1183 results about "Human being" patented technology

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 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.

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.

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.

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.

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 robot control method based on tactile prediction pre-training comprises the following steps: acquiring and generating a human playing data set consisting of three-channel image tensors in an offline stage, and training a constructed conditional diffusion model comprising a tactile encoder, a tactile decoder and an action and visual encoder; in the online stage, the trained conditional diffusion model is integrated into a standard imitation learning strategy network, and an action instruction of the robot is generated according to the state of the robot, the current visual features and the tactile feature vectors extracted by the imitation learning strategy network. According to the method, a specific agent task is completed by training a deep neural network model, that is, a future tactile signal sequence is predicted according to historical information and future action intentions; the model is enabled to characterize generic haptic features contacting physical dynamic laws for further migration into downstream robot control tasks.

The invention discloses a human-computer interaction method and system based on a vision-language-action model, and belongs to the field of human-computer interaction. According to the method, an anchoring ring strategy is adopted to collect human teaching data to finely adjust the VLA model, and then the trained model is applied to an actual human-computer interaction scene. In the data acquisition stage, a first operator guides a master robot to execute task actions, and a slave robot synchronously moves and interacts with a second operator, and returns to a predefined initial position after each interaction; a teaching sample is formed by recording a robot state, an environment image and an instruction text, and a high-quality data set is generated through data enhancement. In the application stage, the real-time robot state, the environment image and the instruction text serve as input, an action instruction is generated through the VLA model, and the robot is driven to complete a cooperation task. According to the method, the data utilization efficiency and the model generalization ability are remarkably improved, the difference between simulation and the real environment is effectively overcome, and efficient, safe and natural man-machine cooperation is achieved.

The invention provides a service robot cross-modal user intention analysis method and system. The method comprises the following steps: firstly, acquiring and analyzing a voice instruction sequence and a visual flow containing a gesture, fusing a task target object and a gesture pointing direction to generate an intention hypothesis, and then projecting the intention hypothesis to a knowledge graph of an environment with a body; the method comprises the following steps: acquiring an intent hypothesis of a user, querying a candidate object set simultaneously matched with spatial constraints and semantic constraints of the intent hypothesis, when ambiguity exists in the candidate object set, generating an inquiry instruction for eliminating the ambiguity according to a relation path in a knowledge graph of the body environment, and finally analyzing and confirming a final user intent according to the response of the user to the inquiry instruction; according to the technical scheme provided by the invention, the ability of the robot to cope with uncertainties such as unknown reference and environment shielding in a real scene is enhanced, the interactive experience closer to the human cognitive level is realized, and the interactive efficiency and satisfaction of the user in a complex environment are also improved.

The invention relates to the technical field of artificial intelligence, and discloses a brain-like decision-making method, device and equipment for body intelligence and a storage medium, and is applied to a robot. The method comprises the following steps: receiving a human language instruction, and carrying out semantic understanding on the human language instruction to obtain a task description; collecting multi-modal data, and generating an environment model based on the multi-modal data; and generating a pulse event sequence by adopting a pulse neural network model based on the task description and the environment model, and generating a motion strategy of the robot based on the pulse event sequence. Through application of the bionic neuromorphic computing architecture, the decision-making speed of the robot in a complex scene is increased by 50 times, the power consumption is reduced to 1 / 10, the real-time response capability of the robot is greatly improved, the robot can quickly cope with various emergencies, for example, when encountering a sudden obstacle, the robot can quickly do an avoidance action, and the robot can be prevented from being damaged. And collision accidents are avoided.

Examples relate to computer-implemented methods for analyzing communication in digital evaluation. A computing device accesses multimodal data comprising video and audio information of human subjects and configures a computational model using this data to identify patterns in communication that correlate with assessment metrics. The configuring implements processing techniques that preserve relationships between features across different modalities. When a video recording of a candidate is received, the computing device processes the video using the configured computational model to extract communication features. These features may include facial expressions, gestures, eye movements, posture, vocal tone, and speech patterns. The device generates an evaluation of the candidate based on the extracted communication features and outputs a representation of the evaluation.

There is provided a method of automatically detecting that a target image is deepfake, comprising: receiving authentic images depicting a vehicle with actual damage, receiving the target image depicting potential damage to the vehicle, feeding the target image into a machine learning (ML) model, obtaining a candidate set of human-readable text describing the potential damage to the vehicle, feeding the authentic images into the ML model, obtaining from the ML model, a ground truth set of human-readable text describing the actual damage to the vehicle depicted in the authentic images, computing a similarity metric indicating a difference between the potential damage described in the candidate set of human-readable text and the actual damage described in the ground truth set of human-readable text, and in response to the difference being above a threshold or meeting a requirement indicating a significant difference, detecting that the target image is likely deepfake.

The invention relates to the field of robot perception and intelligent man-machine interaction, an equipment platform and a perception module, a system is provided with an Intel RealSense D435i depth camera which is used for acquiring an RGB-D image sequence containing depth information in real time, and meanwhile, an advanced YOLOv8-Pose deep learning model is embedded, so that the depth information of the RGB-D image sequence is acquired in real time. High-precision detection, natural interaction, a gesture guidance mechanism, direction inference and target candidate aggregation, interactive target screening and judgment, a robustness enhancement strategy, deep deletion and noise robustness and an incomplete target completion mechanism of human upper body skeleton key points in a front scene are realized; the man-machine collaborative target object identification and positioning method based on depth vision, attitude estimation and semantic understanding is mainly realized. The technology can be suitable for service robots, intelligent assistant robots and other practical application scenes with high requirements for autonomous perception and natural interaction, and man-machine co-fusion and intelligent environment construction are further promoted.

The invention discloses a low-power-consumption warning ground pile awakening method and system based on human activity recognition, and belongs to the technical field of image analysis and intelligent security and protection. According to the method, in a micro-power-consumption mode, the environment is monitored through an image sensor, and when environment changes meet awakening conditions, lightweight human body detection is conducted through an edge AI chip. And if the human activity probability exceeds the confidence coefficient, starting a main camera and a high-computing-power AI chip to carry out deep behavior analysis, fusing a behavior analysis result with a geographic position and a timestamp, generating a risk decision result, and triggering a dynamic response. According to the invention, a hierarchical wake-up and multi-source fusion technology is adopted, on-demand work is realized, power consumption is greatly reduced, early warning accuracy is improved through deep behavior analysis, and the problems of high energy consumption and inaccurate early warning of traditional equipment are effectively solved.

This disclosure provides a method, apparatus, electronic device, and storage medium for animal language conversion, relating to the field of artificial intelligence technology, specifically machine learning, deep learning, and natural language processing. The specific implementation involves: acquiring multimodal data related to the animal, including animal vocal data, animal behavioral data, and animal physical characteristics data; preprocessing the multimodal data to obtain fused multimodal data; identifying the animal's current emotion based on the fused multimodal data to obtain an emotion recognition result; and performing semantic mapping and language translation on the emotion recognition result to convert the animal language into human language, obtaining a language conversion result. This disclosure can accurately identify the animal's current emotional state and convert it into human language, thereby achieving deeper emotional communication and understanding between animals and humans, and improving the accuracy and efficiency of cross-species communication.

The invention discloses a psychological health analysis method and apparatus, an electronic device and a storage medium, through the application, dynamic psychological assessment is generated through multi-modal data fusion analysis, and personalized scenarized dynamic interaction content is generated based on an assessment result and an interaction history, so that the psychological health analysis efficiency is improved. Meanwhile, continuous learning and correlation analysis are carried out on the psychological state of the user by utilizing a multi-dimensional memory architecture, so that the system can understand the change of the user state and adjust an interaction strategy like a human consultant, and therefore, the technical problems of insufficient emotional distraction and credibility of the user due to the adoption of a standardized reply mode in the prior art can be solved, and the user experience is improved. The technical effects of enhancing the emotion affinity of the system, establishing a continuous and credible interaction relationship and improving the durability of the psychological intervention effect are achieved.

The invention discloses a humanoid robot walking and running unified control method and system based on a biological rhythm, and belongs to the technical field of humanoid robots. Capturing human walking and running action data, and extracting rhythm features from frequency and phase time domains; constructing a rhythm generator based on the rhythm features extracted in the step 1; according to a strategy gradient method based on a constraint reinforcement learning algorithm, a walking and running unified control strategy is constructed, rhythm information generated based on a rhythm generator is adopted in the walking and running unified control strategy, then action-critic is used for training, a bionic motion control system Walk2Run under speed driving is jointly constructed, natural gait transition and frequency adjustment are achieved, and the walking and running unified control strategy is obtained. And meanwhile, the physical capacity limitation of the robot is met, and motion fluency and energy efficiency are improved. The problems that an existing humanoid robot does not achieve energy optimization of human walking during walking, and mobility of an existing data set has high requirements for the motion ability of the robot and is poor in generalization ability are solved.

Methods and apparatus for controlling a robot (e.g., having a set of continuous rotation joints) to perform extra human behaviors are provided. The method includes receiving task information to perform a task, determining, using a control system of the robot, a motion plan for the robot to perform the task, wherein the motion plan includes rotation about one or more joints of the robot (e.g., about at least one of the continuous rotation joints in the set of continuous rotation joints) to efficiently perform the task using extra human behaviors.

A conversational artificial intelligence (AI) system is configured to engage in a multi-turn conversation with a user. The multi-turn conversation is substantially focused on a target topic. A conversation analyzer analyzes the multi-turn conversation to detect and store at least some turns in the multi-turn conversation that deviate from the target topic and instead characterize life attributes of the user. A knowledge graph constructor builds a knowledge graph for the user based on at least some turns in the multi-turn conversation that characterize the life attributes of the user. The knowledge graph translates the life attributes of the user into the user's life biography, including life chronology, life preferences, life milestones, life events, or any combination thereof. A knowledge graph applicator uses parts of the knowledge graph in a subsequent multi-turn conversation with the user by contextually interspersing portions of the user's life biography in the subsequent multi-turn conversation.

A system for multitask detection performs subject tracking by processing image frames from one or more video cameras deployed in a monitored environment. The system uses a neural network to detect human subjects in each frame and extracts feature sets for each subject. These features include a semantic center of the body and directional vectors extending to other body parts, such as the head or face, forming a subject-specific fingerprint. The system compares these fingerprints across frames to identify instances of the same subject over time. By correlating subject positions in image frames with the geolocation data of the capturing cameras, the system computes global coordinates for each subject. Using both the subject-specific fingerprints and spatial coordinates, the system determines trajectories of individuals, including transitions between camera views.

Data is the “fuel” that powers the machine learning “engine” for Artificial Intelligence. However, identifying high quality data that can catalyze smarter AI, AGI, and SuperIntelligent systems is becoming an increasingly challenging bottleneck for machine learning. This invention not only describes novel methods for identifying the most valuable data, but it also presents an entirely new framework for understanding the information content of AI-relevant datasets. The methods can be used by intelligent systems autonomously or in collaboration with humans. Novel methods for accelerating AI learning, and for updating the knowledge of AI systems in real-time, are also disclosed. Consistent with the view that human survival may depend on the fastest path to AGI also being the safest path, the invention describes catalysts which help maximize alignment between the values of AGI and humans. These innovative catalysts increase not only the intelligence, but also the safety, of AI systems.

The invention discloses a multi-mode perception collaboration and real-time synchronization system, which breaks through the limitation of vision / hearing in the prior art, covers vision, hearing, smell and movement, and realizes more accurate cooperation through a perception layer, a control layer and an execution layer. According to the invention, an immersive experience space full of shock and reality can be created, synchronous output of visual, auditory, tactile and olfactory signals is realized by means of high-precision equipment, and a series of cross-sensory signal superposition schemes are designed on the basis of response characteristics of human physiology and psychology to various sensory stimuli. Therefore, the overall perception of the experiencer to the space atmosphere is enhanced. Meanwhile, through weight distribution and conflict resolution, the system can flexibly adjust the sensing mode according to the environment and the user state. The multi-device cooperation error can be controlled at millisecond level and millimeter level, and the natural fusion of virtuality and reality is realized.

The invention relates to an unmanned aerial vehicle body cognition alignment method based on man-machine cooperation. The method comprises the following steps: collecting an entity observation picture, and constructing an entity alignment task by taking the entity observation picture as a navigation graph discrete node; an unmanned aerial vehicle body cognitive alignment model constructed for the task comprises four modules. The problem modeling module converts tasks into POMDP containing elements such as states, actions and observation. The observation module questions a target fact with a self-reflection mechanism based on POMDP, and generates a suspected entity description. And the prediction module adopts zero sample learning, inputs the suspected entity description and the target fact into VLM, and outputs a prediction result. The action module depends on a navigation-questioning mechanism: during navigation, selecting an unexplored entity and getting close to obtain a picture, and controlling the distance through a bounding box proportion; during question asking, explored entity pictures are input into the VLM to generate distinguishing questions, and answers related to target facts are obtained through conversation with human beings. By adopting the method, accurate entity alignment in a complex urban environment can be realized.

A method for managing a state of a user includes: receiving a first dataset from a client, in which the user performs an activity using the client; obtaining a second dataset from a database; analyzing the first dataset and the second dataset to generate a unique digital identifier (DI) for the user; analyzing, based on a predetermined threshold, the first dataset and the second dataset to extract relevant data; making a determination that the predetermined threshold is violated; providing, based on the determination, the relevant data and the unique DI to a first infrastructure node (IN); in response to the providing, receiving a recommendation from the first IN to mitigate an issue associated with the user, in which the recommendation is generated for the unique DI; and sending the recommendation to the client to manage the state of the user.

The present disclosure provides a humanoid robot system comprising a mechanical structure with at least 30 degrees of freedom across torso, arms, and legs, actuators driving the degrees of freedom, sensors including cameras and proprioceptive sensors, and a computing system implementing a hierarchical bipedal action model (BAM). The BAM includes: a Delta model processing sensor data and user input to generate latent representations at a first frequency; a Gamma model receiving latent representations to generate human task actions at a higher second frequency; a Beta model translating task actions into joint configurations at a higher third frequency; and an Alpha model converting joint configurations into actuator control signals at a higher fourth frequency.

An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

A node-edge symbolic consent kernel (NESCK) provides a computing architecture in which every instruction is gated by a verifiable human-intent signal and an ethical-predicate chain prior to execution. The system integrates a biometric-sensing front-end (EEG / GSR / facial micro-affect), a symbolic arbitration engine that transforms bio-intent data into consent tokens, and a cryptographically bonded node-edge ledger that records execution lineage, revocation, and audit proofs. Each node represents an executable state bound to a human consent fingerprint, while each edge encodes the ethical transition rules authorizing propagation through the network. At runtime, the kernel evaluates symbolic predicates, verifies zero-knowledge proofs of consent, and allows or halts instruction dispatch. The framework operates across devices, edge nodes, and cloud layers, enabling real-time lawful AI behavior, revocable autonomy, and tamper-proof moral audit trails. Embodiments span neuroadaptive wearables, autonomous vehicles, robotics controllers, and sovereign AI systems requiring continuous consent and transparent accountability.

The invention discloses a federal personalized human activity recognition training method based on a super network, which comprises the following steps that: a server randomly selects a plurality of clients to participate in training, and broadcasts embedded network parameters to the clients; and after receiving the embedded network parameters, the client generates an embedded description vector in combination with the local data set and uploads the embedded description vector to the server. And the server generates corresponding personalized model parameters according to the embedded description vector uploaded by each client, and then issues the personalized model parameters to the clients for local fine tuning. And after fine tuning is completed, the client uploads the personalized model parameter update quantity to the server, and the server updates the super network according to the personalized model parameter update quantity, generates an embedded description vector update quantity and returns the embedded description vector update quantity to the client. And the client generates an update gradient for guiding the embedded network according to the update quantity of the embedded description vector by combining the similarity consistency constraint between the embedded space and the personalized model parameter space. And the server collects the update gradients uploaded by all the clients and aggregates the update gradients to complete the update of the embedded network.