Computer-implemented network node point and computer-implemented method for linking different input devices, computer program product and computer-readable storage medium

Abstract

The invention relates to a computer-implemented network node point and a method for linking different input devices, which are used, for example, during the converging of different user inputs of a virtual meeting and / or in a prototyping phase of a new product, in particular an Internet-based product, at the same time and / or for solving a problem by joint design, planning, describing, by testing and / or by means of a shared file and / or a shared virtual workspace. By means of the invention, it is possible for the first time that different participants, different devices within a virtual project space can collaborate via a network node point without trouble, in the sense of "without compatibility problems", i.e. irrespective of, for example, abilities, language, operating systems of the input and / or output devices, simultaneously and without losses due to data collision.

Description

[0001] Description

[0002] Computer-implemented network node and computer-implemented method for linking various input devices, computer program product and computer-readable storage medium

[0003] The invention relates to a computer-implemented network node and method for linking different input devices, such as those used, for example, when different user inputs come together in a virtual meeting and / or in the prototyping phase of a new product, in particular an Internet-based product, simultaneously and / or to solve a task by joint design, planning, describing, trying out and / or by means of a shared file and / or a shared virtual workstation.

[0004] More and more often, users of different input devices work together digitally, so that they communicate via connections - for example in a star topology - and / or access one and the same storage, processor and / or output device, which is at the center of the collaboration, for example a prototype of a new product and / or a concept board on which everyone is working together.

[0005] For example, several users work together during a virtual meeting using their own input devices, with particular preference given to collaborative work using a shared file, e.g. a concept board, in such a way that all members design and / or access the concept board together and / or simultaneously.

[0006] To date, user input in these cases, for example, to a concept board accessible from multiple computers, a table, a file, and / or a voice output based on chronology, has generally been processed and / or handled according to the time of the user input. Another method for processing signals that simultaneously access the same file is to manually prioritize the computers via which user input is made, so that user input from computer A is treated and / or forwarded with a different priority than user input from computer M.

[0007] No matter how, there is always a rule by which, when several user inputs converge and / or when several signals and / or data packets meet at the same time at a network node or network node, which is for example connected upstream of the shared computer-based device, a kind of priority regulation is made for processing which input is processed and / or forwarded with which priority.

[0008] The current state of the art is to set the order either manually and / or chronologically. The disadvantages of manual setting are the procedural step that requires a user to think through the matter and configure the system accordingly, and above all, the time required and the rigidity, because prioritizations are not automatically and flexibly adapted to the current situation, but can only be adjusted laboriously and manually.

[0009] Aside from these rules, technical stumbling blocks sometimes slow down the confluence of multiple user inputs, for example because of a lack of compatibility between the individual devices. This is particularly the case when signals are sent or received that are not easily transferable and / or acceptable, for example because operating systems and / or connections are based on user input and / or because the different technical capabilities, arrangements, commands, and signals of the individual interconnected computers are not coordinated. It may therefore happen that collaboration between multiple users across different computers is only possible after appropriate technical adaptation, possibly even after upgrading and / or training of the systems.

[0010] Particularly in MVR "Minimum Viable Product" testing, where prototypes are tried out and different variants are tested to determine the best and most user-friendly method and / or technical solution, it is becoming increasingly common for users to be slowed down by technical stumbling blocks - as described above - and to produce unusable results and / or to only achieve usable results after many different attempts, training sessions, updates and / or other attempts.

[0011] The disadvantage of a previously known network node is that it receives data and forwards it completely and chronologically to all connected devices. Simultaneous reception can therefore lead to collisions between data packets, which can cause performance problems in the network. It may therefore be useful to forward data packets with certain metadata preferentially and / or only to certain devices, which is not possible with the conventional, non-processing network nodes.

[0012] There is therefore a need for a solution to the problem described above of integrating different simultaneous and / or incompatible user inputs into a shared computerized device, in particular to a shared file, platform, tool and / or access and / or a processor shared between the users, wherein the user inputs originate from different, possibly incompatible, computerized devices.

[0013] The object of the invention is therefore to create a device and a method for integrating simultaneously occurring signals from different devices in a network, in particular at a time when several network participants make inputs and / or access one or more shared files, platforms, tools.This is especially true if the devices are not compatible with each other from the outset, so the object of the present invention is not to expect users to continue to familiarize themselves with all the technologies of all devices in the network and to have these available and / or to oblige all participants to use the same technology, but to let users continue to use a system that they are best at and still enable smooth collaboration or smooth interaction of user inputs in and on the shared and / or common device and / or file.

[0014] This object is achieved by the subject matter of the present invention as disclosed in the claims, the description and the figures.

[0015] Accordingly, the subject matter of the invention is a computer-implemented network node connecting several computer-aided devices in a communicating computer network in such a way that it receives, buffers, analyses, processes and / or optionally transforms and forwards data packets in a computer-aided manner, wherein the network node has a computer-aided prioritization and / or transformation module having the following components: a) at least one memory unit for buffering the data packet(s) of the connected computer-aided devices after their reception, b) at least one processor which is suitable for reading received data packets and / or their metadata, c) at least one connection to a neural network with an AI for capturing / analyzing and / or transforming and / or processing the data packet(s) before their forwarding and d) at least one processor which is suitableto apply the results of the prioritization and / or analysis of the data packets by the AI ​​to the forwarding, transformation and / or processing of the data packets to the storage, processor and / or output device shared by the network.

[0016] Furthermore, the subject of the invention is a computer-implemented method for the collision-free forwarding of several data packets from different computer-aided input devices connected to one another via a network node to one or more output devices also connected to the network node, comprising the following method steps:

[0017] - Recording each network participant - in short "participant" through an identity,

[0018] - Receipt of at least one input and / or command from one of the participants by the network node

[0019] - Recording and / or saving user inputs in the same project space of the network node

[0020] - Processing of the input and / or command by a processor of the network node that has an AI,

[0021] - Assigning and storing a priority for each network participant by processing the collected information using an AI and subsequently prioritizing each participant,

[0022] - Reading the inputs and / or commands of the participants and / or event lists of the feedback from the output devices,

[0023] - Analysis and prioritization of inputs and / or commands by artificial intelligence and / or processing of user inputs by AI

[0024] - Generating results by the AI ​​regarding prioritization of the inputs and / or commands of the respective participants and

[0025] - Forwarding of inputs and / or commands according to the results of the prioritization

[0026] - Capturing and / or reading the feedback and / or event lists received from the output device regarding the success of forwarding and executing the input and / or command by an output device and finally analyzing the feedback and checking the prioritization by the AI.

[0027] According to an advantageous embodiment of the method, the method also comprises the step of optionally classifying the data packets based on the metadata, such as data format, file format, program version, operating system and optionally automatic transformation, and / or update and / or transfer into a group and thereby improved classification into data packets that are better compatible with a computer-assisted, optionally shared device or an output device.

[0028] According to a further advantageous embodiment of the invention, the method also comprises classifying a participant into a group with regard to his role in the project and / or his location.

[0029] According to an advantageous embodiment of the method, the method also includes the step of visualizing the current prioritization.

[0030] The "prioritization and / or transformation module" of the network node is a collection of computer programs that manages the system resources of a network such as storage unit, hard disk(s), transmit and receive signals and / or a connection to a neural network with an AI and / or a connection to the IoT and makes them available to these application programs. The prioritization and / or transformation module therefore forms the interface between the hardware components and the application software of the network node. The tasks of a prioritization and / or transformation module are, for example: Controlling communication between the devices connected to the network node, both those connected in a star configuration and communication with the shared computer-based device.The technical solution of the present invention enables digital collaboration between multiple participants using their preferred devices without the additional effort of adapting inputs in different formats and / or incompatible data packets. Furthermore, this technology offers participants more options for user interaction. During a prototyping phase, e.g., when designing an MVP, existing interfaces can be reused instead of developing new ones for collaborative collaboration.

[0031] The interoperability of the devices is ensured because the AI, which is included in the network node, automatically downloads the latest updates and / or transformation programs from the Internet of Things and adapts the user data to the common device via these programs, thus ensuring compatibility.

[0032] Especially during the prototyping phase, development costs should be kept as low as possible to allow for the testing and implementation of alternatives. For this reason, participants typically stick with one device, one tool, and / or one program, such as Facebook, a specific data storage, one video collaboration tool, such as TEAMs, one tool for interacting with the design prototype, and so on. Through AI-driven transformation in the network hub, also called a "HUB," these tools can be retained without compatibility issues.

[0033] For example, users of different skill levels can work together without any problems because an appropriately trained AI compensates for certain skills that one or the other user lacks.

[0034] The terms "user" and "participant" differ in this case in that users, regardless of a project, and participants are always assigned to a common project. According to the current state of the art, user inputs are lost if, for example, the first or last user input overwrites all later or earlier inputs, chronologically according to the "FI FO" first-in-first-out technique or the "first-in-last-out" technique.

[0035] Through the present invention, the AI ​​integrated in the hub serves, for example, to compare, weigh, prioritize, rate the inputs according to user profile and / or to assign the reliability and / or hierarchy of the user to his input and then to prioritize the input accordingly.

[0036] It is useful, or even necessary, to continually update priorities, which is ensured automatically by intervening with AI. For example, when a project enters a new phase, when people or machines become inactive or less trustworthy for any reason. The AI ​​in the hub can replace a human administrator and simultaneously accelerate processes.

[0037] A hub according to one embodiment of the invention can also set up and define priority rules - e.g. which communication has priority over another - user communication, loading, executing, interrupting and / or terminating programs, management and allocation of processor time, management of internal memory, management and operation of connected devices, access restrictions and / or prioritization.

[0038] A network node connects devices to one another using a specific topology. Topology generally describes the structure of how nodes are connected to one another. There is both a physical topology, which is what we are talking about here, and a logical topology. Star topology is a type of network topology; it is characterized by the fact that all end devices are connected to the distributor or node, but the end devices are not connected to one another. Other common topologies include point-to-point topology and chain topology.

[0039] A "network node" is a connection and / or distribution point, a redistribution point and / or an end point in data transmission, for example. For example, a network node has the ability to recognize, process and / or forward transmissions for other network nodes. For example, a generic network node connects several computer-based devices in a star configuration on the one hand and at least one computer-based device shared in the network on the other.

[0040] A “network node” can be anything from a backbone, gateway, host computer, server, switch and / or hub.

[0041] "Access" refers to the ability to view, acquire and / or use a file, platform, or tool.

[0042] A "tool" refers to a tool and / or a utility program. In particular, a tool is a program that performs specific tasks within a larger software package. For example, an app is a tool. The difference between an app and a tool is that an app is a small program that can be used on smartphones and / or tablets with a limited user interface, whereas a tool can be a utility program on a smartphone and / or tablet as well as on a server and / or desktop.

[0043] A "hub" is a type of multiport repeater through which a signal from a network participant is transmitted, but not analyzed. The transmitted bit and / or symbol level is regenerated in the hub after each transmission. However, a hub does not contribute to collision avoidance, because all received bits / symbols are forwarded unfiltered and equally to all network participants. Via a hub, the data traffic between network participants can also be analyzed and / or recorded using network sniffer devices at each connection on the hub.

[0044] "AI" refers to an artificial intelligence based on a neural network, which in this case is trained to forward data packets with specific metadata via the connected processor for control purposes faster than others to the communicating computers of the computer network. According to the invention, the processing of the AI ​​can range from a simple prioritization of the data packets based on predetermined hierarchies, conversion of the data packets into system-compatible data packets, completion, correction, addition of the data packets

[0045] The memory module of a network hub according to the invention can, for example, be a simple RAM "Random Access Memory", because the storage of the data packets is only required intermediately until forwarding to the communicating computers.

[0046] The processor can advantageously have means for creating a protocol that can be accessed. The data from the processor's protocol can be used, for example, to train the AI.

[0047] Metadata or metainformation is structured data that contains information about characteristics of the data in a data block.

[0048] Preferably, the storage module of the network hub is configured to store the metadata for access by the processor and / or the connection to the AI.

[0049] The AI ​​can automatically prioritize based on the metadata when multiple data packets are received simultaneously. The control processor implements the prioritization as a result of the AI's processing of the metadata by forwarding the data packets from the memory.

[0050] Especially when several people collaborate on a project, they often work together across multiple devices, for example to jointly design, build and / or test an MVP.

[0051] Because different people's devices are provided with different tools from different vendors, they are often incompatible. This can lead to network problems and / or require some participants to undergo extensive training to ensure they can participate in the MVP on an equal footing.

[0052] The proposed integration of an AI into the network hub makes it possible for participants' training to become obsolete, because the AI ​​prepares and, if necessary, converts the data packets of each participant for the system. All "computer-implemented" devices are referred to as "computer-aided" here.

[0053] In the following, the invention is explained in more detail using a circuit diagram of an example of a preferred embodiment of the present invention.

[0054] Figure 1 shows the circuit diagram of the first example: According to this example, the user will first log in to a selected project area "Project 1 space" for collaboration, for example via its URL, which he specifies as an ID. The user receives this ID and / or project URL via invitation from the organizer. Examples of such space IDs in the form of a URL are: MS Teams meeting, WhatsApp group, Zoom meeting or Google Meet and / or similar tools.

[0055] Next, all users or participants in the project are assigned IDs that encode their nature (human, machine, or AI), and / or their location and / or their role. Furthermore, all participants are given an initial priority. The priority of all participants can initially be the same, different, or randomly assigned. For example, an AI can be given a low priority and / or AI-controlled devices a lower event priority. The initial priorities can be corrected, particularly by a human administrator. The data from the correction can then be used to train the AI ​​in the HUB. For example, a user in a leadership position receives a higher priority than a human user in a lower hierarchy.

[0056] The project participants collaborate by specifying their requests and inputs as commands, which they enter into the shared project space via their preferred devices with preferred interface applications. The interface hub according to the example of the present invention shown here forwards these commands or inputs to the actors or acting software applications connected in the same project space, as shown in Figure 1.

[0057] Figure 1 shows the network node 1 in the center. To the left of it, users 2a to 2c can be seen with their individual input devices 3a to 3c, such as desktop, mobile device, smartphone, tablet, VR "Virtual Reality" and / or AR "Augmented Reality" glasses, microphone, mouse, haptic input devices such as keyboard, touchscreen, joystick, remote control, audio input devices with or without speech recognition, and other devices well known to those skilled in the art, as well as any combinations of the aforementioned devices.

[0058] Alternatively or additionally, automated inputs such as acoustic inputs, light, temperature, magnetic, sensor inputs, inputs from measurements, and / or motion / position detection sensors can be provided with a corresponding connection to an interface at the network node. The participants' input devices and, if applicable, devices with corresponding sensors communicate with the network node 1 via suitable interfaces 4a, 4b, 4c.

[0059] Interfaces 4a, 4b, 4c are connection points between the information processing systems or system components that are related to one another, such as input devices 3a to 3c and network node 1, via which the exchange of data or control information takes place.

[0060] Suitable interfaces 4a to 4c are, for example, all types of GUIs, “Graphical User Interfaces”, programming interfaces, APIs “Application Programming Interfaces”, chat applications with or without automated chat bots, dashboards as physical devices and / or as SW-GUIs “Software Graphical User Interfaces” applications.

[0061] Via the above-mentioned devices 3a to 3c and the communication via the interfaces 4a to 4c, which may also include data from measurements and sensors, the users 2a to 2c provide inputs and / or commands, which are received project-related by the network node 1 via the corresponding URL.

[0062] According to the present exemplary embodiment of the invention, the network node 1 is equipped with a processor 5 which has an AI and optional access to the IoT and is configured to receive and process the inputs and / or commands. The processor 5 of the node 1 can prioritize the inputs and / or commands for the respective project, check them for reliability via the AI ​​and / or transform them so that the inputs and / or commands for a project are compatible with one another and / or with a selected output device 6a to 6h. The inputs from the users 2a to 2c which have been processed in this way, i.e. prioritized, classified as reliable and / or compatible, are forwarded to the output devices 6a to 6h via the network node 1.Participants in a project are all input and output devices that send input and / or feedback to the network node's processor via a common identification. For example, all participants have a common URL, the project URL. All participants in a project are located in a virtual project room, which also contains the task, the prototype, the concept board, etc., on which all participants work together and which is connected to network node 1 as "output device 6a to 6h."

[0063] To train the AI ​​connected to network node 1, project-related feedback signals 7a and 7b are sent from output devices 6a to 6h to processor 5 with connected AI. Via so-called feedback acquisition devices 7a, 7b, which may include sensors, cameras, analysis devices, measuring instruments, microphones, and / or scales, the AI ​​of network node 1 receives feedback or feedback on the success of the output device 6a to 6h in executing the input and forwarded command, which the AI ​​links to the respective participant 2a to 2c from whom the command or input came.

[0064] These feedback signals 7a and 7b contain information about the results of the inputs from users 2a to 2c that have been forwarded to output devices 6a to 6h. For example, the AI ​​is informed that an input from user 2a is useful or not useful for an action that is to be carried out by output device 6c, a robot, so that the AI ​​receives information about the reliability of user 2a, who can then be given a higher or lower priority or even blocked by the AI ​​or manually by an administrator if further inputs are made. In processor 5 of network node 1, all inputs and all information about the inputs are first collected, stored, and processed.

[0065] The processor 5 is connected to a prioritization and / or transformation module such as AI and / or interface to the IoT, which are integrally included in the network node 1.

[0066] Possible output interfaces and / or output actors that execute the input commands are: tools such as Robot 6c - both for "smart home" applications and industrial robots, visual representations such as TV, smart TV, monitor, printer, VR / AR glasses, headsets, with or without HUD "head-up displays", light signals, acoustic signals, vibration signals, as well as any combination of these devices.

[0067] Possibilities for visualizing the feedback 7a, 7b are, for example, screens, web-based within a web browser and / or displays, so-called "viewers" within applications, smart TV, VR / AR glasses, headsets or loudspeakers / soundbars for acoustic feedback, lights, LEDs, possibly mounted on dashboards and / or combined with acoustics.

[0068] Preferably, the event listener component of the processor is connected to the most common interfaces available on the Internet, such as SLACK®, Dropbox®, Airtable®, MQTT Servers®, ThingSpeak®, and / or other IoT interfaces. For example, it is also connected to social networks and / or voice assistants and uses the information thus obtained to process the inputs 4a to 4c.

[0069] Figure 2 shows the structure and / or architecture of a network node 10 according to an exemplary embodiment of the present invention. Network node 10 can be seen in the center through a frame, which could also be network node 1 in Figure 1. Figure 2 shows a schematic circuit diagram illustrating how the individual elements interlock in an exemplary network node 1 according to the invention and how the method according to an exemplary embodiment of the invention proceeds.

[0070] Within the framework of the network node 10, from bottom to top, one can first recognize process step 100, the so-called "project space definition," where the processor assigns the input to a project based on the URL of the received input. For example, the list of participants 101, the list of currently connected projects 102, and the list of currently connected interfaces 103 are used. The result of which project the input is to be assigned to is forwarded to the AI ​​110, the so-called "AI Decision Taker 110," in process step 110. The AI ​​110 also receives the results and information from the feedback analyzer 120, which—for example, referring to Figure 1—is communicatively connected to the output devices 6a and 6f, for the respective project.

[0071] From the "Event Listener 130" contained in network node 10, as shown in Figure 2, information from the interfaces to the IoT, as shown above, an MQTT Broker I 131 and an MQTT Broker II 132, a Social Network I API 133, a Database I 134, an IoT Platform I 135, a Social Network II API 136, and / or a data store 137 arrives at the AI ​​110 via an Input Command Mapping 140, which transforms the information from the Event Listener, classifies it, and makes it compatible with the AI. The "Input Command Mapping" process step is linked to access to a "list of commands" 141, which is naturally expandable, where, for example, participants with higher priority - visible or not - are higher up and different commands are stored depending on the project.The decisions of participants with lower priority can still be visualized in the user interface of the "list of commands" 141, partially transparently, where - e . g . - the transparency percentage corresponds to the priority . For digital twins or other visualization applications involved, e . g . in a prototyping process, the content of this specific user interface from the feedback analyzer with the current decisions can be integrated into these applications .

[0072] After the commands have been collected and prioritized, network node 1 or 10 sends the high-priority commands to the output interfaces connected to this project, such as 6a to 6h in Figure 1. An output interface can be connected to a visualization window of a web browser and / or a webcam and / or a telephone conference window. A user can select which output they want to observe from which end device, e.g. the screen of a desktop computer, laptop, tablet, smartphone, smartwatch, smart TV, VR / AR glasses or headset and / or audio output. The options for the output devices are only limited insofar as the output device is communicatively connected to network node 1 or 10 and to the respective project.

[0073] The AI ​​110 processes the data blocks with information and / or inputs and generates the result of the processing as output 111 to the corresponding devices 112, which may include, for example, imaging devices.

[0074] Devices can be logged on and off at the network node at any time.

[0075] The network node has the AI ​​110, which is configured once and then works without being influenced by the participant or project manager but is constantly trained and trained by the incoming results of the feedback analysis.

[0076] The process shown in Figure 2 is dynamic and runs in real time for the work on the project because the processing by the AI ​​110 is automatic and dynamic, as the AI ​​110 is constantly supplied with new feedback 120, which is then incorporated into the further processing of the inputs.

[0077] A network node 1 or 10 thus equipped according to an exemplary embodiment of the invention analyzes all inputs and the results of the feedback signals Va and 7b, which are received either via project participants and / or via machines and / or automatically via sensors in the feedback analyzer 120. In the process step "Feedback Analyzer" 120, a decision is then made regarding the trustworthiness and reliability of all participants, and the priorities of the inputs are automatically based on the current trustworthiness of the participants.

[0078] For example, a participant who is more actively involved in the collaboration receives a higher priority than other participants who are less involved.

[0079] In the process step "Feedback Analyzer" 120, all feedback from the resulting scene is collected. This includes automatically recorded sensory values ​​as well as views and data from connected cameras with, for example, gesture and facial expression recognition, inputs from participants via corresponding platforms and / or user interfaces. The AI ​​110 can exploit all possibilities here; ratings, chronology, frequencies, etc. can contribute to the result of the Feedback Analyzer 120.

[0080] For example, some devices can communicate synchronously with network node 1 or 10. In this case, for example, an input forwarded from network node 1 or 10 is processed and an "OK" or "Error" feedback is sent back to the feedback analyzer 120. This feedback is assigned to the device from which the input was made, so that the prioritization of the subscriber that used this device for the input can be adjusted. Some devices may not be able to respond immediately with feedback to a received command. This can happen in situations where the results depend on multiple actions from different subscribers or when a subscriber does not have the functionality to send feedback. Such situations can be detected by additional sensors, for example for safety reasons. This sensor can also measure ambient temperature, noise level, etc. using suitable devices.The AI ​​110 can then analyze all the information to learn which command sequences lead to "positive" and which to "negative" feedback.

[0081] Negative feedback is, for example, a signal automatically emitted by the output device, such as the robot, such as "error", "alarm", "fail", whereas positive feedback is expressed, for example, by an "okay", "passed", "done". The feedback analyzer 120 is used with<command_sequence , feedback> trained and learns to predict which command combinations can lead to "negative" or "positive" sensor values. At the same time, the AI ​​110 will also prioritize participants that produce a positive signal in the feedback analyzer 120.

[0082] According to a further embodiment of the invention, the feedback analyzer 120 has an additional user interface through which the results of the current prioritization are visualized and the participants are informed about the decisions and prioritizations. Users recognize this and can understand which participants are classified as unreliable by the AI ​​based on which commands, compare them with reality, and correct them if necessary. This also gives the participants the opportunity to improve the AI's automated decision, which can then be used as new input for training the AI.

[0083] There are many application examples, such as that of a makerspace for 3D printing and / or laser cutting, where the AI ​​120 in network node 1 or 10 can give the user valuable information about which settings produce better and worse results by checking the feedback of each individual input. For example, a transaction involving a participant's input to a model in a 3D printable model is evaluated with an OK as feedback. This setting is given high priority by the network node, which is also visible to the participant. Otherwise, the AI ​​of network node 1 or 10 sends back "error" as feedback if it detects that it cannot print this file.

[0084] The network node 1, 10 according to embodiments of the invention represents a verification tool in the process that collects, evaluates and analyzes all data, both the inputs of the participants and the feedback messages of the output devices.

[0085] Figure 3 shows a possible example of how the results of the AI ​​110 can be visualized to the participants.

[0086] It is clearly visible with a green tick on the one hand and a red cross on the other hand, how often a forwarding of an input 4a to 4c of a participant - i.e. in one line - according to the feedback analyzer 120 at the output device 6a to 6h led to success and how often not.

[0087] According to a preferred embodiment of the invention, the participant is then free to correct this result of the AI ​​110 from the data of the feedback analyzer 120 or to leave it as it is. The feedback analyzer receives positive and negative feedback from the output devices 6a to 6h.

[0088] The participants' inputs are continuously monitored by network node 1 or 10. The history of the monitoring results is also available in the AI, so that, based on the collected historical data of the participants, inputs, and feedback, the network node will assign each participant an appropriate priority for their type of input. The correct type of input is given a high priority, as can be seen, for example, in the "list of commands" 141 in Figure 2. By interposing a network node with AI 1 or 10 as proposed according to the invention, the total number of errors per period is generally reduced.

[0089] Another example or use case is a packaging station. The packaging station in a workshop is operated by robots that can lift, carry and transport heavy packages. Everyday machines and people work closely together and this requires a special security strategy. For example, a sensor is integrated into the ID card or a wearable device of the human participant in order to immediately detect an incident and automatically send an alarm signal. Robots already have integrated sensors that detect their current status and send this as feedback to the network node.

[0090] In the event of an accident in which a human collapses or a robot or its load collides with a human, a sensor sends an alarm signal. This is an example of "negative" input feedback, which is then sent to the robot and / or other output devices, such as those that trigger alarm signals, in the packing station.

[0091] All feedback is continuously collected from the sensors and the network node and immediately passed on as inputs that are processed in the network node and at the same time automatically used to train the network node's AI. The AI ​​in the network node learns to recognize which command from which participant is responsible for this feedback and reflects this decision - for example - in the user interface of the feedback analyzer. This allows users to see the AI's current decision. Users can see the AI's decision by visualizing the AI's results and compare it with reality and correct or confirm it accordingly. This user action is then also made available to the AI ​​for training in order to improve the quality of the automatic prioritization.Based on such training, the AI ​​can also learn to predict whether an input sequence could lead to a dangerous situation or an accident. Ideally, the AI ​​can learn to decide, independently of human review, in real time and dynamically whether or not to forward a corresponding input from an identified user to an output device. Likewise, the AI ​​can perform prioritization dynamically and revise it at any time, with the prioritization results preferably being visualized simultaneously with the change in the "list of commands" 141.

[0092] The AI ​​110 or the “AI Decision Taker” 110 can also be expanded so that it not only prioritizes the participants and users, but also uses the feedback 7a, 7b to compare and / or dynamically monitor the output devices based on their results with theoretical and / or previously obtained work results. For example, if, as in the example of an accident in the packing station described above, the robot 6c does not respond to the alarm signal but continues its activity, then this is evaluated by the AI ​​110 as an incorrect action and the robot 6c is given a low priority accordingly. On the other hand, if the robot stops all activities and informs the system about the accident, e.g. by sending an appropriate alarming feedback to the network node and / or in another way, then this is evaluated as a correct action and the robot 6c remains high prioritized or is upgraded and given more tasks.For example, the robot with the worst results will be removed from the project entirely, the connection will be cut off and the participants will be informed if necessary.

[0093] Such an expanded AI 110, which has an expanded feedback analyzer 120 with management function, automatically helps to remove broken machines and to uncover and eliminate errors, thus supporting security in the entire system. The automatic prioritization by the AI ​​110 based on the behavior and success of all participants 2a to 2c takes place automatically, dynamically in real time and, if necessary, transparently through visualization, for example as shown in Figure 3. All inputs are retained, even if a data collision occurs in network node 1 or 10, with the most valuable decisions - according to the assessment of the AI ​​analyzer 110 - being implemented immediately, while the less valuable ones are saved and implemented with a lower priority.These additional inputs are immediately displayed using a visualization—for example, according to the example shown in Figure 3—and the order of prioritization is also visible in the visualization. To keep the visualization simple yet meaningful, an additional icon for the decision overview can be shown or hidden.

[0094] The automatic connection of the various interfaces presented here keeps the costs for prototyping low and increases the effectiveness of user interaction via input devices that are familiar to the respective user.

[0095] Different types of input and output devices connected within a project via the network node also enable smooth collaboration between people with different abilities, disabilities, and / or cultures, because translation difficulties, as well as differences in the formats of the data blocks associated with the inputs, are automatically eliminated by an appropriately trained AI connected to the IoT.

[0096] This allows inputs and participation from a variety of users in a design / prototyping session in a virtual project space. Not only different human participants, but also human users, intelligent machines, and / or intelligent algorithms can work simultaneously on the same task in the same virtual project space. They can all contribute to different parts of the design / prototype—virtual or physical—concurrently. If multiple participants attempt to contribute to the same part with simultaneous commands or inputs, the AI ​​110 automatically decides which part should be executed. This decision is continually improved and reviewed over time based on the process running via the feedback analyzer 120 and the AI ​​110.

[0097] Regardless of the grammatical use of the term, “users,” “participants,” “human participants,” “project participants,” etc., refer to persons with male, female, or other gender identities.

[0098] The invention makes it possible for the first time for different participants and different devices within a virtual project space to work together simultaneously via a network node without interference in the sense of "without compatibility problems", i.e. independently of, for example, capabilities, language, operating systems of the input and / or output devices and without losses due to data collision.

[0099] Regardless of the grammatical gender of a particular term, persons with male, female or other gender identity are included.

Claims

Patent claims 1. Computer-implemented network node connecting several computer-based devices in a communicating computer network in such a way that it receives and forwards data packets, wherein in or via the network node the data packets are temporarily stored, analyzed, processed and / or optionally transformed before being forwarded, wherein the network node has a computer-based prioritization and / or transformation module having the following components: e) at least one memory unit for temporarily storing the data packet(s) of the connected computer-based devices after they have been received, f) at least one processor that is suitable for reading received data packets and / or their metadata, g) at least one connection to a neural network with an AI for capturing / analyzing and / or transforming and / or processing the data packet(s) before they are forwarded and h) at least one processor that is suitableto apply the results of the prioritization and / or analysis of data packets by the AI to the forwarding, transformation and / or processing of the data packets to the storage, processor and / or output device shared by the network.

2. A computer-implemented network node according to claim 1, wherein the processor has an interface to the IoT.

3. Computer-implemented network node according to one of claims 1 or 2, wherein the network node has a star-shaped topology.

4. Computer-implemented network node according to one of claims 1 or 2, wherein the network node has a chain-like topology.

5. A computer-implemented network node according to any one of claims 1 or 2, wherein the network node has a point-to-point topology.

6. A computer-implemented network node according to any one of the preceding claims, wherein the processor has at least one interface to at least one feedback acquisition device of an output device.

7. Computer-implemented network node according to one of the preceding claims, wherein the processor has at least one output device for visualizing the prioritization performed by the AI or manually.

8. Computer-implemented network node according to one of the preceding claims, wherein the network node is connected to a computer-based output device shared in the virtual project space.

9. Computer-implemented method for the collision-free forwarding of several data packets from different computer-based input devices connected to one another via a network node to one or more output devices also connected to the network node, comprising the following method steps: - Recording each network participant - in short "participant" through an identity, - Receipt of at least one input and / or command from one of the participants by the network node - Recording and / or saving user inputs in the same project space of the network node - Processing of the input and / or command by a processor of the network node that has an AI, - Assigning and storing a priority for each network participant by processing the collected information using an AI and subsequently prioritizing each participant, - Reading the inputs and / or commands of the participants and / or event lists of the feedback from the output devices, - Analysis and prioritization of inputs and / or commands by artificial intelligence and / or processing of user inputs by AI - Generating results by the AI regarding prioritization of the inputs and / or commands of the respective participants and - Forwarding of inputs and / or commands according to the results of the prioritization - Capturing and / or reading the feedback and / or event lists received from the output device regarding the success of the forwarding and execution of the input and / or command by an output device and finally - Analysis of feedback and review of prioritization by AI.

10. A computer-implemented method according to claim 9, comprising the additional step of classifying a participant into a group based on his role in the project and / or his location.

11. Computer-implemented method according to claim 9 or 10, which comprises the additional step of classifying the received data packets based on their metadata.

12. Computer-implemented method according to claim 9 to 11, which comprises the additional step of visualizing the respective current prioritization.

13. A computer product comprising program code means which cause the processor and / or the AI of a network node to carry out a method according to any one of claims 9 to 12 when the program code means are processed by the processor.

14. A computer-readable storage medium comprising a computer program product according to claim 13.

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