30 results about "Sensory input" patented technology

In various examples, a technique for performing a task includes converting one or more sensory inputs obtained using one or more sensors of a machine into a plurality of segments. The technique also includes, for each segment included in the plurality of segments, generating, via execution of a machine learning model, a caption for the segment, and storing, in a data store, a representation of the caption in association with the segment. The technique further includes performing, by the machine, one or more actions based at least on one or more queries of the data store.

An emotion-aware computing operating system integrates multimodal sensory inputs, stratified memory, cyclehit scoring, and history score values to predict user tasks, orchestrate autonomous cognitive agents, and adapt interface outputs in real time. A prediction module applies cyclehit scoring derived from historical task cycles and weighted history score values to forecast workflows.An orchestration kernel selects and coordinates agents, redistributes subtasks, and negotiates dynamically under cognitive load. A stratified memory fabric maintains ephemeral, situational, and long-term user models for personalization. An adaptive interface layer adjusts informational density and tool availability based on inferred user state. A certification and licensing API enforces agent onboarding, compliance, and monetization policies, requiring registration of performance metrics prior to integration. Embodiments include software, cloud, edge, and robotic platforms, enabling monetizable deployment across healthcare, finance, education, enterprise, and ambient device ecosystems.

A multifunctional sensory device designed to aid individuals with sensory processing disorders (SPD) in achieving self-regulation is disclosed. The device can be in the form of a plush animal or a backpack. The device includes a plurality of removable weights to provide deep pressure, a scent emitter for calming or alerting smells, a Bluetooth speaker for producing calming sounds, and an embedded vibration coil for adjustable tactile and heating stimulation. The device also features various textured surfaces and modular components for customizable sensory input. Additionally, a connected system monitors and adjusts sensory inputs based on user feedback.

The invention relates to digital watermarking of a machine learning model. In one embodiment, a method is disclosed that includes detecting, by a computing device, a sensory input; identifying, using a machine learning model, one or more attributes associated with the machine learning model, wherein the attributes are identified based on sensory inputs according to training of the model; and presenting the attribute as an output. The identification may be performed at least in part by an inference engine interacting with the model. The sensory input may include an input image received from a camera, and the model may identify an attribute based on an input object in the input image according to training of the model. The model may include a convolutional neural network trained using training data that associates training sensory inputs with attributes. The training sensory input may include a training image having a training object, and the input object may be classified in the same category as the training object.

Implementations of a fully configurable, modularized home robot are described that implement trained neural network classifiers to solve problems of providing home health care and monitoring of the elderly and / or infirm, providing entertainment for the family, providing environmental and safety monitoring coupled with mechanisms to clean the air and remedy indoor climates such as humidity and temperature, and provide a mechanism for caring for pets left at home when the owner is away based upon sensory input.

The processing subsystem communicates with brain regions of an animal subject that are responsible for forming concepts, without sensory input. The processing subsystem receives brain signals representing concepts formed by these brain regions, without sensory input. The processing subsystem processes the received brain signals to convert the concepts into computer-readable data that represents their tangible form. The processing subsystem identifies data records associated with one or more data elements of the computer-readable data, and also associated with real-world objects associated with the concepts.

The invention discloses a digital integrated brain injury nerve regulation diagnosis and treatment platform, and relates to the technical field of brain injury diagnosis and treatment, and the technical scheme is characterized in that the platform comprises a multi-mode brain function monitoring module used for collecting brain function data of a brain injury consciousness disorder patient in real time; the multi-sensory nerve regulation and control stimulation module is used for cooperatively applying multi-sensory stimulation to the patient; and the main control and data processing module receives and processes the multi-modal signals transmitted by the multi-modal brain function monitoring module, generates a personalized nerve regulation scheme by extracting brain function characteristics, and controls the multi-sensory nerve regulation stimulation module to execute the personalized nerve regulation scheme at the same time. According to the method, multi-sensory stimulation is cooperatively applied to the patient, the problem of pain points of traditional distributed non-standard stimulation is solved, the core requirement of the patient for multi-channel sensory input awakening is met, meanwhile, a complete closed loop of monitoring-analysis-intervention-feedback is formed, and dynamic evaluation and accurate intervention on the brain function state of the patient with acute brain injury consciousness disorder are achieved.

A computer implemented method for translating a sensory input to a sensory output for communication between first and second entities. The first entity includes a user and the second entity includes a user or a computing system. The method includes obtaining a first profile specifying a communication capability of the first entity, and a second profile specifying a communication capability of the second entity and obtaining a trained Machine Learning (ML) model operable to map an input sensory communication in accordance with one of the first or second profiles to an output sensory communication in accordance with the other of the profiles. The method further includes receiving an input sensory communication from one of the first or second entities, using the ML model to map the input sensory communication to an output sensory communication, and providing the output sensory communication to the other of the entities.

The present disclosure provides a control system for a humanoid robot comprising a bipedal action model (BAM) with hierarchical architecture including a beta model executing cognitive tasks at lower frequency, ingesting multimodal sensory inputs including visual data and natural language instructions, and an alpha model executing reactive tasks at higher frequency, communicatively coupled to the beta model. The BAM is trained on retargeted robot training data derived from robot-free training data. At runtime, the BAM outputs continuous control commands as parallel-generated action chunks controlling at least 18 degrees of freedom. The system includes a wearable collection apparatus capturing movement data from a human operator without physical connection to the robot, and a retargeting module translating robot-free training data into robot training data by solving embodiment mismatches between human and robot kinematic structures.

A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.

In various examples, a technique for performing end-to-end navigation using a generative world model includes converting a set of sensory inputs received by a machine at a current time step into a set of embedded features. The technique also includes generating, via execution of one or more neural networks, one or more states associated with the current time step based at least on the set of embedded features, a history of states preceding the current time step, and a first set of actions associated with a previous time step. The technique further includes converting, via execution of the one or more neural networks, the one or more states into a set of predictions associated with the current time step, and performing, by the machine, a second set of actions associated with the current time step based on the set of predictions.

A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

A method for training a style encoder of a neural network. Sensory input variables, which represent a movement of a system and surroundings of the system, are compressed to an abstract driving situation representation in at least one portion of a latent space of the neural network, using a trained situation encoder of the neural network. The sensory input variables are compressed to a driving style representation in at least one portion of the latent space, using the untrained style encoder. The driving style representation and the driving situation representation are decompressed from the latent space to output variables, using a style decoder of the neural network. A structure of the style encoder is changed to train the style encoder until the output variables of the style decoder represent the movement.

The invention discloses long-term perception for robotic systems and applications. In various examples, techniques for performing a task include converting one or more sensing inputs obtained using one or more sensors of a machine into a plurality of segments. The technique further includes, for each segment included in the plurality of segments, generating a description for the segment via execution of a machine learning model; and storing the representation of the description in a data store in association with the segment. The technique further includes performing, by the machine, one or more actions based at least on the one or more queries on the data store.

Methods of sensory input integrity attestation are provided. Artifacts included within devices under test inject a known noise signal into the output signal of one or more output devices that are detectable by one or more input devices (i.e., sensors) of an embedded device, and monitor the received input data. By comparing the received signal against the expected noise signal, attestation of the validity of sensory input data is possible. Such sensory input data attestation is capable either locally or using a remote attestation device with knowledge of the expected data stream.

The invention is suitable for the technical field of power grid intelligent control, and provides an intelligent control method and device for a low-carbon park optical storage direct-flexible system based on brain emotion learning, and the method comprises the steps: obtaining the output voltage and output current of each distributed power generation unit; calculating a global average voltage and a weighted average current; for each distributed power generation unit, generating a sensory input signal and an emotion signal; inputting the sensory input signal and the emotion signal into a brain emotion learning model to generate a compensation signal; superposing the compensation signal to a local droop control link, and correcting the reference voltage; wherein the sensory input signal represents the voltage deviation and the current distribution deviation of the system, and the emotion signal represents the instantaneous value of the voltage deviation of the system, the integral of the voltage deviation and the current distribution deviation and the self-feedback of the compensation signal. According to the method, voltage stabilization and current accurate distribution are realized through hierarchical cooperative control, the problem of controller performance deterioration caused by model mismatch is solved, and the robustness in a high-uncertainty scene is improved.

Applying therapeutic neural stimuli involves monitoring for at least one of sensory input and movement of a user. In response to detection of sensory input or user movement, an increased stimulus dosage is delivered within a period of time corresponding to a duration of time for which the detected sensory input or user movement gives rise to masking, the increased stimulus dosage being configured to give rise to increased neural recruitment.

A processing subsystem communicates with a region of an animal subject's brain that is responsible for forming concepts without sensory input. The processing subsystem receives brain signals that are representative of a concept formed by the region of the brain without sensory input. The processing subsystem processes the received brain signals to convert the concept to computer readable data that is representative of a tangible form of the concept. The processing subsystem identifies a data record that is associated with one or more data elements of the computer readable data and that is also associated with a real-world object that is related to the concept.

Exemplary embodiments include systems and methods for pairing a user to an available parking space, the system comprising: a positional device; at least one sensory input device positioned on an infrastructure point located proximally to at least one parking space; and a central server platform comprising a server having a processor and a memory unit communicatively coupled with the processor, the memory unit storing instructions which, when executed, perform a method. The method comprises: receiving a first signal from the positional device, the first signals indicating an intention of a user to seek parking and further indicating a location of the user; receiving a second signal from the at least one sensory input device, the second signal indicating availability of a parking space; determining, from the second signal, which of the parking spaces are available to the user; and pairing the user with a parking space.

The Fidget Knob is a compact, handheld device designed to provide tactile stimulation and promote focus and relaxation. Consisting of two rotating knobs, possibly with textured surfaces, it offers a satisfying tactile experience for users to fidget with during moments of stress or boredom. The Fidget Knob's ergonomic design allows for easy manipulation and discreet use in various settings, such as classrooms, offices, meetings, and public spaces. The Fidget Knob's reasonably silent behavior allows it to be used & felt by the user without distracting nearby people. Research suggests that fidgeting can help improve concentration and reduce stress levels by providing sensory input and promoting a sense of calm. The Fidget Knob offers a convenient and socially acceptable way for individuals to engage in fidgeting behavior, potentially enhancing cognitive performance and well-being.

A method for determining driving comfort in a motor vehicle is provided, in which an AI is used to determine driving comfort by evaluating sensor data from the vehicle's sensors, where the sensor data includes optical signals. Since not only the interior climate but also, or alternatively, optically detectable influences occurring only during driving, especially vibrations, are considered as effects on subjectively perceived driving comfort, a high level of driving comfort is achieved. By coupling this with acoustic and mechanical sensors, driving comfort can be evaluated using all sensory input.

A method and apparatus for performing state estimation and satellite reacquisition for a mobile satellite terminal is described. In some embodiments, the apparatus includes a planar antenna and a signal processing engine communicatively coupled to the planar antenna configured to process asynchronous sensory inputs from a plurality of sensors and generate an estimate of a state of the planar antenna of the satellite antenna terminal based on the sensory inputs, the estimate including an estimate of orientation.

This invention discloses a control method and system for permanent magnet synchronous motors based on brain-based emotional learning, belonging to the field of permanent magnet synchronous motor control. This invention establishes a sensory input function and an emotional cue function based on the speed error e, generates a q-axis current setpoint through a brain-based emotional control method, and obtains a predicted value y through a neural network identifier. m (k), where the input variables of the neural network identifier are x1 = e(k) - e(k-1), x2 = e(k), and x3 = e(k) - 2*e(k-1) + e(k-2); the adjustable coefficients of the emotional cue function are optimized using the predicted values. This invention improves the control effect of the brain emotion controller on the PMSM speed regulation system, and further enhances the controller's anti-interference capability while simplifying the adjustment parameter process of brain emotion control.

The invention provides an Alzheimer's disease rehabilitation training system based on a sense-motion-cognition coupling mechanism, which comprises a sense guide module, a motion participation module, a cognition intervention module and a coupling regulation and control module, and is characterized in that the sense guide module is used for providing sense input related to a training task; the exercise participation module is used for guiding the subject to complete an exercise task matched with the sensory stimulation; the cognitive intervention module is used for embedding cognitive processing and task decision links in the sensory-motion interaction process; and the coupling regulation and control module is used for carrying out coordinated regulation on the action sequence, duration and task difficulty of each module according to the reaction state, participation degree or completion characteristics of the subject in the training process, so that a stable, continuous and adaptive coupling relationship is formed in the same training process by sensory input, motion execution and cognitive processing.

In various examples, a technique for performing a task involves converting one or more sensory inputs, obtained using one or more sensors of a machine, into a multitude of segments. The technique also includes, for each segment contained within the multitude of segments, generating, by executing a machine learning model, a heading for the segment and storing in a data store a representation of the heading associated with the segment. The technique further includes the machine performing one or more actions based on at least one or more queries of the data store.

A processing subsystem communicates with a region of an animal subject's brain that is responsible for forming concepts without sensory input. The processing subsystem receives brain signals that are representative of a concept formed by the region of the brain without sensory input. The processing subsystem processes the received brain signals to convert the concept to computer readable data that is representative of a tangible form of the concept. The processing subsystem identifies a data record that is associated with one or more data elements of the computer readable data and that is also associated with a real-world object that is related to the concept.

The invention relates to the technical field of medical apparatus and instruments, and discloses a tongue muscle vibration and electrical stimulation composite therapeutic apparatus, which comprises a main body unit and a tongue body therapeutic head which are assembled together, and is characterized in that the main body unit is a handheld main body unit; the main body unit is provided with a main control unit, and a power supply management module, a vibration driving module, an electrical stimulation generation module and a control module which are connected with the main control unit; a vibration array and an electrode array are arranged on the tongue body treatment head, the vibration array is composed of a plurality of resonant motors, and the electrode array is composed of a plurality of surface electrode points. According to the invention, professional tongue muscle electrical stimulation and physical vibration functions are integrated in a portable device which can be safely operated by a non-professional person for the first time, and peripheral sensory input enhancement (vibration) and central movement path activation (electrical stimulation) can be synchronously realized through space-time cooperation of physical vibration and neuromuscular electrical stimulation. The proprioceptive feeling, the muscle strength and the coordination of the tongue muscle are improved.

The invention discloses an olfactory training system based on visual and auditory assistance, relates to the technical field of sensory training and cognitive enhancement, and constructs a bidirectional perception bridge connecting digital virtual training and a real physical world by introducing a parallel architecture of real object image recognition and multi-mode training selection. A scene self-adaptive flexible training path is realized, the system provides three selectable training modes of pure visual coordination assistance, pure auditory coordination assistance and audio-visual coordination assistance, after a real object is identified and basic smell is released, a user can select associated pictures and visual patterns or only associated music or sound to carry out auxiliary memory, so that the user experience is improved, and the user experience is improved. The design allows training to be flexibly adapted according to sensory preferences of different users, requirements of different rehabilitation stages or different environment restrictions, and multi-mode selection endows the system with the ability of dynamically adjusting sensory input weights according to user states and scenes, so that training feelings extend to daily life scenes, and generalization learning is realized.

A neurosuit system including a neurosuit, a sensory input device, and a suit control system is disclosed. The neurosuit may include a plurality of wearable pieces, and a plurality of bungee cords to interlock the plurality of wearable pieces. The sensory input device may be configured to capture a body movement of a user wearing the neurosuit. The suit control system may be communicatively coupled to the sensory input device and configured to receive inputs from the sensory input device, and determine the body movement based on the inputs. The suit control system may compare the body movement with an ideal body movement, and perform a predetermined action when the body movement is different from the ideal body movement. The predetermined action may be associated with the plurality of bungee cords.

A product is traditionally experienced in XR mainly through sight that accounts for about 80% of sensory inputs, thereby impeding an immersive experience of a product. In the present disclosure, an object's form factor guides a user's gestures in carrying out actions on an augmentation. A suitable physical object marker mimics physical touch interaction with a virtual product. Leveraging the sense of touch, the user gains deeper understanding of the virtual product, since the product responds to the user's natural gestures. In accordance with the present disclosure, transform data in the form of a position, an orientation and a scale associated with the physical object and user's hands handling the physical object are recorded to identify an associated gesture. Utilizing gesture to action mapping, the user's action is translated to the virtual product. The augmentation is modified in real time to provide an immersive experience.