2161 results about "Human being" patented technology

A multi-modal articulation system translates human gestures into control commands for digital and automation devices. The system integrates a camera module, which captures facial and hand movements, with an image processing unit that extracts facial features, such as eyebrow movement, lip curvature, eyelid motion, and eye trajectory. These extracted features are analyzed in real time by an articulation recognition module that compares them against a database to recognize specific gestures. The system tracks and processes air-drawn hand gestures using a gesture trajectory tracking unit, which converts the motions into digital representations and classifies them using deep learning algorithms. The recognized gestures are converted into machine-readable control signals and executed on various connected devices via a relay control unit. The system incorporates adaptive algorithms to improve accuracy, account for environmental conditions, and stabilize hand tremors, providing a gesture-based control interface for applications ranging from smart home systems to multimedia devices.

Provided is a process, including: executing, with the computer system, tasks in the workflow with AI agents; detecting, with the computer system, that a human is needed to assist with a given task among the tasks in the workflow after one of the AI agents attempts the given task; selecting, with the computer system, the human from among a set of humans based on a profile of the human and the given task and sending a message that causes the selected human to perform the given task; and receiving, with the computer system, the selected human's completion of the given task and inputting the human completion of the given task, or input, to one of the AI agents performing a downstream task in the workflow; and storing, with the computer system, a result of completing the workflow in memory.

Techniques for triggering a transfer of a chat conversation with a user from a chatbot to a human agent based on detection of transfer criteria are disclosed. The chatbot uses natural language processing and a generative model to collect and organize information from the chat conversation to present to the human agent in a report when the chat conversation is transferred to the human agent. The chat conversation is transferred to the human agent by presenting the report and a graphical chat interface to the human agent. The graphical chat interface displays messages from chat conversation between the human agent and the user and displays messages from chat conversations between the human agent and multiple other users. Transferring the chat conversation from the chatbot to the human agent includes presenting interface elements to the human agent for receiving user input from the human agent for transmission to the user.

Various disclosed embodiments are directed to learning-free video object segmentation (VOS) of one or more video frames. In various instances, such VOS does not rely on human supervision or labeling and can be generalized or applied to any video “in the wild.” This is because training and fine-tuning are not required for VOS. Particular embodiments perform VOS based on feature similarity of different sections of a video frame and / or estimated motion similarity of different sections of the video frame, such as via a graph cut on a graph.

Systems, devices and methods are provided for stimulating one or more nerves within a user. A system comprises stimulator having an electrode configured for contacting the outer skin surface at a target location and an energy source coupled to the stimulator, The energy source is configured to generate at least one electrical impulse and to transmit the electrical impulse transcutaneously from the electrode through the outer skin surface of the user to a selected nerve in the user. In certain aspects, the systems and methods are particularly useful for non-medical purposes, such as reducing stress and anxiety, enhancing relaxation, improving sleep, energy, concentration and mood, increasing mental or physical performance, promoting mental health, recovery and wellness, generally improving the health and wellbeing of a user and / or for temporarily or permanently improving intelligence, learning capacity, memory retention, recall, mood, alertness and / or sleep efficiency in human beings.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The present disclosure provides a humanoid robot with an arrangement of components that allows the robot to mimic the movements, functionality and capabilities of a human being. The robot includes a torso coupled to a waist, an arm assembly, and a head assembly. A pelvis is coupled to the waist and has left and right actuator mounts. Left and right hip assemblies are coupled to the respective actuator mounts. Each hip assembly includes a hip pitch actuator assembly, a hip roll actuator assembly, and a leg twist actuator assembly. The hip pitch actuator assembly has a portion positioned within the pelvis and is coupled to the actuator mount. The hip roll actuator assembly is coupled to the hip pitch actuator assembly, with a non-90 degree angle formed between their axes. The leg twist actuator assembly is coupled to the hip roll actuator assembly and positioned below extents of both the hip pitch and hip roll actuator assemblies.

A method includes creating, via a server, a plurality of virtualized human personalities associated with respective human users; receiving, via the server, a cyberattack message; determining, via the server, the cyberattack message targets a human user of the respective human users; selecting, via the server, a virtualized human personality of the plurality of virtualized human personalities based on the virtualized human personality being associated with the human user targeted by the cyberattack message; and responding, via the server, to the cyberattack message using the virtualized human personality selected from the plurality of virtualized human personalities.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

A high-quality digital human avatar is quickly reconstructed from a monocular video and is rendered in real time, the method is widely applied to various fields, and an existing method based on a neural radiation field is high in training and rendering cost. According to the dynamic human body three-dimensional modeling method based on the three-dimensional Gaussian sputtering technology, three-dimensional Gaussian can be expanded to a dynamic human scene, and rapid training and real-time rendering can be achieved. Specifically, the three-dimensional Gaussian of the human body is initialized in a standard space, and the three-dimensional Gaussian is converted from the standard space to a posture space by utilizing linear hybrid skin (LBS) according to posture parameters, so that fine details of the human body are learned at very low calculation cost; a posture parameter refining module and an LBS weight optimization module are designed to improve the problem of inaccurate detection of posture parameters in a real data set, and multi-resolution hash coding is introduced to accelerate the model training speed.

A head and neck assembly for a humanoid robot, including a head portion having an exterior surface defining an overall shape resembling a human head; a neck portion extending from the head portion; a head housing assembly enclosing the head portion and neck portion; an electronics assembly contained within the head housing assembly; and a head actuator assembly configured to move the head portion relative to a torso of the humanoid robot.

The invention discloses a human abnormal behavior monitoring method based on a large-model multi-agent, which is executed by a modular multi-agent system deployed on a back-end server, obtains information through a monitoring camera, and comprises the following steps: obtaining a video stream from the monitoring camera by a sensing agent and extracting human body posture features; analyzing the key frame by a scene understanding agent by using a visual large model, and constructing a time sequence dynamic scene graph; the core reasoning agent evaluates the scene semantic conformity based on the pre-trained large model and performs abnormal preliminary judgment; performing fine-grained classification, interpretation generation and risk assessment on the abnormal behaviors; and the report and action agent generates an alarm and records event data. According to the invention, through multi-agent cooperative work and a large model technology, efficient and accurate monitoring of human abnormal behaviors is realized, and the intelligent level of the monitoring system and the abnormal behavior identification accuracy are improved.

The invention discloses a mechanical arm autonomous grabbing system based on a visual language model and a control method of the mechanical arm autonomous grabbing system. The method comprises the steps that firstly, a mechanical arm is initialized, and a color image and a depth image of a working interval are collected; secondly, based on the color image and the natural language description, generating a target object bounding box and confidence, and judging whether a target object exists in the scene or not; and when the target object does not exist, instance segmentation and capture feasibility detection are carried out on the visible object in the current scene. And then the depth image and the object binary mask passing through grabbing feasibility detection are sent to an interaction object selection strategy and an action selection strategy to obtain an object to be interacted and a pushing action direction. And finally, center coordinates of the to-be-interacted object and center coordinates of the mechanical arm are obtained, the obstacle is moved in the pushing action direction until the target object appears and can be grabbed, and the mechanical arm grabs the target object and places the target object at a designated position. According to the method, the object specified by the human natural language can be accurately recognized and grabbed.

This disclosure relates to a method of treating a neurological disorder, comprising administering a bupropion, or a combination of a bupropion and a dextromethorphan, to a human being in need thereof, monitoring the human being for Brugada pattern / syndrome, drug reaction with eosinophilia and systemic symptoms, or acute generalized exanthematous pustulosis, and reducing or discontinuing use of the bupropion, or the combination of the bupropion and the dextromethorphan, if the human being develops Brugada pattern / syndrome, subcutaneous tissue disorder, drug reaction with eosinophilia and systemic symptoms (DRESS), or acute generalized exanthematous pustulosis.

A method for predicting and assessing motion of a user in an environment. The method includes determining estimated positions of key points of the user at a first time and determining link lengths for linkages connecting the key points. The method further includes determining, for an upcoming time, predicted positions of the key points and predicted kinematics for the key points. The estimated positions, predicted positions, and kinematics are determined periodically over a time period. The method further includes classifying an activity being performed by the user over the time period and predicting a future motion path of the user for an upcoming time period. The method further includes generating a motion assessment for the user using the predicted future motion of the user and making the motion assessment available to a device or account associated with the user.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

According to the double-arm mechanical arm teleoperation system based on the VR head-mounted display and the control method, high-precision and flexible teleoperation is achieved by accurately capturing movement tracks of the two hands and mapping the movement tracks to the double-arm mechanical arm, meanwhile, the content shot by the scene camera is transmitted to the VR head-mounted display in a streaming mode, and the real-time performance and immersion of operation are enhanced. In addition, the intelligent obstacle avoidance strategy designed by the invention effectively prevents the movement of the mechanical arm from being out of control in the operation process, and ensures the safety and reliability of the operation. The system has the remarkable advantages of high-precision operation, real-time performance, immersion, multi-scene adaptability, safety, reliability and the like, is suitable for interaction and task execution of a universal robot in industrial, household and other scenes, replaces human beings to execute precise operation in a high-risk environment, and has wide application prospects and important practical significance.

The invention provides a toy interaction control method and device based on a large language model, a terminal and a medium, and the method comprises the steps: obtaining user side information which comprises one or more of voice, text, image, limb movement or external environment information; performing feature extraction on the user side information to generate feature representation; inputting the feature representation into a large language model, and identifying a user intention; generating a corresponding control instruction according to the identified user intention; and converting the control instruction into a specific control signal, and sending the specific control signal to a control system of the toy so as to execute corresponding control. The large language model is utilized to perceive human language characters, voice emotions, limb actions and the like, human intentions are accurately recognized, then the feedback of the toy is controlled, and intelligent interaction is formed for users.

Using generative AI to supplement automated information extraction is disclosed. Computer vision (CV) and / or optical character recognition (OCR) models and a generative artificial intelligence (AI) model are used together to extract information (e.g., names, dates, invoice numbers, etc.) from a source. Acceptance threshold(s) may be used to accept predictions for extracted data elements from the models, and the prediction from the generative AI model may be preferred or a human may be tasked with reviewing the element. If no model meets its respective acceptance threshold (whether common or specific to that model), these element(s) may be marked for subsequent human review, or a human can be looped in to correct these element(s). The models may then be retrained using these labeled elements.

The present disclosure describes systems and methods for asynchronous conversational deliberation among a population of human participants. In some aspects, a collaboration server is connected to a memory containing a representation of conversational content that was previously captured from prior human participants, the representation associated with each prior participant including at least one stored idea, insight, view, or assertion expressed by that prior participant and at least one associated numerical measure of sentiment, conviction, confidence, or support. According to some embodiments, an asynchronous conversational process is enabled in which each of a plurality of new human participants engages in real-time dialog with a Conversational Surrogate Agent displayed by their associated computing device, each Conversational Surrogate Agent configured to express to at least one new participant, at least one stored idea, insight, view, or assertion associated with at least one prior participant.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

A method for detecting and verifying A.I.-generated material, said method comprising the steps of: receiving a submitted material; analyzing the received material for attributes characteristic of at least one of an A.I.-generated material or human-generated material; scoring the analyzed material for a detection score; flagging the scored material above a pre-defined detection score; prompting a verification request for at least one submission of a response to at least one of a question or task; analyzing said response material by referencing against the submitted material for a verification score; and verifying the authenticity of the submitted material based on the verification score.

A power system human resource allocation method based on multi-agent cooperation comprises the steps that S1, a human resource task collection module is constructed, and newly-added human tasks of a power system are collected in real time; s2, quantitatively decomposing each human task into a plurality of sub-tasks, calculating the complexity of each sub-task, and constructing a human task balance optimization model; s3, constructing an intelligent agent role allocation model according to the sub-task complexity, the intelligent agent capability matrix and the real-time load of the intelligent agent; and S4, performing iterative operation through a multi-objective optimization algorithm, solving the human task balance optimization model and the agent role allocation model, allocating the subtasks to the agents to obtain a task allocation matrix and estimated completion time, and executing the subtasks by the agents according to the allocation matrix to realize allocation of the human tasks. The design not only considers the balance of resource consumption and processing time of human tasks, but also allocates based on allocation efficiency and task completion efficiency.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

In various examples, systems and methods are disclosed relating to training machine learning models using human demonstration of segments of a task, where other segments of the task are performed by a planning method, such as a Task and Motion Planning (TAMP) system. A method may include segmenting a task to be performed by a robot into segments, determining a first set of instructions of a plurality of sets of instructions for operating the robot to perform a first objective of a first segment, determining that the plurality of sets of instructions is inadequate to perform a second objective of a second segment, receiving from a user device a second set of instructions for operating the robot for the second segment following an end of the first segment, and updating a machine learning model for controlling the robot using the second set of instructions for the second segment.

The invention provides a robot skill learning and control method and device oriented to rich contact operation tasks. The method provided by the invention comprises the following steps: collecting human demonstration data; the human demonstration data at least comprises end effector pose data and stiffness matrix data; on the basis of the end effector pose data and the stiffness matrix data, a generalization motion track and a generalization stiffness track are obtained through time alignment, probability distribution modeling and new state constraint adaptation; constructing an obstacle model through environmental perception, taking the generalized motion trajectory and the generalized rigidity trajectory as reference sampling candidate trajectories, screening a collision-free trajectory in combination with motion consistency, rigidity consistency and smoothness cost, and performing iterative optimization to obtain an optimized motion trajectory and an optimized rigidity trajectory; and converting the optimized motion track and the optimized rigidity track into a robot joint torque instruction based on variable impedance control, and controlling the robot to execute a rich contact operation task based on the robot joint torque instruction.

Real-time extraction of human poses from video data for animation of avatars. In some implementations, a computer-implemented method includes obtaining an input video including a plurality of video frames in a sequence that depict movement of a person based on a plurality of poses of the person in the input video. Keypoints of the person are detected in the video frames of the input video data, and a sequence of 3D body poses are determined that correspond to the plurality of poses of the person in the video frames of the input video. Determining the 3D body poses includes using a spatial-temporal transformer to determine joint angles of the keypoints, where the spatial-temporal transformer separately encodes inputs in spatial dimensions within each video frame and a temporal dimension across the video frames.

Approaches presented herein provide for the automated determination of a level of impairment of a person, as may be relevant to the performance of a task. A light and camera-based system can be used to determine factors such as gaze nystagmus that are indicative of inebriation or impairment. A test system can simulate motion of a light using a determined pattern, and capture image data of at least the eye region of a person attempting to follow the motion. The captured image data can be analyzed using a neural network to infer at least one behavior of the user, and the behavior determination(s) can be used to determine a capacity or level of impairment of a user. An appropriate action can be taken, such as to allow a person with full capacity to operate a vehicle or perform a task, or to block access to such operation or performance if the person is determined to be impaired beyond an allowable amount.