105 results about "Sensory input" patented technology

A novel method provides an AI language virtual agent having self-improvement features and which uses language modeling and tree search techniques. The AI language virtual agent exchanges textual discussion with users and other simulated agents. The method includes receiving a current situational description depicting natural language user input, temperament qualities and textual tendencies of the virtual agent, and indicia regarding subject matter context of a present conversation. The indicia regarding subject matter context include textual logs from recent conversational exchanges. The current situational description includes audio, visual, and tactile inputs collected proximate to the virtual agent. The method preferably utilizes an MCTS tree search in combination with self-moving modules, one or more language models, tree search techniques outputting textual responses to the current situation description, and the virtual agent responding with textual expression to verbal input in combination with the audio, visual, tactile, and other sensory inputs.

A system and method for AI-assisted conversation utilize a biosensor to receive biosignals, which are analyzed by the perception engine. The engine generates context tags and text descriptions from multimodal sensory inputs and biosignal analysis. This information is then used by the conversation engine, along with memory engine data (conversation history, biographical background, keywords), to generate prompts for a language model. These prompts are displayed to a user through a computing device that presents various conversation features like communication history, context data, and selected content. The system enables interactive AI-assisted conversations between humans and machines or other humans.

The invention relates to the technical field of neuromuscular electrical stimulation, and provides a multi-modal feedback self-adaptive adjustment neuromuscular regulation and control treatment system, which comprises wearable working equipment for outputting pulse electric signals to adjust nerves and muscles to form movement or adjustment feeling, electromyographic signals, motion data and user feeling input are collected to conduct feedback adjustment on output parameters of the output pulse electric signals; the handheld control equipment is used for being connected with the wearable working equipment and controlling the output working mode and the working state of the wearable working equipment; and the server-side equipment is used for managing user information and preference of the handheld control equipment, setting function authority of the handheld control equipment, acquiring data acquired by the wearable working equipment, and realizing self-adaptive output of output parameters of the pulse electric signals of the wearable working equipment based on a self-adaptive reinforcement learning algorithm. Adaptive optimization of millisecond-level dynamic electrical stimulation parameters is realized through a multi-modal feedback data fusion technology in combination with a layered reinforcement learning architecture.

The invention discloses an energy storage scheduling method and system in combination with dynamic load prediction, and particularly relates to the field of risk prediction of energy storage scheduling, comprising the steps of constructing a fusion structure comprising a perception input set, a disturbance trend set and a system variable set, and forming a perception input structural body in a scheduling process; comparing the perception input structural body with the behavior track sample set, generating a residual structural body and marking an abnormal behavior candidate section; and processing the abnormal behavior candidate section, and generating an abnormal confirmation result through a freezing mechanism and a trend consistency judgment process. An abnormal behavior candidate section caused by false load change is identified by constructing a sensing input structural body and introducing a behavior trajectory comparison mechanism, so that a misjudgment instruction is frozen before response, trend consistency verification is implemented, frequent start and stop and response drift of an energy storage system caused by sensing errors are avoided, and the fault-tolerant capability of the system is improved.

The invention discloses a text narrative-guided virtual reality motion sickness monitoring and relieving system, and relates to the technical field of virtual reality, the text narrative-guided virtual reality motion sickness monitoring and relieving system comprises an adaptability monitoring module, the adaptability monitoring module comprises a data acquisition unit, and the data acquisition unit is used for acquiring physiological information of a user in virtual reality training in real time; the data is transmitted to the adaptability evaluation unit; the dynamic adjustment module is used for receiving the sensing data acquired by the data acquisition unit and dynamically adjusting parameters of the virtual reality equipment according to the received data; by monitoring physiological information and perception data of a user in real time, signs of motion sickness can be rapidly recognized, the real-time monitoring is combined with dynamic adjustment of visual and vestibular stimulation in a VR environment, the risk of motion sickness caused by mismatching of sensory input and actual body movement can be effectively reduced, and the motion sickness can be rapidly recognized through the dynamic adjustment. The system can preventively adapt to individual differences of users and reduce motion sickness.

End effector tool changers for a robotic system are disclosed. The end effector tool changer is physically compact, mechanically robust, has high radial strength, prevents undesirable rotations, uses minimal sensory input, is suitable for a wide variety of tools, and operates quickly. The end effector tool changer includes a robotic arm attachment portion, including a first magnetic part and a first engagement part, and a tool attachment portion, including a second magnetic part and a second engagement part, where the first engagement part is configured to engage with the second engagement part, the first engagement part and the second engagement part are selected from the group consisting of a pin and a socket, and the first magnetic part spatially and magnetically corresponds to the second magnetic part.

This invention presents a computer-implemented method for answering polls and surveys using large language models (LLMs), a novel approach that leverages an LLM's ability to emulate a group of human responses for diverse data collection. The system involves training a single or multi-modal LLM on a comprehensive dataset comprising one or more categories of text, image, sound and other sensory input data, which can be continuously updated with current events and trends, to ensure accurate representation of human behaviors and opinions. Utilizing a user interface, the method includes receiving survey and poll questions, posing these questions to a unique instance of the trained LLM and receiving the answers, recording the question-answer sessions, and compiling the results for presentation. Additionally, the system prioritizes data privacy and integrates a feedback mechanism for continuous improvement, providing an efficient solution to answering surveys or polls in the digital age.

A system and method for providing sensory relief from distraction, inattention, anxiety, fatigue, and / or sensory issues to a user in need thereof, the user may be autistic / neurodiverse or neurotypical. The system may be configured to obtain user sensory sensitivity data indicative of the user's visual, sonic, or interoceptive sensitivity, use at least the user sensory sensitivity data to determine mediation data corresponding to a user-specific sensory threshold and a user-specific mediation, store the sensory threshold and mediation data, record sensory input stimuli to the user using one or more sensors, compare the sensory input stimuli to the sensory threshold, and determine a mediation to be provided to the user based at least on the mediation data in response to comparing the sensory input stimuli to the sensory threshold, the mediation configured to provide the user with relief from distraction, inattention, anxiety, fatigue, or sensory issues.

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.

A method including: capturing or receiving data associated with behavior of a user of a virtual reality device during a session in a virtual environment, wherein the data includes sensory inputs associated with the user from one or more sensors and information associated with user parameters; initiating authentication of the user in the virtual environment; comparing the captured or received data with historic data of the user, wherein the historic data is associated with behavior of the user monitored for a plurality of sessions over a period of time in the virtual environment; determining a score based on the comparison; comparing the score with a predefined threshold score; and in response to determining that the score is above the predefined threshold score, authenticating the user.

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.

Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information. The virtual personal assistant can further be configured to determine an action using the current intent and the current input state.

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.

A device and method that supplements sensory input, thereby providing a supplemented sensory environment, to induce plasticity within the central nervous system that effectively overcomes sensory-neural processing deficits or strengthens specific sensory-neural abilities. In one implementation, ear-level hearing devices are used to deliver therapeutic sound with specific acoustic features that serve as archetypes of stimulus features for which sensory-neural processing is compromised by a sensory-neural deficit.

A processing device is configured to interface with a region of the brain of a subject that is responsible for forming concepts without sensory input. The processing device receives brain signals representative of at least one concept formed by the region of the brain without sensory input, and processes the received brain signals so as to convert the at least one concept to data that is representative of a tangible form of the at least one concept. In certain embodiments, the processing device processes data that is representative of at least one concept to be formed by the region so as to convert the data into one or more brain signals, and selectively providing the one or more brain signals to the region of the brain such that the at least one concept represented by the data is formed by the region of the brain without sensory input.

Touring circuitry for managing an immersion tour as a vehicle traverses a path through a touring theatre receives location information and provides control signals to an augmented-reality system based on a location of the vehicle in the touring theater. The control signals cause the augmented-reality system to stimulate at least two sensory inputs of a passenger.

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.

A method of training an autonomous vehicle system including pretraining first and second components to form a pretrained model, employing the pretrained model in an operative environment to identify a predominant source of error, and updating either the first component or the second component based on which one is the predominant source of error. The first component being configured to predict future events from sensory inputs and a second component being configured to determine one or more actions based on the future events with a dataset.

During operation, a computer system may receive a sequence of sensory inputs (e.g., at different timestamps) associated with an object in an environment. Then, the computer system may train a predictive model that predicts a future location of at least a representation corresponding to the object in the environment based at least in part on the sequence of sensory inputs. Moreover, the computer system may provide a world view of the object in the environment based at least in part on the pretrained predictive model. Note that providing of the world view may include: comparing the predicted future location of at least the representation with the future location of the object in the environment; and when a difference between the predicted future location of at least the representation and the future location of the object in the environment exceeds a predefined value, selectively performing a remedial action.

One or more multi-layer systems are used to perform inference. A multi-layer system may correspond to a node that receives a set of sensory input data for hierarchical processing, and may be grouped to perform processing for sensory input data. Inference systems at lower layers of a multi-layer system pass representation of objects to inference systems at higher layers. Each inference system can perform inference and form their own versions of representations of objects, regardless of the level and layer of the inference systems. The set of candidate objects for each inference system is updated to those consistent with feature-location representations for the sensors as well as object representations at lower layers. The set of candidate objects is also updated to those consistent with candidate objects from other inference systems, such as inference systems at other layers of the hierarchy or inference systems included in other multi-layer systems.

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 discloses a self-service terminal based on multi-sensory interaction, and the terminal comprises a multi-sensory input module which is used for receiving voice, gesture and AR / VR input; the instruction analysis engine is used for analyzing the input information into a unified operation instruction; the task distribution module is used for distributing tasks to the corresponding processing units; the mode self-adaption module is used for automatically switching interaction modes; the database module is used for storing related data and examples; the voice synthesis and special effect module is used for carrying out vivid voice broadcast; the gesture auxiliary learning module is used for assisting gesture learning through AR / VR; the gesture auxiliary learning module can adjust a learning guiding strategy and prompting frequency in real time according to the gesture execution condition of the user, and it is ensured that the user effectively learns gesture operation; according to the method, more flexible and convenient operation selection can be provided for the user by integrating multiple interaction modes such as voice, gestures, AR / VR and the like, and the user operation threshold is greatly reduced.

The invention belongs to the technical field of upper limb auxiliary training, and discloses an upper limb auxiliary training device based on the early stage of hemiplegia, which comprises an analysis module, a design module, a training module and an optimization module, according to the training device, through the integrated circuit and the pressure sensor, feelings are input and transmitted to a patient in real time, and stimulation intensity and auxiliary strength are adjusted according to feedback of the patient. The design objective is to provide upper limb extensor active movement induction training for early hemiplegic patients, and operation repeatability and safety are both considered. Clinical practice verifies that the active motion induction training of the upper limb extensor in the early stage of hemiplegia has remarkable effects in promoting motion recovery and improving the functions of the upper limbs. A new technical solution is provided for early hemiplegia rehabilitation treatment, the workload of primary therapists is relieved, and the development of early rehabilitation is promoted. The method is of great significance in promoting functional recovery of hemiplegic patients, improving the life quality of the patients and relieving family and social burdens.

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.

The present teachings relate to a method, system, medium and implementation for enabling communication with a user. Information characterizing the surroundings of a user who is about to participate in a new conversation is received via a communication platform, wherein the information is obtained from a scene in which the user is located and captures characteristics of the user and the scene. Relevant features are extracted from the information. The state of the user is inferred based on the relevant features, and the conversation context of the scene is determined based on the relevant features. A topic for a new conversation is determined based on the user, and feedback is generated to initiate a new conversation with the user based on the topic, the user state, and the conversation context.

The invention relates to a sensory-based smart child car seat consisting of a camera (1), pressure sensor (2), heating surface (3), speaker (4), foot support (5), activity table (6), beverage compartment (7), sponge structure (8), which minimizes the safety problems that may occur during travel for children, allows them to travel for longer periods without getting bored, and provides tactile sensory input for children.

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.