific factors and / or semantics associated with the collaborators who determine, cause and / or infer incoherent superposition and / or high confusion.

[0777] Signal conditioning represents an important step in being able to eliminate noise and improve signal accuracy. As such, performing signal conditioning based on semantic analysis is of outmost importance in semantic systems.

[0778] The semantic conditioning means that semantics inferred based on received measurements and data including the waveforms, parameters, envelopes, values, components and / or units are processed and augmented by semantic analysis. Semantic signal conditioning uses semantic conditioning on unconditioned measurements and signals. Semantic signal conditioning also uses semantic conditioning to compose and / or gate conditioned and / or generated semantic waves and / or signals. Thus, the system is able to use semantic conditioning for a large variety of purposes including inference in a semantic mesh.

[0779] In an example, the system conditions a received signal based on a modulated semantic wave signal. The conditioning may take place in a semantic unit comprising a summing amplifier at the front end producing a composed and / or gated semantic wave signal. In an example, the composition and / or gating is performed by modulating the output signal (e.g. voltage) based on the input signals (e.g. unconditioned signals 64, conditioned and / or generated semantic wave signals 65) to be added (as depicted in Fig. 19 A B C). It is to be understood that the amplifier GAIN Rf 66, SU GAIN 67 may be also be adjusted based on semantic artifacts (e.g. semantics, semantic waves etc.) and / or be in itself a semantic unit (SU GAIN); adjustments of the gain may be used for access control and / or gating purposes in some examples wherein the output voltage may be adjusted to account for allowabletransitions and / or semantics. While an amplifier has been used in examples, it is to be understood that in other examples additional and / or alternative analog and / or digital voltage adders, operational amplifiers, differential amplifiers, analog blocks, digital blocks, filters and / or other components (e.g. as specified throughout this application) may be used. Also, while the depicted examples may show physical and / or logical electronic components and / or blocks including capacitors, resistor, amplifiers, inductors, transistors, diodes and other electronic parts / units / blocks, it is to be understood that they may not be present in other embodiments or they may be substituted with other components and / or parts / units / blocks with similar or different functionality. In an example, the capacitors C in Fig 19 might be missing altogether; further the amplifier A may be missing and thus, the front-end block might be...

Claims

1. I claim:

1. A device networking system, comprising: a first device comprising a first processor, a first memory and a first transceiver; a second device comprising a second processor, a second memory and a second transceiver; wherein the first device and the second device are arranged to communicate first connection information between the first device via the first transceiver and the second device via the second transceiver on a first physical communication interface of a first type when the first device is communicatively coupled with the second device on the first physical communication interface; wherein the first device is communicatively coupled to the second device via a second physical communication interface of a second type; wherein the first device and the second device are communicatively coupled to one another through a provider establishing a brokered communication link between the first device and the second device based on the first connection information received from the first device; wherein the brokered communication link comprises a first communication link between the provider and the first device and a second communication link between the provider and the second device on the second physical communication interface.

2. The device networking system of claim 1, wherein at least one among the first device and the second device comprises at least one sensor.

3. The device networking system of claim 1, wherein at least one among the first device or the second device is a mobile device.

4. The device networking system of claim 1, wherein at least one among the first device or the second device is a robotic device.

5. The device networking system of claim 1, wherein the first device and the second device are communicatively uncoupled based on a detected presence of the first device or the second device at an endpoint among a plurality of endpoints.

6. The device networking system of claim 1, wherein the first device and the second device are communicatively coupled based on a semantic interval.

7. The device networking system of claim 1, wherein the first device and the second device are communicatively uncoupled based on a semantic interval.

8. The device networking system of claim 1 , wherein the first device is comprised by an apparatus comprising a holder and the first device and the second device are communicatively coupled when the second device is secured in the holder.

9. The device networking system of claim 1 , wherein the first device is comprised by an apparatus comprising a holder and the first device and the second device are communicatively uncoupled when the second device is unsecured from the holder.

10. The device networking system of claim 1, wherein the brokered communication link comprises a plurality of semantic fluxes.

11. The device networking system of claim 10, wherein the brokered communication link is hierarchical based on a hierarchy of the semantic fluxes.

12. The device networking system of claim 1, wherein the brokered communication link is hierarchical comprising a first brokered communication link of a first communication protocol at a first hierarchical level and a second brokered communication link of a second communication protocol at a second hierarchical level.

13. The device networking system of claim 12, wherein the first connection information comprises connection information associated with the first communication protocol and connection information associated with the second communication protocol and wherein the first brokered communication link is established based on the connection information associated with the first communication protocol and the second brokered communication link is established based on the connection information associated with the second communication protocol.

14. A device networking system, comprising: a first device comprising a first processor, a first memory and a first transceiver; a second device comprising a second processor, a second memory and a second transceiver; wherein the first device and the second device are arranged to communicate first connection information between the first device and the second device on a first physical communication interface of a first type when or after the first device is communicatively coupled with the second device on the first physical interface; and wherein the first device is communicatively coupled on a second physical communication interface of a second type with the second device; wherein the first device and the second device are communicatively coupled through a provider establishing a brokered network connection between the first device and thesecond device based on the first connection information received from at least one among the first device and the second device; wherein the brokered network connection comprises a first network connection between the provider and the first device on the second physical communication interface and a second network connection between the provider and the second device.

15. The device networking system of claim 14, wherein at least one among the first device and the second device comprises at least one sensor.

16. The device networking system of claim 14, wherein at least one among the first device or the second device is a mobile device.

17. The device networking system of claim 14, wherein at least one among the first device or the second device is a robotic device.

18. The device networking system of claim 14, wherein the first device and the second device are communicatively uncoupled based on a detected presence of the first device or the second device at an endpoint among a plurality of endpoints.

19. The device networking system of claim 14, wherein the first device and the second device are communicatively coupled based on a semantic interval.

20. The device networking system of claim 14, wherein the first device and the second device are communicatively uncoupled based on a semantic interval.

21. The device networking system of claim 14, wherein the first device is comprised by an apparatus comprising a holder and the first device and the second device are communicatively coupled when the second device is secured in the holder.

22. The device networking system of claim 14, wherein the first device is comprised by an apparatus comprising a holder and the first device and the second device are communicatively uncoupled when the second device is unsecured from the holder.

23. The device networking system of claim 14, wherein the first connection information comprises connection information associated with the first communication protocol and connection information associated with the second communication protocol and wherein the first brokered network connection is established based on the connection information associated with the first communication protocol and the second brokered network connection is established based on the connection information associated with the second communication protocol.

24. The device networking system of claim 14, wherein the system establishes a brokered communication link in a semantic network model based on the brokered network connection.

25. The device networking system of claim 24, wherein the brokered communication link is hierarchical comprising a first brokered communication link of a first communication protocol at a first hierarchical level and a second brokered communication link of a second communication protocol at a second hierarchical level.

26. The device networking system of claim 24, wherein the brokered communication link comprises a plurality of semantic fluxes.

27. The device networking system of claim 26, wherein the brokered communication link is hierarchical based on a hierarchy of the semantic fluxes.

28. A payment processor system, comprising: a memory storing data indicative of each of a plurality of users and a plurality of payment accounts associated with a first user among the plurality of users; the memory further storing a plurality of item semantic identities, and further storing associations between the plurality of item semantic identities and a corresponding plurality of items for purchase; a point of sale device comprising at least one wireless transceiver; at least one processor and a computer program operable by the at least one processor to: cause the at least one processor to detect a first payment transaction for one or more of the plurality of the items for purchase by the first user at the point of sale based on one or more inputs from the at least one wireless transceiver; and assign a first account from among the plurality of payment accounts to service the first payment transaction with respect to the one or more of the plurality of items; the first account being assigned as a function of a semantic matching between one or more inferred item semantic identities and a user payment semantic identity associated with the first account, and further based on a reward provided for use of the first account for purchasing the one or more items associated with the item semantic identities; wherein the one or more inferred item semantic identities is inferred by the at least one processor based on the detected first payment transaction and at least one ofthe stored associations between the one or more of the plurality of items for purchase and the corresponding plurality of item semantic identities.

29. The payment processor system of claim 28, wherein the user payment semantic identity is inferred based on a user interest.

30. The payment processor system of claim 28, wherein the user payment semantic identity is inferred based on matching a user interest semantic identity with a rewards semantic identity.

31. The payment processor system of claim 28, wherein the first payment account is associated with a credit card account.

32. The payment processor system of claim 28, wherein the first payment account is associated with a temporary account linked to a permanent bank account.

33. The payment processor system of claim 28, wherein the system comprises a sensor and the system further determines a goodwill to be applied to an item charge amount based on a semantic inferred based on an input from the sensor and further based on a configuration stored in a semantic profile.

34. The payment processor system of claim 33, wherein the sensor is a vision sensor.

35. The payment processor system of claim 34, wherein the sensor is embedded in a user mobile device.

36. The payment processor system of claim 28, wherein the payment processor system comprises a sensor and wherein the payment transaction is initiated based on detected gestures based on inputs from the sensor.

37. The payment processor system of claim 36, wherein the sensor is embedded in a user mobile device.

38. The payment processor system of claim 37, wherein the payment transaction is initiated from the user mobile device.

39. The payment processor system of claim 36, wherein the first account is selected based on one or more additional detected gestures from the user, wherein the one or more additional detected gestures are inferred based on inputs from the sensor.

40. The payment processor system of claim 38, wherein the detected gestures from the user are configured into a semantic profile communicated from a user mobile device via the at least one wireless transceiver.

41. The payment processor system of claim 28, wherein the at least one wireless transceiver is a radio frequency transceiver.

42. The payment processor system of claim 28, wherein the processor and the memory are components of the point of sale device.

43. The payment processor system of claim 42, wherein the point of sale device is a mobile device.

44. A payment processor system, comprising: one or more computer accessible memories storing data indicative of each of a plurality of users and a plurality of payment accounts associated with a first user among the plurality of users; the one or more memories further storing at least one user payment semantic identity defining a user-preferred association between the at least one user payment semantic identity and a first account from among the plurality of payment accounts; at least one wireless transceiver, at least one processor and a computer program operable by the at least one processor to: cause the at least one processor to detect a first payment transaction for a plurality of the items for purchase by the first user at a point of sale based on one or more inputs from the at least one wireless transceiver, one or more of the plurality of items being within an item category; access the at least one stored user payment semantic identity; and assign the first account to service the payment transaction with respect to the one or more of the plurality of items within the item category; the first account being assigned automatically by the at least one processor as a function of a semantic matching between the item category and the at least one stored user payment semantic identity, and further based on a reward provided for use of the first account for purchasing the one or more items associated with the item category; wherein the item category applicable to the one or more of the plurality of the items is received by the at least one processor via the at least one wireless transceiver from a computer system operating a retail application.

45. The payment processor system of claim 44, wherein the user payment semantic identity is inferred based on a user interest.

46. The payment processor system of claim 44, wherein the user payment semantic identity is inferred based on matching a user interest semantic identity with a rewards semantic identity.

47. The payment processor system of claim 44, wherein the payment account is associated with a credit card account.

48. The payment processor system of claim 44, wherein the payment account is associated with a temporary account linked to a permanent bank account.

49. The payment processor system of claim 44, wherein the system comprises at least one sensor and wherein the payment transaction is initiated based on detected gestures based on inputs from the at least one sensor.

50. The payment processor system of claim 49, wherein the first account is selected based on one or more additional detected gestures from the user, wherein the one or more additional detected gestures are inferred based on inputs from the at least one sensor.

51. The payment processor system of claim 50, wherein the detected gestures from the user are configured in a semantic profile communicated via the at least one wireless transceiver.

52. The payment processor system of claim 47, wherein the system determines a goodwill to be applied to an item charge amount based on a semantic inferred based on an input from the at least one sensor and further based on semantic matching with a configured preference stored in a semantic profile.

53. The payment processor system of claim 47, wherein a charge amount of first account is an item category amount as received by the at least one processor via the at least one wireless transceiver from the computer system operating the retail application.

54. The payment processor system of claim 47, wherein the payment transaction is initiated based on a designation gesture by the first user detected based on inputs from the at least one sensor.

55. A payment processor system, comprising: one or more computer accessible memories storing data indicative of each of a plurality of users and a plurality of payment accounts associated with a first user among the plurality of users; the one or more memories further storing at least one user payment semantic identity defining a user-preferred association between the at least one user payment semantic identity and a first account from among the plurality of payment accounts; at least one wireless transceiver,at least one processor and a computer program operable by the at least one processor to: update the first account with a second account in the user-preferred association based on a reward provided by the first account and the second account, the reward being inferred as applicable to the user payment semantic identity; cause the at least one processor to detect a first payment transaction for a plurality of the items for purchase by the first user at a point of sale based on one or more inputs from the at least one wireless transceiver, one or more of the plurality of items being within an item category; and assign the second account to service the payment transaction with respect to the one or more of the plurality of items within the item category; the second account being assigned automatically by the at least one processor as a function of a semantic matching between the item category and the at least one stored user payment semantic identity; wherein the item category applicable to the one or more of the plurality of the items is received by the at least one processor via the at least one wireless transceiver from a computer system operating a retail application.

56. The payment processor system of claim 55, wherein the system comprises at least one sensor and wherein the payment transaction is initiated based on detected gestures based on inputs from the at least one sensor.

57. The payment processor system of claim 55, wherein the first account is selected based on one or more additional detected gestures from the user, wherein the one or more additional detected gestures are inferred based on inputs from the at least one sensor.

58. The payment processor system of claim 57, wherein the detected gestures and the one or more additional detected gestures from the user are configured in a semantic profile communicated via the at least one wireless transceiver.

59. The payment processor system of claim 55, wherein the system determines a goodwill to be applied to an item charge amount based on a semantic inferred based on an input from the at least one sensor and further based on semantic matching with a configured preference stored in a semantic profile.

60. The payment processor system of claim 55, wherein a charge amount of first account is an item category amount as received by the at least one processor via the at least one wireless transceiver from the computer system operating the retail application.

61. The payment processor system of claim 55, wherein the payment transaction is initiated based on a designation gesture by the first user detected based on inputs from the at least one sensor.

62. A payment processor system, comprising: a memory storing data indicative of each of a plurality of users and a plurality of payment accounts associated with a first user among the plurality of users; the memory further storing a plurality of semantic identities, and further storing associations between the plurality of semantic identities and a corresponding plurality of items for purchase; a sensor; a first software application configured to receive inputs from the sensor; the first software application operable by at least one processor to: detect a checkout transaction for one or more of the plurality of the items for purchase by the first user based on one or more inputs from the sensor; and assign a first payment account from among the plurality of payment accounts to service the checkout transaction with respect to the one or more of the plurality of items; the first payment account being assigned as a function of a semantic matching between one or more inferred checkout semantic identities and a user payment semantic identity associated with the first account, and further based on a reward provided by the first account, the reward being inferred as being applicable to the one or more inferred checkout semantic identities based on a validation with the user payment semantic identity; wherein the one or more inferred checkout semantic identities is inferred by the at least one processor based on the detected checkout transaction and at least one of the stored associations between the one or more of the plurality of items for purchase and the corresponding plurality of semantic identities, wherein the user payment semantic identity is inferred based on a user interest.

63. The payment processor system of claim 62, wherein the user payment semantic identity is inferred based on matching a user interest semantic identity with a rewards semantic identity.

64. The payment processor system of claim 62, wherein the user payment semantic identity is inferred based on semantic matching between a user interest activity and a rewards activity.

65. The payment processor system of claim 62, wherein the user payment semantic identity is received by the first software application from a mobile device as a preference for the first user.

66. The payment processor system of claim 62, wherein the checkout transaction is initiated based on detected gestures based on inputs from the sensor.

67. The payment processor system of claim 66, wherein the sensor is embedded in a user mobile device.

68. The payment processor system of claim 67, wherein the payment transaction is initiated from the user mobile device.

69. The payment processor system of claim 66, wherein the first payment account is selected based on one or more additional detected gestures from the first user, wherein the one or more additional detected gestures are inferred based on inputs from the sensor.

70. The payment processor system of claim 69, wherein the detected gestures from the first user are configured into a semantic profile communicated from a user mobile device via at least one wireless transceiver.

71. The payment processor system of claim 62, wherein the first software application is operated by a web browser as a web application.

72. The payment processor system of claim 62, wherein the first software application is operated by a web browser as a web browser plug-in.

73. The payment processor system of claim 72, wherein the first software application is a web browser.

74. A payment processor system, comprising: a memory storing data indicative of each of a plurality of users and a plurality of payment accounts associated with a first user among the plurality of users; the memory further storing a plurality of semantic identities, and further storing associations between the plurality of semantic identities and a corresponding plurality of items for purchase;a sensor; a plurality of software applications wherein at least one software application among the plurality of software applications is configured to receive inputs from the sensor; the plurality of software applications being operable by at least one processor to: detect a checkout transaction for one or more of the plurality of the items for purchase by the first user based on one or more inputs from the sensor; and assign a first payment account from among the plurality of payment accounts to service the checkout transaction with respect to the one or more of the plurality of items; the first payment account being assigned as a function of a semantic matching between one or more inferred checkout semantic identities and a user payment semantic identity associated with the first account, and further based on a reward provided by the first account, the reward being inferred as being applicable to the one or more inferred checkout semantic identities based on a validation with the user payment semantic identity; wherein the one or more inferred checkout semantic identities is inferred by the at least one processor based on the detected checkout transaction and at least one of the stored associations between the one or more of the plurality of items for purchase and the corresponding plurality of semantic identities, wherein the user payment semantic identity is inferred based on a user interest.

75. The payment processor system of claim 74, wherein the user payment semantic identity is inferred based on matching a user interest semantic identity with a rewards semantic identity.

76. The payment processor system of claim 74, wherein the user payment semantic identity is inferred based on semantic matching between a user interest activity and a rewards semantic activity.

77. The payment processor system of claim 74, wherein the user payment semantic identity is received by at least one software application among the plurality of software applications from a mobile device as a preference for the first user.

78. The payment processor system of claim 74, wherein the system determines a goodwill to be applied to an item charge amount based on a semantic inferred based on an input from at leastone sensor and further based on semantic matching with a configured preference stored in a semantic profile.

79. The payment processor system of claim 74, wherein a charge amount of first payment account is a checkout semantic identity amount as received by a first software application among the plurality of software applications via at least one transceiver from a computer system operating a second software application among the plurality of software applications.

80. The payment processor system of claim 74, wherein a charge amount of first payment account is a checkout semantic identity amount as received by a first software application among the plurality of software applications via an established communication link between the first software application and a second software application, among the plurality of software applications81. The payment processor system of claim 74, wherein a charge amount of first payment account is a checkout semantic identity amount as received by a first software application among the plurality of software applications from a second software application among the plurality of software applications via a shared memory.

82. The payment processor system of claim 74, wherein the checkout transaction is initiated based on a designation gesture by the first user detected based on inputs from at least one sensor.

83. The payment processor system of claim 74, wherein a first software application among the plurality of software applications is operated by a web browser as a web application.

84. The payment processor system of claim 74, wherein a first software application among the plurality of software applications is operated by a web browser as a web browser plug-in.

85. The payment processor system of claim 74, wherein a first software application among the plurality of software applications is a web browser.

86. A payment processor system, comprising: one or more computer accessible memories storing data indicative of each of a plurality of users and a plurality of payment accounts associated with a first user among the plurality of users; the one or more memories further storing at least one user payment semantic identity defining a user-preferred association between the at least one user payment semantic identity and a first payment account from among the plurality of payment accounts;a first software application operable by a first processor, the first software application being communicatively coupled with a second software application, the first software application being operable by the at least one processor to: update the first payment account with a second payment account in the userpreferred association based on a reward provided by the first payment account and the second payment account, the reward being inferred as applicable to the user payment semantic identity; receive checkout transaction data from the second software application for a plurality of the items for purchase by the first user, the checkout transaction data comprising a checkout semantic identity linked to the one or more of the plurality of the items for purchase; and assign the second payment account to service the payment transaction with respect to the one or more of the plurality of items for purchase; the second payment account being assigned automatically by the at least one processor as a function of a semantic matching between the checkout semantic identity and the at least one stored user payment semantic identity; wherein the user payment semantic identity is inferred based on a user interest.

87. The payment processor system of claim 86, wherein the user payment semantic identity is inferred based on matching a user interest semantic identity with a rewards semantic identity.

88. The payment processor system of claim 86, wherein the user payment semantic identity is inferred based on semantic matching between a user interest activity and a rewards activity.

89. The payment processor system of claim 86, wherein a charge amount of first payment account is a checkout semantic identity amount as received by the first application via at least one wireless transceiver from the second software application.

90. The payment processor system of claim 89, wherein a charge amount of first payment account is a checkout semantic identity amount as received by the first software application via an established communication link with the second software application.

91. The payment processor system of claim 86, wherein a charge amount of first payment account is a checkout semantic identity amount as received by the first software application via a shared memory from the second software application.

92. A conveyor system comprising:an optical sensor; a memory, the memory storing conveyable object data for each of a plurality of conveyable objects, the conveyable object data including a visual aspect, a size, and a weight for each of the plurality of conveyable objects; a first plurality of posts supporting a first roller engaging a first conveying surface; a second conveying surface positioned adjacent to the first conveying surface to form a substantially continuous combined conveying surface, the first conveying surface and the second conveying surface being arranged for coupled movement via at least one of a gear, a belt, or a chain; the first plurality of posts having a first sensor arranged to detect a first weight signal associated with a weight on the first conveying surface; one or more processors in communication with the memory and arranged to receive an optical sensor input from the optical sensor and to receive the first weight signal from the first sensor, the one or more processors further being configured to: infer and store in the memory, for each of the plurality of conveyable objects, a first set of leadership semantics based on the conveyable object data; determine, based on the optical sensor input from the optical sensor, that a first item is located on the first conveying surface; determine a first size of the first item based on the optical sensor input; determine a first weight of the first item based on the first weight signal from the first sensor; infer a second set of leadership semantic attributes for the first item based on the optical sensor input; semantically match the second set of leadership semantic attributes with the first set of leadership attributes; identify the first item as a particular convey able object from among the plurality of conveyable objects based on the semantic matching, the first weight and the first size; and send, to a first account associated with the first item, a presentation depicting the conveying surface and a location of the first item on the conveying surface.

93. The conveyor system of claim 92, wherein the visual aspect comprises an item image of the conveyable object.

94. The conveyor system of claim 93, wherein the one or more processors is further configured to receive, from a user device associated with the first account, the item image, the item image being one of a still or video image of the conveyable item, the first account further having a user identity associated with the first account.

95. The conveyor system of claim 92, wherein the first plurality of posts is configured to adjust the first conveying surface to isolate the first item.

96. The conveyor system of claim 92, wherein the first plurality of posts is configured to adjust the first conveying surface to position the first item at a plurality of orientations with respect to the optical sensor.

97. The conveyor system of claim 92, wherein the first plurality of posts is configured to adjust the first conveying surface to block the first item on the second conveying surface.

98. The conveyor system of claim 92, wherein the first conveying surface is formed by a longitudinal composed conveying surface.

99. The conveyor system of claim 92, wherein the first conveying surface is formed by a transversal composed conveying surface.

100. The conveyor system of claim 92, wherein the first conveying surface is longitudinally composed as a set of conveying surfaces.

101. The conveyor system of claim 92, wherein the first conveying surface is transversally composed as a set of conveying surfaces.

102. The conveyor system of claim 92, wherein the first conveying surface is composed from a plurality of component conveying surfaces and a shape of the first conveying surface is adjusted by altering at least one component conveying surface from among the plurality of component conveying surfaces.

103. The conveyor system of claim 92, wherein the first plurality of posts includes an actuated barrier, the actuated barrier being actuated based on semantic access control.

104. The conveyor system of claim 92, wherein the first plurality of posts includes at least two actuated barriers or wedges, the actuated barriers or wedges being actuated based on semantic analysis to jointly implement a guiding path.

105. The conveyor system of claim 92, wherein the first plurality of posts comprises at least one guiding or access control barrier.

106. The conveyor system of claim 92, wherein the first plurality of posts is attached to a main conveyor structure comprising the second conveying surface.

107. The conveyor system of claim 106, wherein the first plurality of posts is attached to the main conveyor structure via a plurality of connectors.

108. The conveyor system of claim 107, wherein the first plurality of posts is attached to the main conveyor structure via a plurality of latches.

109. The conveyor system of claim 106, wherein the first plurality of posts is attached to the main conveyor structure via a plurality of suction pods.

110. The conveyor system of claim 92, wherein the first plurality of posts have attached at least one facade or shell panel between at least two posts among the plurality of posts.

111. The conveyor system of claim 110, wherein the first plurality of posts is attached to the main conveyor structure via the at least one facade or shell panel.

112. The conveyor system of claim 92, wherein the first plurality of posts is attached to a main conveyor structure and the first roller is coupled to the main conveyor via at a coupling gear.

113. The conveyor system of claim 106, wherein the first roller is coupled to the main conveyor via a belt.

114. The conveyor system of claim 113, wherein the belt is engaged by a toothed coupling with the first conveying surface or the second conveying surface.

115. The conveyor system of claim 114, wherein the toothed coupling is engaged, at least partially, by the first conveying surface or the second conveying surface.

116. The conveyor system of claim 92, wherein the first roller is electrically actuated and rotated.

117. The conveyor system of claim 92, wherein the first roller is adjusted in elevation based on the actuation and positioning of a set of first roller supports connected to the plurality of posts.

118. The conveyor system of claim 92, wherein the first roller is adjusted in elevation based on an adjusted elevation of at least one post among the plurality of posts.

119. The conveyor system of claim 92, wherein the first roller is adjusted in elevation based on an adjusted elevation of a module of at least one post among the plurality of posts.

120. The conveyor system of claim 92, wherein the first plurality of posts is moveable to move along the second conveying surface.

121. The conveyor system of claim 92, wherein the first set of leadership semantic attributes is inferred based on sensing capabilities of the optical sensor.

122. The conveyor system of claim 92, wherein the memory further stores a plurality of endpoints and wherein the first set of leadership semantic attributes is inferred based on sensing capabilities at a first endpoint among a plurality of endpoints.

123. The conveyor system of claim 92, wherein the optical sensor is carried on a post among the plurality of posts.

124. A conveyor system comprising: a memory; the memory storing at least one image or frame of an item of interest, and a stored weight of the item of interest; a first plurality of posts supporting a first conveying surface and having a first sensor; a second plurality of posts supporting a second conveying surface and having a second sensor; one or more processors in communication with the memory and arranged to receive first sensor data associated with signals detected by the first sensor and second sensor data associated with signals detected by the second sensor; a first subset of the first plurality of posts being coupled to a second subset of the second plurality of posts via a first set of connectors; the first subset of the first plurality of posts supporting a first roller, the first roller engaging the first conveying surface; the second subset of the second plurality of posts supporting a second roller, the second roller being actuated by at least one actuator and engaging the second conveying surface; wherein the second roller is positionable by the at least one actuator such that the second conveying surface is positioned adjacent to the first conveying surface to form a substantially continuous combined conveying surface; wherein the second conveying surface is engaged and moveable by the second roller synchronously in the same orientation with the first conveying surface;wherein the one or more processors detects the presence of the item of interest on the combined conveying surface based on the first sensor data and the second sensor data; wherein the detected presence is based on a comparison by the one or more processors between the stored weight and a sensed weight, the sensed weight being based on at least one of the first sensor data or the second sensor data; wherein the detected presence is further based on semantically matching a first set of leadership semantic attributes with a second set of leadership attributes, wherein the first set of leadership attributes is inferred based on the stored image or frame of the item of interest; and further wherein the second set of leadership attributes is inferred based on sensed optical data, the sensed optical data being based on at least one of the first sensor data or the second sensor data.

125. A conveyor system comprising: a memory; the memory storing at least one image or frame of an item of interest, and a stored weight of the item of interest, a first plurality of posts supporting a first conveying surface and having a first sensor; a second plurality of posts supporting a second conveying surface and having a second sensor; a first subset of the first plurality of posts being coupled to a second subset of the second plurality of posts via a first set of connectors; the first subset of the first plurality of posts supporting a first roller, the first roller engaging the first conveying surface; the second subset of the second plurality of posts supporting a second roller, the second roller engaging the second conveying surface; the second conveying surface positioned adjacent to the first conveying surface to form a substantially continuous combined conveying surface; wherein the first conveying surface and the second conveying surface are engaged and moved by an actuator which enables the first conveying surface and the second conveying surface to move synchronously in the same orientation;wherein the system detects the presence of the item of interest on the combined conveying surface based on one or more inputs from the first sensor or the second sensor; wherein the detected presence is based on comparing the stored weight with a sensed weight, wherein the sensed weight is determined based on the one or more inputs from the first sensor or the second sensor; the presence detection further being based on semantically matching a first set of leadership semantic attributes with a second set of leadership attributes, wherein the first set of leadership attributes is inferred based on the stored image or frame and the second set of leadership attributes is inferred based on the one or more inputs from the first sensor or the second sensor, at least one of the first sensor or the second sensor being an optical sensor.

126. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart; the first cart having a first electrical power module with a first signal conditioner, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; the second cart having a second electrical power module with a second signal conditioner, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first electrical power module and the second electrical power module being connectable in a mutual electrical charging configuration in which the first electrical power module is electrically coupled with the second electrical power module via a first physical socket coupling; wherein the first processor is programmed to operate the first signal conditioner to condition an electrical signal to produce and deliver a first conditioned electrical signal to the first battery to provide charging electrical power to the first battery and further to deliver the first conditioned electrical signal to the second electrical power module; and wherein the second processor is programmed to operate the second signal conditioner to condition the first conditioned electrical signal to produce and deliver a secondconditioned electrical signal to the second battery to provide charging electrical power to the second battery; and wherein the first signal conditioner comprises a first switching regulator operated by the first processor based on semantic analysis, the semantic analysis being based on a determination of a projected plurality of users of the plurality of carts, and further based on a plurality of user interests associated with the plurality of projected users.

127. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart, the first cart having a first electrical power module with a first signal conditioner, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; the second cart having a second electrical power module with a second signal conditioner, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first electrical power module and the second electrical power module being connectable in a mutual electrical charging configuration in which the first electrical power module is electrically coupled with the second electrical power module via a first physical socket coupling; wherein the first processor is programmed to operate the first signal conditioner to condition an electrical signal to produce and deliver a first conditioned electrical signal to the first battery to provide charging electrical power to the first battery and further to deliver the first conditioned electrical signal to the second electrical power module; wherein the second processor is programmed to operate the second signal conditioner to condition the first conditioned electrical signal to produce and deliver a second conditioned electrical signal to the second battery to provide charging electrical power to the second battery; and wherein the first signal conditioner comprises a first switch operated by the first processor based on semantic analysis, the semantic analysis being based on a determination of a projected plurality of users of the plurality of carts, and further based on a plurality of user interests associated with the plurality of projected users.

128. The cart system of claim 127, wherein the first cart is further coupled to an external power charging socket.

129. The cart system of claim 127, wherein the first cart and the second cart are robotic carts.

130. The cart system of claim 127, wherein the second processor is programmed to map the first cart to an at least one endpoint among a plurality of endpoints.

131. The cart system of claim 127, wherein the first signal conditioner comprises a first conditioning circuit having a first rectifier.

132. The cart system of claim 127, wherein the first signal conditioner comprises a first conditioning circuit having a first inverter.

133. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart; the first cart having a first electrical power module with a first signal conditioner, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; the second cart having a second electrical power module with a second signal conditioner, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first electrical power module and the second electrical power module being connectable in a mutual electrical charging configuration in which the first electrical power module is electrically coupled with the second electrical power module via a first physical socket coupling; wherein the first processor is programmed to operate the first signal conditioner to condition an electrical signal to produce and deliver a first conditioned electrical signal to the first battery to provide charging electrical power to the first battery and further to deliver the first conditioned electrical signal to the second electrical power module; wherein the second processor is programmed to operate the second signal conditioner to condition the first conditioned electrical signal to produce and deliver a second conditioned electrical signal to the second battery to provide charging electrical power to the second battery;wherein the first signal conditioner is connected to a first latching socket on the first cart, wherein the first latching socket is arranged to couple to and latch with a second latching socket on the second cart; and wherein the first latching socket and the second latching socket are latched or unlatched based on a semantic goal.

134. The cart system of claim 133, wherein the first latching socket and the second latching socket each includes a set of male and female connectors.

135. The cart system of claim 133, wherein the first latching socket and the second latching socket each includes a set of pin connectors and at least one of a seat or guide.

136. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart; a first switching and conditioning circuit carried on the first cart, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; a second switching and conditioning circuit carried on the second cart, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first cart and the second cart being connected in a mutual electrical charging configuration in which the first cart is electrically coupled with the second cart via a first electrical socket; wherein the first processor is programmed to operate the first switching and signal conditioning circuit to condition an electrical signal to produce a first conditioned electrical signal and to switch and route the first conditioned electrical signal to provide charging electrical power to the first battery or to the second cart; wherein the second processor is programmed to operate the second switching and signal conditioning circuit to condition the received first conditioned electrical signal to produce a second conditioned electrical signal and to switch and to route the second conditioned electrical signal to provide charging electrical power to the second battery or to an additional cart from among the plurality of carts; andwherein the first switching and signal conditioning circuit comprises a first switching regulator operated by the first processor based on semantic analysis, wherein the semantic analysis is based on a semantic drift inference in rapport with a semantic goal.

137. The cart system of claim 136, wherein the first switching and signal conditioning circuit comprises a first rectifier.

138. The cart system of claim 136, wherein the first switching and signal conditioning circuit comprises a first inverter.

139. The cart system of claim 136, wherein the first cart is further coupled to an external power charging socket.

140. The cart system of claim 136, wherein each of the carts among the plurality of carts are robotic carts.

141. The cart system of claim 136, wherein the second memory stores a plurality of endpoints and further, wherein the second processor is programmed to map the first cart to an at least one endpoint among the plurality of endpoints.

142. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart; a first switching and conditioning circuit carried on the first cart, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; a second switching and conditioning circuit carried on the second cart, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first cart and the second cart being connected in a mutual electrical charging configuration in which the first cart is electrically coupled with the second cart via a first electrical socket; wherein the first processor is programmed to operate the first switching and signal conditioning circuit to condition an electrical signal to produce a first conditioned electrical signal and to switch and route the first conditioned electrical signal to provide charging electrical power to the first battery or to the second cart; wherein the second processor is programmed to operate the second switching and signal conditioning circuit to condition the received first conditioned electrical signal to producea second conditioned electrical signal and to switch and to route the second conditioned electrical signal to provide charging electrical power to the second battery or to an additional cart from among the plurality of carts; and wherein the first switching and signal conditioning circuit comprises a first switch operated by the first processor based on semantic analysis, wherein the semantic analysis is based on a semantic drift inference in rapport with a semantic goal.

143. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart, a first switching and conditioning circuit carried on the first cart, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; a second switching and conditioning circuit carried on the second cart, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first cart and the second cart being connected in a mutual electrical charging configuration in which the first cart is electrically coupled with the second cart via a first electrical socket; wherein the first processor is programmed to operate the first switching and signal conditioning circuit to condition an electrical signal to produce a first conditioned electrical signal and to switch and route the first conditioned electrical signal to provide charging electrical power to the first battery or to the second cart; wherein the second processor is programmed to operate the second switching and signal conditioning circuit to condition the received first conditioned electrical signal to produce a second conditioned electrical signal and to switch and to route the second conditioned electrical signal to provide charging electrical power to the second battery or to an additional cart from among the plurality of carts; wherein the first signal conditioning circuit is connected to a first latching socket attached to the first cart, wherein the first latching socket couples and latches with a second latching socket attached to the second cart; and wherein the first latching socket and the second latching socket are latched or unlatched based on a semantic goal.

144. The cart system of claim 143, wherein the first latching socket and the second latching socket each include a set of male and female connectors.

145. The cart system of claim 143, wherein the first latching socket and the second latching socket each comprise a set of pin connectors wherein the pin connectors are actuated to extend and retract within a seat or guide hole to make contact and electrically couple with a matching pin connector within the seat or guide hole.

146. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart, a first power switching module and a first signal conditioning module attached to the first cart, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; a second power switching module and a second signal conditioning module attached to the second cart, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first cart and the second cart being connected in a mutual electrical charging configuration in which the first cart is electrically coupled with the second cart via one or more electrical sockets; wherein the first processor is programmed to operate the first power switching module and the first signal conditioning module to condition an electrical signal from the first battery to produce a first conditioned signal and to switch and to route the first conditioned signal to the second cart; wherein the second processor is programmed to operate the second power switching module and the second signal conditioning module to condition the received first conditioned electrical signal and to produce a second conditioned electrical signal and to switch and to route the second conditioned electrical signal to provide charging electrical power to the second battery or to an additional cart from among the plurality of carts; and wherein the first switching module and the first signal conditioning module comprises a first switching regulator operated by the first processor based on semantic analysis, wherein the semantic analysis is based on a semantic drift inference in rapport with a semantic goal.

147. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart; a first power switching module and a first signal conditioning module attached to the first cart, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; a second power switching module and a second signal conditioning module attached to the second cart, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first cart and the second cart being connected in a mutual electrical charging configuration in which the first cart is electrically coupled with the second cart via one or more electrical sockets; wherein the first processor is programmed to operate the first power switching module and the first signal conditioning module to condition an electrical signal from the first battery to produce a first conditioned signal and to switch and to route the first conditioned signal to the second cart; wherein the second processor is programmed to operate the second power switching module and the second signal conditioning module to condition the received first conditioned electrical signal and to produce a second conditioned electrical signal and to switch and to route the second conditioned electrical signal to provide charging electrical power to the second battery or to an additional cart from among the plurality of carts; and wherein the first switching module and the first signal conditioning module comprises a first switch operated by the first processor based on semantic analysis, wherein the semantic analysis is based on a semantic drift inference in rapport with a semantic goal.

148. The cart system of claim 147, wherein the first signal conditioning module comprises a first inverter.

149. The cart system of claim 147, wherein the first cart is further coupled to an external power charging socket.

150. The cart system of claim 147, wherein each of the plurality of carts are robotic carts.

151. The cart system of claim 147, wherein the second memory stores a plurality of endpoints and further, wherein the second processor is programmed to map the first cart to at least one endpoint among the plurality of endpoints.

152. A cart system, comprising: a plurality of carts forming the cart system, the plurality of carts including at least a first cart and a second cart; a first power switching module and a first signal conditioning module attached to the first cart, the first cart further having a first battery, a first processor and a first memory accessible by the first processor; a second power switching module and a second signal conditioning module attached to the second cart, the second cart further having a second battery, a second processor and a second memory accessible by the second processor; the first cart and the second cart being connected in a mutual electrical charging configuration in which the first cart is electrically coupled with the second cart via one or more electrical sockets; wherein the first processor is programmed to operate the first power switching module and the first signal conditioning module to condition an electrical signal from the first battery to produce a first conditioned signal and to switch and to route the first conditioned signal to the second cart; wherein the second processor is programmed to operate the second power switching module and the second signal conditioning module to condition the received first conditioned electrical signal and to produce a second conditioned electrical signal and to switch and to route the second conditioned electrical signal to provide charging electrical power to the second battery or to an additional cart from among the plurality of carts; wherein the first signal conditioning module is connected to a first latching socket carried on the first cart, wherein the first latching socket couples and latches with a second latching socket carried on the second cart, wherein the first latching socket and the second latching socket comprise each a set of male and female connectors; and wherein the first latching socket and the second latching socket are latched or unlatched based on a semantic goal.

153. The cart system of claim 152, wherein the first latching socket and the second latching socket each comprise a set of pin connectors wherein the pin connectors are actuated to extend and retract within a seat or guide hole to make contact and electrically couple with a matching pin connector within the seat or guide hole.

154. A robotic device for manipulating a computer control device positioned adjacent a first securable connector, the robotic device comprising: a first optical sensor, a second optical sensor; at least one actuated link; a second securable connector; at least one of the first optical sensor or the second optical sensor being arranged to provide a field of view towards the second securable connector and the computer control device; a user interface; and one or more processors in communication with a memory, the memory storing programable instructions configured to cause the one or more processors to: determine, based on inputs from at least one of the first optical sensor or the second optical sensor, that the first securable connector is fastened to the second securable connector, the second securable connector being positioned to retain the robotic device adjacent the computer control device when the first securable connector is fastened to the second securable connector; cause the actuated link to physically interact with the computer control device, whereby a first movement of the actuated link causes the computer control device to achieve a first state at a first time, and a second movement of the actuated link causes the computer control device to achieve a second state at a second time; and present an augmented display on the user interface, the augmented display including an indication of the state of the computer control device.

155. The robotic device of claim 154, wherein the computer control device is a physical button.

156. The robotic device of claim 154, wherein the computer control device is a physical switch.

157. The robotic device of claim 154, wherein the computer control device is a displayed button.

158. The robotic device of claim 154, wherein the computer control device is a keyboard.

159. The robotic device of claim 154, wherein the computer control device is a remote control.

160. The robotic device of claim 154, wherein the computer control device is portion of a remote control.

161. The robotic device of claim 154, wherein the computer control device is a wireless mouse.

162. The robotic device of claim 154, wherein the computer control device is a portion of a wireless mouse.

163. The robotic device of claim 154, wherein the computer control device is a touch screen.

164. The robotic device of claim 154, wherein the computer control device is a user interface control.

165. The robotic device of claim 154, wherein the computer control device is a tablet computer.

166. The robotic device of claim 154, wherein the computer control device is a laptop computer.

167. The robotic device of claim 154, wherein the computer control device is portion of a keyboard.

168. A robotic device comprising: an external optical sensor, an internal optical sensor; at least one actuated link; at least one latch or pod; at least one pocket; one or more processors in communication with a memory, the memory storing a programable instructions configured to cause the one or more processors to: determine based on inputs from the at least one among the internal and external optical sensors that a target device is inserted, at least partially, into the at least one pocket at a first time; fasten the target device to the robotic device by actuating the at least one latch or pod at a second time, the target device being manipulated, fastened and secured to be significantly encompassed and secured into the pocket of the robotic device; the internal optical sensor being arranged to have a field of view towards the target device; and cause the target device to be manipulated via the at least one actuated link; wherein the manipulation includes causing the actuated link to physically interact with a user interface control on the target device.

169. The robotic device of claim 168, wherein the target device is a physical switch.

170. The robotic device of claim 169, wherein the user interface control is a switch lever.

171. The robotic device of claim 168, wherein the user interface control is a button.

172. The robotic device of claim 168, wherein the target device is a keyboard.

173. The robotic device of claim 168, wherein the target device is a remote control.

174. The robotic device of claim 168, wherein the target device is a wireless mouse.

175. The robotic device of claim 168, wherein the target device is a touch screen.

176. The robotic device of claim 168, wherein the user interface control is displayed on the touch screen.

177. The robotic device of claim 168, wherein the target device is a tablet.

178. The robotic device of claim 177, wherein the user interface control is displayed on the tablet.

179. The robotic device of claim 168, wherein the target device is a laptop computer comprising at least one amongst a touch screen and a keyboard.

180. A robotic device for manipulating an electrical control device positioned on a first structure, the robotic device comprising: a first optical sensor, a second optical sensor; at least one actuated link; a first securable connector; at least one of the first optical sensor or the second optical sensor being arranged to provide a field of view towards the first structure and the computer control device; a user interface; and one or more processors in communication with a memory, the memory storing programable instructions configured to cause the one or more processors to: determine, based on inputs from at least one of the first optical sensor or the second optical sensor, that the first securable connector is fastened to the first structure, the first securable connector being positioned to retain the robotic device adjacent the electrical control device when the first securable connector is fastened to the first structure;cause the actuated link to physically interact with the electrical control device, whereby a first movement of the actuated link causes the electrical control device to achieve a first state at a first time, and a second movement of the actuated link causes the electrical control device to achieve a second state at a second time; and present an augmented display on the user interface, the augmented display including an indication of the state of the electrical control device.

181. A robotic device for manipulating a computer control device, the robotic device comprising: a first optical sensor, a second optical sensor; at least one actuated link; a first securable connector; at least one of the first optical sensor or the second optical sensor being arranged to provide a field of view towards the computer control device; a user interface; and one or more processors in communication with a memory, the memory storing programable instructions configured to cause the one or more processors to: determine, based on inputs from at least one of the first optical sensor or the second optical sensor, that the first securable connector is fastened to a computer control device enclosure or surface, the first securable connector being positioned to retain the robotic device adjacent the computer control device when the first securable connector is fastened to the computer control device enclosure or surface; cause the actuated link to physically interact with the computer control device, whereby a first movement of the actuated link causes the computer control device to manipulate a displayed user interface control to achieve a first state at a first time, and a second movement of the actuated link causes the computer control device to manipulate the displayed user interface control to achieve a second state at a second time; and present an augmented display on the user interface, the augmented display including an indication of the state of the displayed user interface control.

182. A robotic emulation device, comprising:a processor, a memory, a first transceiver and a second transceiver, the first transceiver being coupled to a first physical connector; the first physical connector being configured for connection to a target device, wherein the robotic emulation device, when the first physical connector is connected to the target device, is arranged to receive a video signal from the target device; the robotic emulation device further being arranged to transmit electric signals to a peripheral input device via the second transceiver, the peripheral input device being coupled to the target device via a third transceiver; the peripheral input device having an associated peripheral input device identification which is indicative of a computer keyboard or mouse; the processor being configured to (1) interpret the video signal received via the first physical connector, wherein the interpretation includes identifying based on semantic analysis a target interface control associated with a first user interface encoded and transmitted in the video signal, (2) determine manipulation data associated to the target interface control, and (3) transmit one or more manipulation data electric signals to the peripheral input device via the second transceiver, the one or more manipulation data electric signals including key codes associated with an emulated performance of one or more manipulation actions at the peripheral input device; wherein the peripheral input device is configured to form one or more encoded inputs from the one or more manipulation data electric signals and to provide the encoded inputs to the target device via the third transceiver.

183. The robotic emulation device of claim 182, wherein the peripheral input device is configured to emulate a peripheral device.

184. The robotic emulation device of claim 182, wherein the peripheral input device further comprises a peripheral device driver.

185. The robotic emulation device of claim 182, wherein the peripheral input device comprises a hardware bus core driver.

186. The robotic emulation device of claim 185, wherein the peripheral input device operates software implementing a human device interface profile or function.

187. The robotic emulation device of claim 182, wherein at least one among the target device or the robotic emulation device is communicatively coupled with at least one computer host ortenant, wherein the robotic emulation device manipulates the target device user interface to manage and configure computer hosts, tenants, virtual machines, communication, resources, tasks, resource allocations, responses or flux infrastructure comprising the least one computer host or tenant.

188. The robotic emulation device of claim 182, wherein the target interface control is associated with an interest semantic.

189. The robotic emulation device of claim 182, wherein the manipulation data is inferred and applied based on interpreting video signals encoding pixels associated with a digital representation of a user agenda or task.

190. The robotic emulation device of claim 182, wherein the manipulation data is inferred and applied based on inputs from a sensor capturing data associated with a rendering of a user agenda or task.

191. The robotic emulation device of claim 182, wherein the manipulation data is determined based on interpreting video signals encoding a digital representation of an agenda or task management application output.

192. The robotic emulation device of claim 182, wherein the robotic emulation device is communicatively coupled with at least one computer host or tenant, wherein the robotic emulation device communicates imaging data encoded in the video signal with the at least one computer host or tenant.

193. The robotic emulation device of claim 182, wherein the target interface control is determined based on a first inferred semantic and further based on semantic drift between the first inferred semantic and a semantic associated with the target interface control.

194. The robotic emulation device of claim 193, wherein the target interface control is determined based on an interest semantic.

195. The robotic emulation device of claim 194, wherein the target interface control is associated with an inferred semantic identity or activity, wherein the semantic identity or activity is inferred and associated with the target interface control based on video signal analysis, wherein the target interface control is determined based on a semantic drift between the semantic identity or activity and the interest semantic.

196. The robotic emulation device of claim 182, wherein the target interface control is associated with an inferred semantic identity or activity, wherein the semantic identity oractivity is inferred and associated with the target interface control based on a document analysis.

197. The robotic emulation device of claim 196, wherein the document analysis is based on launching a document viewing application on the target device and acquiring and analyzing video signals from the target device comprising the document viewing application user interface.

198. The robotic emulation device of claim 196, wherein the document analysis is based on capturing optical sensor inputs comprising renderings of pages of the document and analyzing the optical sensor inputs.

199. The robotic emulation device of claim 196, wherein the document analysis is based on downloading and analyzing the document data from a web server.

200. The robotic emulation device of claim 182, wherein robotic emulation device is communicatively coupled with at least one computer host or tenant via a fourth transceiver, and wherein the video signal comprises a plurality of video signals, and further wherein the robotic emulation device is configured to forward video data associated with one or more of the plurality of received video signals to the at least one computer host or tenant, the at least one computer host or tenant applying image analysis to the video data to determine user interface control semantics and to return user interface control semantics signals to the robotic emulation device to cause manipulation action signals inferences by the robotic emulation device.

201. The robotic emulation device of claim 182, wherein the processor is being further configured to determine content associated to the target user interface control and forming one or more data signals based on the determined content, and transmitting the one or more data signals to the peripheral input device; wherein the peripheral input device is configured to form, based on the one or more data signals, one or more encoded inputs related to the emulated peripheral input device, and to provide the one or more encoded inputs to the target device via the third transceiver.

202. A robotic emulation device, comprising: a processor, a memory and at least one transceiver; the robotic emulation device being arranged to receive a video signal from the target device via the at least one transceiver;the robotic emulation device further being arranged to transmit electric signals to the target device via the at least one transceiver; the robotic emulation device further being arranged to communicate data with at least one computer host or tenant via the at least one transceiver; the robotic emulation device storing an associated peripheral input device identification which is indicative of an emulated peripheral input device; the processor being configured to control a movement and selection by a cursor of a target user interface control on a first user interface by: (1) interpreting the video signal received via the at least one transceiver, wherein the interpreting includes identifying based on semantic analysis a target interface control associated with the first user interface encoded and transmitted in the video signal, (2) determining content associated to the target user interface control and forming one or more manipulation signals based on the determined content, and (3) forming, based on the one or more manipulation signals, one or more encoded inputs related to the emulated peripheral input device; and (4) transmitting via the at least one transceiver the one or more encoded inputs to the target device, wherein the video signal comprises a plurality of video signals, and further wherein the robotic emulation device is configured to forward data associated with one or more of the plurality of received video signals to the at least one computer host or tenant, the at least one computer host or tenant applying image analysis to the data to determine user interface control semantics and to return user interface control semantics signals to the robotic emulation device to cause manipulation action signals inferences by the robotic emulation device.

203. The robotic emulation device of claim 202, wherein the content is determined based on an inferred first semantic at an endpoint, the first semantic being inferred based on an input from a sensor.

204. The robotic emulation device of claim 203, wherein the content is further determined based on a semantic matching between the first semantic and a second semantic comprised in a user preference or profile.

205. The robotic emulation device of claim 202, wherein the target user interface control is determined based on an inferred first semantic at an endpoint, the first semantic being inferred based on an input from a sensor.

206. The robotic emulation device of claim 205, wherein the target user interface control is further determined based on a semantic matching between the first semantic and a second semantic comprised in a user preference or profile.

207. The robotic emulation device of claim 202, wherein the content or the target user interface control is further determined based on an indication from a user.

208. The robotic emulation device of claim 202, wherein the peripheral input device identification is stored as a device descriptor.

209. The robotic emulation device of claim 202, wherein the robotic emulation device having an associated peripheral device designation and wherein the processor is further being configured to: determine one or more manipulation actions associated to the target interface control, and form and transmit one or more manipulation action signals to the target device via the at least one transceiver, the one or more manipulation action signals being based on the peripheral device designation and including key codes associated with the an emulated performance of the one or more manipulation actions by the robotic emulation device.

210. A robotic emulation device, comprising: a processor, a memory, a first wireless transceiver and a first physical connector; the first physical connector being configured for connection to a target device, wherein the robotic emulation device, when the first physical connector is connected to the target device, is arranged to receive a video signal from the target device; the robotic emulation device further being arranged to transmit wireless signals to a peripheral input device via the first wireless transceiver, the peripheral input device being coupled to the target device via a second physical connector; the peripheral input device having an associated peripheral input device identification which is indicative of a computer keyboard or mouse; the processor being configured to (1) interpret the video signal received via the first physical connector, wherein the interpretation includes identifying based on semantic analysis a target interface control associated with a first user interface encoded and transmitted in the video signal, (2) determine one or more manipulation actions associated to the targetinterface control, and (3) transmit one or more manipulation action wireless signals to the peripheral input device via the first wireless transceiver, the one or more manipulation action wireless signals including key codes associated with an emulated performance of the one or more manipulation actions by the peripheral input device; wherein the peripheral input device is configured to form one or more encoded inputs from the one or more manipulation actions and to provide the encoded inputs to the target device via the second physical connector.

211. A robotic emulation device, comprising: a processor, a memory and a first physical connector; a memory storing a plurality of manipulation action semantics; the first physical connector being configured for connection to a target device, wherein the robotic emulation device, when the first physical connector is connected to the target device, is arranged to receive a video signal from the target device; the robotic emulation device further being arranged to transmit signals to the target device via the first physical connector; the robotic emulation device having an associated designation of a peripheral input device; the processor being configured to (1) interpret the video signal received via the first physical connector, wherein the interpretation includes identifying based on semantic analysis a target interface control associated with a first user interface encoded and transmitted in the video signal, (2) determine one or more manipulation actions associated to the target interface control, and (3) form and transmit one or more manipulation action signals to the target device via the first physical connector, the one or more manipulation action signals comprising encoded inputs based on the peripheral input device designation, and including key codes associated with the performance of the one or more manipulation actions by the target device, wherein the one or more manipulation actions are determined based on a plurality of semantic factorizations of one or more manipulation action semantics among the plurality of the stored manipulation action semantics.

212. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one processor and at least one sensor;the memory further storing at least one affirmative semantic and at least one non-affirmative semantic; wherein at least one among the at least one affirmative semantic and the at least one non- affirmative semantic is indicative of a first activity; a computer program configured to cause the at least one processor to infer a first semantic and a second semantic based on one or more inputs from the at least one sensor; wherein the system is configured to generate semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time, wherein the semantic augmentation is directed to the first user based on the first identity; and wherein the first inferred semantic comprises a second activity and the determination that the first inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.

213. The smart device system of claim 212, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is configured by the first user.

214. The smart device system of claim 212, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is associated with a semantic time.

215. The smart device system of claim 212, wherein the system is configured to infer a countermeasure and to apply the counter-measure to reduce the high entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

216. The smart device system of claim 212, wherein the system is configured to infer an affirmative measure and to apply the affirmative measure to cause an affirmative entropy between the at least one among the at least one non-affirmative semantic or the at least oneaffirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

217. The smart device system of claim 216, wherein the system is configured to determine that the entropy between the at least one affirmative semantic and subsequent inferred semantics, based on the one or more inputs from the at least one sensor, is within a likeable interval.

218. The smart device system of claim 217, wherein the likeable interval is associated with a semantic time.

219. The smart device system of claim 217, wherein the likeable interval is associated with an affirmative semantic.

220. The smart device system of claim 212, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on web content parsing.

221. The smart device system of claim 212, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on an operating manual parsing.

222. The smart device system of claim 212, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a third activity.

223. The smart device system of claim 222, wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic comprises an activity semantic.

224. The smart device system of claim 212, wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of an intrinsic orientation.

225. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one processor and at least one sensor; the memory further storing at least one affirmative semantic and at least one non-affirmative semantic; wherein at least one among the at least one affirmative semantic and the at least one non- affirmative semantic is indicative of a first activity;a computer program being configured to cause the at least one processor to infer a first semantic and a second semantic based on one or more inputs from the at least one sensor; wherein the system is configured to generate semantic augmentation based on a determination that the first inferred semantic is affirmative by semantically matching the first inferred semantic with the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is non-affirmative by semantically matching the second inferred semantic with the at least one non-affirmative semantic at a second time, wherein the semantic augmentation is directed to the first user based on the first identity; and wherein the second inferred semantic comprises a second activity and a determination that the second inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.

226. The smart device system of claim 225, wherein: the memory further stores the at least one affirmative semantic in association with an object of an object type; and the system is configured to generate semantic augmentation based on a determination that a third inferred semantic is affirmative by semantically matching the third inferred semantic with the at least one affirmative semantic at a third time.

227. The smart device system of claim 225, further comprising at least one wireless transceiver wherein a plurality of interests is received via the at least one wireless transceiver.

228. The smart device system of claim 225, wherein the at least one among the first time and second time is a semantic time.

229. The smart device system of claim 225, wherein the interests are published in association with a first semantic flux operated by the first user.

230. The smart device system of claim 225, wherein the capabilities are published in association with a semantic flux.

231. The smart device system of claim 225, wherein the interests are being discovered from an operating manual.

232. The smart device system of claim 225, wherein the interests are being discovered from an image.

233. The smart device system of claim 225, wherein the interests are being discovered from provider content.

234. The smart device system of claim 225, wherein the capabilities are associated with a semantic group of sensors.

235. The smart device system of claim 225, wherein the system is configured to infer a countermeasure and to apply the counter-measure to reduce the high entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

236. The smart device system of claim 225, wherein the system is configured to infer an affirmative measure and to apply the affirmative measure to cause an affirmative entropy between the at least one among the at least one non-affirmative semantic or the at least one affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

237. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one sensor; at least one wireless transceiver; the memory further storing a plurality of interests received via the at least one wireless transceiver; the memory further storing a plurality of capabilities associated with at least one sensor; wherein the capabilities are published in association with at least two semantic fluxes; wherein each semantic flux among the at least two semantic fluxes is associated with a semantic group of sensors from among the at least one sensor; and at least one processor and a computer program operable by the at least one processor to cause the at least one processor to match the plurality of interests with the plurality of capabilities based on semantic matching; wherein the system is configured to generate semantic augmentation based on a determination that the plurality of interests and capabilities are affirmatively matching at a first time and that the plurality of interests and capabilities are non-affirmativelymatching at a second time, wherein the semantic augmentation is directed to the assigned first user based on the first identity.

238. The smart device system of claim 237 wherein the system is configured to infer a countermeasure and to apply the counter-measure to reduce the high entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor239. The smart device system of claim 237, wherein the system is configured to infer a measure and to apply the measure to cause a likeable entropy between the at least one among the at least one non-affirmative semantic or the at least one affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

240. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one processor and at least one sensor; the memory further storing at least one affirmative semantic and at least one non-affirmative semantic; a computer program configured to cause the at least one processor to infer a first semantic and a second semantic based on one or more inputs from the at least one sensor; and wherein the system is configured to generate semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time, and further based on a determination that a third inferred semantic is non-affirmative by semantically matching the third inferred semantic with the at least one non-affirmative semantic at a third time, wherein the semantic augmentation is directed to the first user based on the first identity.

241. The smart device system of claim 240, wherein the system is configured to infer an affirmative measure and to apply the affirmative measure to cause an affirmative entropy between the at least one among the at least one non-affirmative semantic or the at least oneaffirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

242. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one processor and at least one sensor; the memory further storing at least one affirmative semantic and at least one non-affirmative semantic; a computer program configured to cause the at least one processor to infer a first semantic and a second semantic based on one or more inputs from the at least one sensor; wherein the system is configured to generate semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time, wherein the semantic augmentation is directed to the first user based on the first identity; and wherein the system is configured to infer a counter-measure and to apply the countermeasure to reduce the entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

243. The smart device system of claim 242, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is configured by the first user.

244. The smart device system of claim 242, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is associated with a semantic time.

245. The smart device system of claim 242, wherein the system is configured to infer a countermeasure and applies the counter-measure to reduce the entropy between the at least one non- affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor.

246. The smart device system of claim 242, wherein the system is configured to infer an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one affirmative semantic and subsequent inferred semantics based on the one or more inputs from the at least one sensor.

247. The smart device system of claim 246, wherein the system is configured to determine that the entropy between the at least one affirmative semantic and subsequent inferred semantics based on the one or more inputs from the at least one sensor is within a likeable interval.

248. The smart device system of claim 247, wherein the likeable interval is associated with a semantic time.

249. The smart device system of claim 247, wherein the likeable interval is associated with an affirmative semantic.

250. The smart device system of claim 242, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on discovered content parsing.

251. The smart device system of claim 242, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first activity.

252. The smart device system of claim 251, wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic comprises an activity semantic.

253. The smart device system of claim 251, wherein the first inferred semantic comprises a second activity and the determination that the first inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.

254. The smart device system of claim 242, wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of an intrinsic orientation.

255. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one processor and at least one sensor;the memory further storing at least one affirmative semantic and at least one non-affirmative semantic; a computer program configured to cause the at least one processor to infer a first semantic and a second semantic based on one or more inputs from the at least one sensor; wherein the system is configured to generate semantic augmentation based on a determination that the first inferred semantic is affirmative by semantically matching the first inferred semantic with the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is non-affirmative by semantically matching the second inferred semantic with the at least one non-affirmative semantic at a second time, wherein the semantic augmentation is directed to the first user based on the first identity; and wherein the system is configured to infer a counter-measure and to apply the countermeasure to reduce the entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

256. The smart device system of claim 255, wherein: the memory further stores the at least one affirmative semantic in association with an object of an object type; and wherein the system is configured to generate semantic augmentation based on a determination that a third inferred semantic is affirmative by semantically matching the third inferred semantic with the at least one affirmative semantic at a third time.

257. The smart device system of claim 256, wherein the system generates semantic augmentation based on a determination that the second inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic.

258. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one processor and at least one sensor; the memory further storing at least one affirmative semantic and at least one non-affirmative semantic; a computer program configured to cause the at least one processor to infer a first semantic and a second semantic based on one or more inputs from the at least one sensor;wherein the system is configured to generate semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time, wherein the semantic augmentation is directed to the first user based on the first identity; and wherein the system is configured to infer an affirmative measure and to apply the affirmative measure to cause an affirmative entropy between the at least one among the at least one affirmative semantic or the at least non-affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

259. The smart device system of claim 258, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is configured by the first user.

260. The smart device system of claim 258, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is associated with a semantic time.

261. The smart device system of claim 258, wherein the system is configured to infer a countermeasure and applies the counter-measure to reduce the entropy between the at least one non- affirmative semantic and subsequent inferred semantics based on the one or more inputs from the at least one sensor.

262. The smart device system of claim 258, wherein the system determines that the entropy between the at least one affirmative semantic and subsequent inferred semantics based on the one or more inputs from the at least one sensor is within a likeable interval.

263. The smart device system of claim 262, wherein the likeable interval is associated with a semantic interval.

264. The smart device system of claim 262, wherein the likeable interval is associated with an affirmative semantic.

265. The smart device system of claim 258, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on discovered content parsing.

266. The smart device system of claim 258, wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first activity.

267. The smart device system of claim 266, wherein the first inferred semantic comprises a second activity and the determination that the first inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.

268. The smart device system of claim 266, wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of an intrinsic orientation.

269. A smart device system, comprising: a memory storing a first identity of a first user of the smart device system; at least one processor and at least one sensor; the memory further storing at least one affirmative semantic and at least one non-affirmative semantic; a computer program configured to cause the at least one processor to infer a first semantic and a second semantic based on the one or more inputs from the at least one sensor; wherein the system is configured to generate semantic augmentation based on a determination that the first inferred semantic is affirmative by semantically matching the first inferred semantic with the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is non-affirmative by semantically matching the second inferred semantic with the at least one non-affirmative semantic at a second time, wherein the semantic augmentation is directed to the first user based on the first identity; and wherein the system is configured to infer an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one among the at least one affirmative semantic or the at least non-affirmative semantic in rapport with subsequent inferred semantics based on the one or more inputs from the at least one sensor.

270. The smart device system of claim 269, wherein: the memory further stores the at least one affirmative semantic in association with an object of an object type; andwherein the system generates semantic augmentation based on a determination that a third inferred semantic is affirmative by semantically matching the third inferred semantic with the at least one affirmative semantic at a third time.

271. The smart device system of claim 270, wherein the system generates semantic augmentation based on a determination that the second inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic.

272. A semantic robotic device, comprising: a processor, a memory and at least one transceiver; the memory storing a plurality of semantics; the memory storing a plurality of endpoints; each of the endpoints being associated with at least one semantic among the plurality of semantics; the memory further storing at least one semantic goal; the processor being configured to: infer a first semantic group comprising a first subset of endpoints among the plurality of endpoints, the first semantic group being inferred based on a determination of an affirmative semantic resonance between each member of the first semantic group and the at least one semantic goal; infer a second semantic group comprising a second subset of endpoints among the plurality of endpoints, the second semantic group being inferred based on a nonaffirmative semantic resonance between each member of the second semantic group and the at least one semantic goal; and present a representation of the first semantic group and the second semantic on a display.

273. The semantic robotic device of claim 272, wherein the at least one semantic goal is indicative of an intrinsic first user goal.

274. The semantic robotic device of claim 272, wherein the at least one semantic goal is indicative of an observer goal.

275. The semantic robotic device of claim 272, wherein the at least one semantic goal is inferred based on the user guide.

276. The semantic robotic device of claim 275, wherein the user guide content is associated with a programming schedule.

277. The semantic robotic device of claim 275, wherein the user guide content is associated with a manual.

278. The semantic robotic device of claim 272, wherein the processor is further configured to factorize a first semantic goal from among the at least one semantic goal in rapport with a second semantic goal from among the at least one semantic goal.

279. The semantic robotic device of claim 272, wherein the processor is further configured to factorize a leadership of a first semantic goal from among the at least one semantic goal and a second semantic goal from among the at least one semantic goal in rapport with a first subset of semantics among the plurality of semantics.

280. The semantic robotic device of claim 279, wherein the first subset of semantics are associated with the first subset of endpoints among the plurality of endpoints.

281. The semantic robotic device of claim 280, wherein a second subset of semantics are associated with the second subset of endpoints among the plurality of endpoints.

282. A semantic robotic device, comprising: a processor, a memory and at least one transceiver; the memory storing a plurality of semantics; the memory storing a plurality of endpoints; each endpoint being associated with at least one semantic among the plurality of semantics; the memory storing a user interface control; the memory storing at least one semantic goal; the processor being configured to: infer a first semantic group comprising a first subset of endpoints among the plurality of endpoints, the first semantic group being inferred via a semantic analysis which applies a semantic drift to determine affirmative semantic resonance between each member of the first semantic group and the at least one semantic goal; infer a second semantic group comprising a second subset of endpoints among the plurality of endpoints, the second semantic group being inferred via a semantic analysis which applies a high entropy semantic drift to determining a non-affirmative semantic resonance between each member of the second semantic group and the at least one semantic goal;select the at least one user interface control based on a first semantic from among the plurality of semantics, the first semantic being associated with the first semantic group; and send data associated with the first semantic group to the at least one user interface control for display.

283. The semantic robotic device of claim 282, wherein the at least one semantic goal is indicative of an intrinsic first user goal.

284. The semantic robotic device of claim 282, wherein the at least one semantic goal is indicative of an observer goal.

285. The semantic robotic device of claim 282, wherein the at least one semantic goal is inferred based on the user guide.

286. The semantic robotic device of claim 285, wherein the user guide content is associated with a programming schedule.

287. The semantic robotic device of claim 285, wherein the user guide content is associated with a manual.

288. The semantic robotic device of claim 282, wherein the processor is configured to factorize a first semantic goal from among the at least one semantic goal in rapport with a second semantic goal from among the at least one semantic goal.

289. The semantic robotic device of claim 282, wherein the processor is configured to factorize a leadership of a first semantic goal from among the at least one semantic goal and a second semantic goal from among the at least one semantic goal in rapport with a first subset of semantics among the plurality of semantics.

290. The semantic robotic device of claim 289, wherein the first subset of semantics is associated with the first subset of endpoints among the plurality of endpoints.

291. The semantic robotic device of claim 289, wherein the second subset of semantics are associated with the second subset of endpoints among the plurality of endpoints.

292. A semantic robotic device, comprising: a processor, a memory and at least one transceiver; the memory storing a plurality of semantics; the memory storing a plurality of endpoints; each endpoint being associated with at least one semantic among the plurality of semantics;the memory storing a user interface control; the memory further storing at least one semantic goal; the processor being configured to: infer a first semantic group comprising a first subset of endpoints among the plurality of endpoints, the first semantic group being inferred based on a semantic analysis which applies a semantic drift to determine affirmative semantic resonance between each member of the first semantic group and the at least one semantic goal; infer a second semantic group comprising a second subset of endpoints among the plurality of endpoints, the second semantic group being inferred based on a semantic analysis which applies a high entropy semantic drift to determining a non-affirmative semantic resonance between each member of the second semantic group and the at least one semantic goal; select the at least one user interface control based on a first semantic from among the plurality of semantics, the first semantic being associated with the second semantic group; and send data associated with the second semantic group to the at least one user interface control for display.

293. The semantic robotic device of claim 293, wherein the at least one semantic goal is indicative of an intrinsic first user goal.

294. The semantic robotic device of claim 292, wherein the at least one semantic goal is indicative of an observer goal.

295. The semantic robotic device of claim 292, wherein the at least one semantic goal is inferred based on the user guide.

296. The semantic robotic device of claim 295, wherein the user guide content is associated with a programming schedule.

297. The semantic robotic device of claim 295, wherein the user guide content is associated with a manual.

298. The semantic robotic device of claim 292, wherein the processor is configured to factorize a first semantic goal from among the at least one semantic goal in rapport with a second semantic goal from among the at least one semantic goal.

299. The semantic robotic device of claim 292, wherein the processor is configured to factorize a leadership of a first semantic goal from among the at least one semantic goal and a second semantic goal from among the at least one semantic goal in rapport with a first subset of semantics among the plurality of semantics.

300. The semantic robotic device of claim 299, wherein the first subset of semantics is associated with the first subset of endpoints among the plurality of endpoints.

301. The semantic robotic device of claim 300, wherein the second subset of semantics is associated with the second subset of endpoints among the plurality of endpoints.

302. A semantic robotic device, comprising: a processor, a memory and at least one transceiver; the memory storing a first software application and a second software application; the memory further storing at least one semantic goal; the semantic robotic device being arranged to receive a video signal from a target device via the at least one transceiver; the processor being configured to: ingest first user guidelines content for the first software application and second user guidelines content for the second software application, the ingested first user guidelines content being associated with the first software application and the ingested second user guidelines content being associated with the second software application; infer first software application capabilities semantics associated with the first software application and second software application capabilities semantics associated with the second software application, the inference of each of the first software application capabilities semantics and the second software application capabilities semantics being based on semantic analysis which applies at least one affirmative semantic factorization projection in rapport with the at least one semantic goal; emulate first peripheral input device signals, and communicate the first peripheral input device signals to the target device via the at least one transceiver to launch and operate the first software application and the second software application; capture from the video signal a first data, the first data being displayed on a user first interface control by the first software application;capture from the video signal a second data, the second data being displayed on a second user interface control by the second software application; perform semantic inference on the first data to determine a first semantic factorization with respect to the at least one semantic goal; perform semantic inference on the second data to determine a second semantic factorization with respect to the at least one semantic goal; determine, based on a comparison of the first semantic factorization and the second semantic factorization, that the first software application capabilities semantics are more affirmatively factorized in rapport with the at least one semantic goal than the second software application capabilities semantics; and emulate further peripheral input device signals, and communicate the further peripheral input device signals to the target device via the at least one transceiver to cause the manipulation of the first user interface control, the emulated further peripheral input device signals being based on the determination.

303. The semantic robotic device of claim 302, wherein the at least one semantic goal is indicative of an intrinsic user goal.

304. The semantic robotic device of claim 302, wherein the at least one semantic goal is indicative of an observer goal.

305. The semantic robotic device of claim 302, wherein the at least one semantic goal is inferred based on the user guidelines.

306. The semantic robotic device of claim 302, wherein an input device descriptor is indicative of a peripheral input device.

307. The semantic robotic emulation device of claim 306, wherein the robotic device operates software implementing a human device interface profile or function.

308. The semantic robotic device of claim 302, wherein the user guidelines content is associated with a programming schedule.

309. The semantic robotic device of claim 302, wherein the user guidelines content is associated with a synopsis.

310. The semantic robotic device of claim 309, wherein the at least one semantic goal includes a first semantic goal and a second semantic goal, and further wherein the processor factorizes the first semantic goal in rapport with the second semantic goal.

311. The semantic robotic device of claim 309, wherein the processor factorizes a leadership indicator of the first semantic goal and the second semantic goal.

312. The semantic robotic device of claim 309, wherein the processor factorizes a leadership of the first semantic goal and the second semantic goal in rapport with a first subset of semantics.

313. A semantic robotic device, comprising: at least one processor, a memory and at least one transceiver; the memory storing a first software application and a second software application; the memory further storing at least one semantic goal; the memory further storing a plurality of capabilities semantics; the at least one processor being configured to: ingest first user guidelines content for the first software application and second user guidelines content for the second software application; analyze the ingested first user guidelines content and the ingested second user guidelines content based on semantic analysis, the semantic analysis comprising a semantic factorization of a first subset of capabilities semantics among the plurality of capabilities semantics with respect to each of the first software application and the second software application, the step of analyzing further including inferring a first set of manipulation semantics associated with the first software application and a second set of manipulation semantics associated with the second software application; determine, based on the inferred first set of manipulation semantics and the inferred second set of manipulation semantics, that the first subset of capabilities semantics is more affirmative for the first software application than for the second software application in rapport with the at least one semantic goal; determine a first semantic route comprising a plurality of semantics among the first set of manipulation semantics to manipulate the first software application based on at least one affirmative semantic factorization projection in rapport with the at least one semantic goal; launch the first software application and manipulate first software application user interface controls based on the first semantic route; capture first data displayed by a user interface control by the first software application;based on semantic inference on the first data, refactorize the first subset of capabilities semantics of the first software application; determine a second semantic route comprising a plurality of semantics among the second set of manipulation semantics to manipulate the second software application based on at least one affirmative semantic factorization projection in rapport with the at least one semantic goal; and manipulate second software application user interface controls based on the second semantic route.

314. The semantic robotic device of claim 313, wherein the at least one semantic goal is indicative of an intrinsic user goal.

315. The semantic robotic device of claim 313, wherein the at least one semantic goal is inferred based on the user guidelines.

316. The semantic robotic device of claim 313, wherein the user guidelines content is associated with a programming schedule.

317. The semantic robotic device of claim 313, wherein the user guidelines content is associated with a manual.

318. The semantic robotic device of claim 313, wherein the at least one semantic goal includes a first semantic goal and a second semantic goal, and further wherein the processor factorizes the first semantic goal in rapport with the second semantic goal.

319. The semantic robotic device of claim 313, wherein the at least one semantic goal includes a first semantic goal and a second semantic goal, and further wherein the processor factorizes a leadership indicator of the first semantic goal and the second semantic goal.

320. The semantic robotic device of claim 313, wherein the at least one semantic goal includes a first semantic goal and a second semantic goal, and further wherein the processor factorizes a leadership of the first semantic goal and the second semantic goal in rapport with the first subset of semantics.

321. A semantic robotic device, comprising: at least one processor, a memory and at least one transceiver; the memory storing a first provider semantic identity and a second provider semantic identity; the memory further storing at least one semantic goal;the memory further storing a plurality of capabilities semantics; the at least one processor being configured to: connect via the at least one transceiver to a first provider service and a second provider service; capture first user interface data rendered by the at least one processor in a user interface control associated with the first provider service; capture second user interface data rendered by the at least one processor in a user interface control associated with the second provider service; based on semantic factorization applied on the first user interface data and the second interface data, determine that a first subset of semantics among the plurality of capabilities semantics are more affirmatively factorized for the first provider service than for the second provider service in rapport with the at least one semantic goal; determine a first semantic route of manipulating the user interface controls of the first provider service based on at least one affirmative semantic factorization projection in rapport with the at least one semantic goal; manipulate the user interface controls of the first provider service based on the first semantic route; capture third user interface data rendered by the at least one processor in the user interface control associated with the first provider service; based on semantic analysis applied to the third user interface data, determine that the first subset of semantics are more affirmatively factorized for the second provider service than for the first provider service in rapport with the at least one semantic goal; determine a second semantic route of manipulating the user interface controls of the second provider service based on at least one affirmative semantic factorization projection in rapport with the at least one semantic goal; and manipulate the user interface controls of the second provider service based on the second semantic route.

322. The semantic robotic device of claim 321, wherein the system applies an upscaling transformation to the first user interface data, the second user interface data, and the third user interface data.

323. The semantic robotic device of claim 321, wherein at least one among the first user interface data, the second user interface data, and the third user interface data is widget data.

324. The semantic robotic device of claim 321, wherein the at least one semantic goal includes a first semantic goal and a second semantic goal, and further wherein the processor factorizes the first semantic goal in rapport with the second semantic goal.

325. The semantic robotic device of claim 321, wherein the at least one semantic goal includes a first semantic goal and a second semantic goal, and further wherein the processor factorizes a leadership indicator of the first semantic goal and the second semantic goal.

326. The semantic robotic device of claim 321, wherein the at least one semantic goal includes a first semantic goal and a second semantic goal, and further wherein the processor factorizes a leadership of the first semantic goal and the second semantic goal in rapport with the first subset of semantics.

327. The semantic robotic device of claim 321, wherein the at least one semantic goal is indicative of a user goal.

328. The semantic robotic device of claim 321, wherein at least one among the first user interface data, the second user interface data, and the third user interface data is rendered by the first processor in a memory.

329. The semantic robotic device of claim 321, wherein at least one among the first user interface data, the second user interface data, and the third user interface data is rendered by the first processor for displaying.

330. The semantic robotic device of claim 321, wherein the user guidelines content is associated with a programming schedule.

331. The semantic robotic device of claim 321, wherein the user guidelines content is associated with a synopsis.

332. A securable robotic controller for manipulating a controlled device having a physical switch, the securable robotic controller comprising: a first sensor; at least one actuated switch; a fastener; a user interface having a status indicator; andone or more processors in communication with a memory, the memory containing stored program instructions configured to cause the one or more processors to: determine, based on one or more inputs from the first sensor, that the securable robotic controller is secured to the controlled device through attachment via the fastener; cause the at least one actuated switch to physically interact with the physical switch, whereby a first movement of the at least one actuated switch causes the physical switch to achieve a first state at a first time, and a second movement of the at least one actuated switch causes the physical switch to achieve a second state at a second time; and present an updated status indicator, the updated status indicator including an indication of the state of the physical switch.

333. The securable robotic controller of claim 332, wherein the memory further contains stored program instructions configured to cause the one or more processors to determine, based on the one or more inputs from the first sensor, that the securable robotic controller is only partially secured to the controlled device, wherein the partial securement of the robotic device to the controlled device permits less than complete control of the physical switch by the robotic controller.

334. The securable robotic controller of claim 332, wherein the memory further contains stored program instructions configured to cause the one or more processors to determine, based on the one or more inputs from the first sensor, that the securable robotic controller is non- affirmatively attached to the controlled device.

335. The securable robotic controller of claim 334, wherein the memory further contains stored program instructions configured to cause the one or more processors to determine, based on the one or more inputs from the first sensor, that the securable robotic controller is affirmatively attached to the controlled device.

336. The securable robotic controller of claim 332, wherein the user interface comprises a light emitting indicator and the presentation of the updated status indicator comprises adjusting the light emitting indicator.

337. The securable robotic controller of claim 332, wherein the user interface comprises a screen.

338. The securable robotic controller of claim 332, wherein the user interface comprises a speaker.

339. The securable robotic controller of claim 332, wherein the securable robotic controller comprises a concealable hand crank, the hand crank being engaged to generate an electrical current.

340. A securable robotic controller for manipulating a controlled device having a physical control, the securable robotic controller comprising: a first sensor; at least one actuated link; a fastener; a status indication interface for presenting a status indicator; and one or more processors in communication with a memory, the memory containing stored program instructions configured to cause the one or more processors to: determine, based on inputs from the first sensor that the securable robotic controller is fastened, through the fastener, to the controlled device; cause the actuated link to physically interact with the physical control, whereby a first movement of the actuated link and a first manipulation of the physical control causes the controlled device to achieve a first state at a first time, and a second movement of the actuated link and a second manipulation of the physical control causes the controlled device to achieve a second state at a second time; and present an updated status indicator, the updated status indicator including an indication of the state of the controlled device.

341. The securable robotic controller of claim 340, wherein the controlled device is a lock.

342. The securable robotic controller of claim 340, wherein the controlled device is a remote control.

343. The securable robotic controller of claim 340, wherein the controlled device is a computer peripheral device.

344. The securable robotic controller of claim 340, wherein the controlled device is a switch.

345. The securable robotic controller of claim 340, wherein the controlled device is an appliance.

346. The securable robotic controller of claim 345, wherein the physical control is comprised by an appliance control module.

347. The securable robotic controller of claim 340, wherein the controlled device is an appliance control device.

348. The securable robotic controller of claim 340, wherein the controlled device is an electrical control panel.

349. The securable robotic controller of claim 340, wherein the physical control is a lever.

350. The securable robotic controller of claim 340, wherein the physical control is a knob.

351. The securable robotic controller of claim 340, wherein the physical control is a button.

352. The securable robotic controller of claim 340, wherein the physical control is a handle.

353. The securable robotic controller of claim 340, wherein the physical control is a key.

354. The securable robotic controller of claim 340, wherein the fastener is magnetic.

355. The securable robotic controller of claim 340, wherein the fastener comprises a pod.

356. The securable robotic controller of claim 340, wherein the fastener comprises a fastening band.

357. The securable robotic controller of claim 340, wherein the fastener is adjustable.

358. The securable robotic controller of claim 340, wherein the status indication interface comprises a light emitting indicator and the presentation of the updated status indicator comprises adjusting the light emitting indicator.

359. The securable robotic controller of claim 340, wherein the status indication interface comprises a screen.

360. The securable robotic controller of claim 340, wherein the status indication interface comprises a speaker.

361. The securable robotic controller of claim 340, wherein the securable robotic controller comprises a concealable hand crank, the hand crank being engaged to generate an electrical current.

362. A robotic emulation device, comprising: a processor, a memory and at least one transceiver; the robotic emulation device being arranged to receive a video signal from a target device via the at least one transceiver;the robotic emulation device further being arranged to transmit electric signals to the target device via the at least one transceiver; the memory storing an associated input device identifier which is indicative of an emulation of a peripheral input device by the robotic emulation device; the processor being configured to cause the robotic emulation device to interact with a target user interface control on a first user interface on the target device by (1) interpreting the video signal received via the at least one transceiver, wherein the interpretation includes identifying a target interface control associated with the first user interface, the target interface control being encoded in the video signal, (2) determining manipulation data associated to the target interface control, (3) forming, based on the manipulation data, one or more encoded inputs related to the emulation of the peripheral input device by the robotic emulation device; and (4) transmitting one or more manipulation data signals to the target device via the at least one transceiver, the one or more manipulation data signals relaying the one or more encoded inputs to the target device.

363. The robotic emulation device of claim 362, wherein the robotic emulation device emulates a computer keyboard or mouse.

364. The robotic emulation device of claim 362, wherein the robotic emulation device operates software implementing a human interface device profile.

365. The robotic emulation device of claim 362, wherein at least one among the target device or the robotic emulation device is communicatively coupled with at least one computer host or tenant, wherein the robotic emulation device manipulates the target device user interface to manage and configure computer hosts, tenants, virtual machines, communication, resources, tasks, resource allocations, responses or flux infrastructure comprising the least one computer host or tenant.

366. The robotic emulation device of claim 362, wherein the target interface control is associated with an interest semantic.

367. The robotic emulation device of claim 362, wherein the manipulation data is inferred and applied based on interpreting video signals encoding pixels associated with a digital representation of a user agenda or task.

368. The robotic emulation device of claim 362, wherein the manipulation data is inferred and applied based on inputs from a sensor capturing data associated with a rendering of a user agenda or task.

369. The robotic emulation device of claim 362, wherein the manipulation data is determined based on interpreting video signals encoding a digital representation of an agenda or task management application output.

370. The robotic emulation device of claim 362, wherein the robotic emulation device is communicatively coupled with at least one computer host or tenant, wherein the robotic emulation device communicates imaging data encoded in the video signal with the at least one computer host or tenant.

371. The robotic emulation device of claim 362, wherein the target interface control is determined based on a first inferred semantic and further based on semantic drift between the first inferred semantic and a semantic associated with the target interface control.

372. The robotic emulation device of claim 371, wherein the target interface control is determined based on an interest semantic.

373. The robotic emulation device of claim 372, wherein the target interface control is associated with an inferred semantic identity or activity, wherein the semantic identity or activity is inferred and associated with the target interface control based on video signal analysis, wherein the target interface control is determined based on a semantic drift between the semantic identity or activity and the interest semantic.

374. The robotic emulation device of claim 362, wherein the target interface control is associated with an inferred semantic identity or activity, wherein the semantic identity or activity is inferred and associated with the target interface control based on a document analysis.

375. The robotic emulation device of claim 374, wherein the document analysis is based on launching a document viewing application on the target device and acquiring and analyzing video signals from the target device comprising the document viewing application user interface.

376. The robotic emulation device of claim 3774, wherein the document analysis is based on capturing optical sensor inputs comprising renderings of pages of the document and analyzing the optical sensor inputs.

377. The robotic emulation device of claim 374, wherein the document analysis is based on downloading and analyzing the document data from a web server.

378. The robotic emulation device of claim 374, wherein the document analysis is based on copying in memory the document data from the target device.

379. The robotic emulation device of claim 362, wherein robotic emulation device is communicatively coupled with at least one computer host or tenant, and wherein the robotic emulation device is configured to forward video data associated with the received video signal to the at least one computer host or tenant, the at least one computer host or tenant applying image analysis to the video data to determine user interface control semantics and to return user interface control semantics signals to the robotic emulation device to cause manipulation action signals inferences by the robotic emulation device.

380. The robotic emulation device of claim 362, wherein the processor is being further configured to determine content associated to the target user interface control and forming one or more encoded inputs related to the emulation of the peripheral input device by the robotic emulation device, and to provide the one or more encoded inputs to the target device via the at least one transceiver.

381. The robotic emulation device of claim 380, wherein the encoded inputs cause a data download at the target device.

382. The robotic emulation device of claim 380, wherein the encoded inputs cause a data upload at the target device.

383. The robotic emulation device of claim 380, wherein the encoded inputs cause data inputting at the target device.

384. The robotic emulation device of claim 362, wherein the manipulation data is determined based on an inferred first semantic at an endpoint, the first semantic being inferred based on an input from a sensor.

385. The robotic emulation device of claim 384, wherein the manipulation data is further determined based on a semantic matching between the first semantic and a second semantic comprised in a user preference or profile.

386. The robotic emulation device of claim 362, wherein the target user interface control is determined based on an inferred first semantic at an endpoint, the first semantic being inferred based on an input from a sensor.

387. The robotic emulation device of claim 362, wherein the target user interface control is further determined based on a semantic matching between the first semantic and a second semantic comprised in a user preference or profile.

388. The robotic emulation device of claim 362, wherein the manipulation data or the target user interface control is further determined based on an indication from a user.

389. The robotic emulation device of claim 362, wherein the input device identifier is stored as a device descriptor.

390. The robotic emulation device of claim 362, wherein the robotic emulation device having an associated peripheral device designation and wherein the processor is further being configured to: determine one or more manipulation actions associated to the target interface control, and form and transmit one or more manipulation action signals to the target device via the at least one transceiver, the one or more manipulation action signals being based on the peripheral device designation and including key codes associated with an emulated performance of the one or more manipulation actions by the robotic emulation device.

391. The robotic emulation device of claim 362, wherein the robotic emulation device comprises an output port, wherein the robotic emulation device outputs augmented data signals at the output port.

392. A robotic control system, comprising: one or more processors and a memory; the memory storing a plurality of inference models and a plurality of capabilities semantics associated with the plurality of inference models; the one or more processors being configured to launch and operate a subset of inference models among the plurality of inference models; the one or more processors further being configured to perform semantic inference by: applying the subset of inference models on an input data and, aggregating the outputs resulted from applying each inference model among the subset of inference models to the input data based on the plurality of capabilities semantics.

393. The robotic control system of claim 392, wherein the robotic inference computer comprises a plurality of sensors and a plurality of augmentation capabilities.

394. A smart device system comprising: a memory, one or more runtimes;the memory storing a plurality of inference models and a plurality of capabilities semantics associated with the plurality of inference models; the smart device system being configured to select, launch and operate a subset of inference models among the plurality of inference models on the one or more runtimes and to perform semantic inference by: applying the subset of inference models on an input data and, aggregating the outputs resulted from applying each inference model among the subset of inference models to the input data based on the plurality of capabilities semantics.

395. The smart device system of claim 394, wherein the smart device system comprises a plurality of sensors and a plurality of augmentation capabilities.

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