618 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.

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 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.

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 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.

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.

A system and method of determining a sentiment of a participant in an interaction may include: obtaining a plurality of textual segments, each representing a portion of the interaction, and labeled according to a specific participant; inferring a language model on one or more textual segments of the plurality of textual segments, to generate respective semantic embedding vectors, each representing a semantic meaning of the respective textual segment in a semantic vector space; compiling a semantic vector set that includes (i) a target semantic embedding vector, corresponding to a target textual segment of a target participant, and (ii) one or more peripheral semantic embedding vectors, respectively corresponding to one or more peripheral textual segments of the plurality of textual segments; and inferring a composite machine-learning (ML)-based model on the semantic vector set, to classify a sentiment of the target participant, as expressed in the target textual segment.

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 enginethat exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

An information processing method includes: obtaining, as text information, information related to communication by a person; performing emotion analysis on a plurality of words or phrases after breaking down the text information into the plurality of words or phrases by performing morphological analysis on the text information; and visualizing an analysis result of the emotion analysis according to each row and column of a matrix table by forming the matrix table by arranging, in a first direction, a plurality of emotional expression-related items that represent human emotions and arranging, in a second direction, an attribute category-related item that indicates an attribute of the person or an organization.

A text editor or a plugin thereto automatically generates authorship tokens to 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 relates to the technical field of intelligent equipment. A trajectory diffusion imitation learning method based on single human video demonstration comprises the steps that a demonstration video is analyzed to obtain an operation body posture motion trajectory and an object pose motion trajectory, and the demonstration video is a video of a human or machine operation object; respectively carrying out interpolation calculation on the positions and postures of the operation body posture motion trail and the object pose motion trail to obtain a training data set; using the training data set to train a diffusion strategy model to obtain a target diffusion strategy model; and during deployment, the real-time pose of an end effector of the robot serves as observation input of the target diffusion type strategy model, an operation body pose movement track and an object pose movement track in the deployment stage are formed, and a joint control instruction of a target operation body is obtained. And the task success rate is kept, and the practicability and the expandability of the imitation learning method are improved at the same time.

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.

A control system for human-robot collaboration, including: a passive reactive control path implemented through a virtual damping system and configured to generate reactive control signals in response to human-applied interaction inputs; a predictive control path configured to generate predictive control signals based on predicted human performance objective inferred online from measured contact wrenches; and a signal blending component configured to combine the reactive control signals and the predictive control signals, wherein the predictive control signals are bounded in magnitude such that passivity of the reactive control path is preserved and stability of closed-loop human-robot interaction is maintained during collaboration.

The system according to this embodiment aims to efficiently match tasks between generating AI and humans. [Solution] The system according to the embodiment comprises a reception unit, a matching unit, a generation AI selection unit, and a human selection unit. The reception unit registers jobs. The matching unit matches the jobs registered by the reception unit with the generation AI and humans. The generation AI selection unit has the generation AI select a job. The human selection unit has humans select a job.

The invention discloses a creation contribution degree evaluation system and method based on keyboard behaviors and AI interaction content, and relates to the technical field of computer software and information security. The method comprises the following steps: collecting keyboard input rhythm, rollback behavior and paste operation data in a user creation process; obtaining interactive dialogue content between the user and the AI model; generating an independent authoring confidence rating based on behavioral data analysis; performing semantic comparison on the user creation text and the AI interaction content, and calculating a human semantic contribution degree; fusing the confidence rating and the semantic contribution degree to generate a human creation contribution degree index; and finally, carrying out block chain evidence storage on the contribution statement abstract. According to the method, through a double evidence chain of'behavior + semantics', accurate quantification of the contribution ratio of human and AI is realized from the creation process, the problems of easy avoidance, high misjudgment rate and lack of credible evidence storage in the prior art are effectively solved, and the method is suitable for evaluation requirements of the fields of scientific research, education, publishing and the like on creation authenticity.

As an example, a conversational artificial intelligence (AI) that is in a conversation receives a human response from a human, augments the human response to create an augmented response, determines a context of the conversation, and provides the context and the augmented response to a plurality of automatic speech recognition (ASR) modules that individually process the augmented response in parallel. The conversational AI receives a plurality of intermediate text outputs from the plurality of ASR modules, wherein individual intermediate text outputs of the plurality of intermediate text outputs are received from individual ones of the ASR modules. A reconciliation AI performs a contextual reconciliation of the plurality of intermediate text outputs based at least in part on the context of the conversation to create a final text output. The conversational AI provides, in real-time, an artificial intelligence response to the human based on the final text output.

The invention discloses a low-cost robot imitation learning method and system based on a human video. The method comprises the following steps: S1, data acquisition; s2, data extraction and physical alignment are carried out to eliminate man-machine physical differences; the step is divided into two parallel processing modules of action space alignment and visual space alignment; s3, data set construction: mixing the aligned human data with real robot teleoperation data, carrying out balanced sampling, and constructing a mixed data set Dmix; and S4, cooperative training: constructing a strategy network based on diffusion Transform for training. According to the method, data can be acquired only through the monocular RGB camera, expensive robot teleoperation data are replaced with cheap and easily available human videos, and the data acquisition threshold is greatly reduced. Through a visual alignment strategy of random color grid rendering, a network can learn neglect skin color textures and pay attention to geometric structures without a complex generative model, so that the robot can be seamlessly migrated to robots in different forms.

A methodology for auditing the visual diversity of unlabeled human face image datasets uses a set of core human interpretable dimensions derived from human similarity judgments. Given a face image, a model can output dimensional values aligned with the human mental representational space of faces, where values not only express the presence of a feature, but also its extent. Since the model can be learned entirely from human behavior, the learned dimensions are not biased toward features that are easier to verbalize or quantify.

The invention discloses a multi-fingered dexterous hand operation reinforcement learning method based on a human action prediction model, and belongs to the field of humanoid robot dexterous hand tool body operation. A human action prediction model is trained by using data with three-dimensional pose marks of a human hand and an object, and is used for generalizedly generating a three-dimensional track of interaction between the human hand and the object. And a universal reward function is constructed by using the trajectory and is used for training a dexterous hand reinforcement learning strategy in simulation. Then, the migration ability of the strategy from simulation to reality is improved through simulation parameter domain randomization and course learning, and the reinforcement learning strategy obtained through training is deployed on a real world robot. According to the scheme, understanding of the future attitude of the operated object is introduced, the interaction process of the human hand and the object is predicted as a whole, the unified, simple and efficient reward function is constructed based on the prediction, the method is suitable for different operation tasks and different types of dexterous hands, and the method has the advantages of being high in generalization, high in success rate, high in reward function universality and the like.

A classification model for multimodal human physiological data, and a training method therefor, a classification method for multimodal human physiological data, and a device are provided. The classification model includes: a multi-headed self-attention module, a normalization module, a fusion expert system, and a decision module. The multi-headed self-attention module is configured to perform feature extraction on multimodal synchronous data. The normalization module is configured to generate normalized feature data based on the extracted feature data. The fusion expert system includes: an electroencephalogram expert subsystem, an electrocardiogram expert subsystem, an electrodermal activity expert subsystem, and a multimodal synchronous fusion expert subsystem each configured to perform a classification task based on the corresponding normalized feature data. The decision module is configured to calculate a final classification result based on the above four classification results.

The current document is directed to methods and systems that provide an emotional compass that reflects the emotional states of human beings, dynamics of the emotional states over time periods, emotional synchronization between humans and between humans and automated interactive systems, and the degree to which the emotional state of one human is influenced or affected by the emotional state of another human or by an automated interactive system. The emotional compass can be used for continuous or intermittent emotional orientation by both automated systems and human analysts. The emotional awareness provided by the emotional compass facilitates continuous or intermittent strategy selection for achieving various different types of emotion-related results.

The invention provides a sleep disorder animal model device and an experiment method, the sleep disorder animal model device comprises an experiment box, the experiment box is internally provided with grid division boxes in grid distribution, and the sleep disorder animal model device integrates a liftable auxiliary box body, a cover plate mechanism capable of amplitude modulation and jittering and a sound, light, vibration and tactile stimulation module; complex stimulation close to insomnia inducements of real human beings can be applied in a staged and combined mode, the problem of strong stress caused by a traditional single stimulation model is effectively solved, and the clinical goodness of fit of an animal model is remarkably improved. Meanwhile, based on intelligent management of a central control system, real-time non-invasive monitoring of animal behaviors and sleep states is achieved by fusing camera visual analysis, pressure sensing and wireless physiological telemetry technologies, execution units are automatically and accurately regulated according to the real-time non-invasive monitoring, standardization and repeatability of the experimental process are ensured, and the experimental efficiency is improved. And the construction efficiency and the evaluation reliability of the sleep disorder animal model are improved.

The invention belongs to the technical field of man-machine cooperation interactive motion monitoring, and provides a man-machine cooperation-oriented human trajectory prediction method and monitoring system, and the method comprises the steps: obtaining multi-view image data; based on the multi-view image data, reconstructing three-dimensional positions, linear velocities and angular velocities of a plurality of joint points of a human body and the tail ends of all connecting rods of the robot in real time, and generating a motion state sequence; inputting human body joint point three-dimensional coordinates of past T frames in the motion state sequence into a deep neural network model, and generating K multi-modal human body motion track prediction results of future S frames in a single step; calculating a potential risk index based on the multi-modal human body motion trajectory prediction result, the current human-machine relative pose and the motion state, and triggering a graded early warning signal according to a preset threshold value; and sending the graded early warning signal to a robot controller to execute deceleration, pause or avoidance actions, and synchronously displaying a man-machine digital twin, a predicted trajectory cloud and a risk grade on a three-dimensional visual interface.

The lower tibia fusion plate structurally comprises an upper plate part, a fusion plate body, a groove, an accessory assembly, a debugging structure and an auxiliary handle, the upper plate part is connected to the end of the fusion plate body in a sleeving mode, the debugging structure is installed in the middle of the fusion plate body through the groove, and the accessory assembly and the auxiliary handle are arranged on the outer side of the fusion plate body; by means of the accessory assembly and the debugging structure arranged in the middle and outside the fusion plate body, when the fusion plate body, the upper plate piece and the auxiliary handle are connected with the tibia through bone nails, the debugging structure in the groove in the middle of the fusion plate body can increase the fixing strength of the middle of the fusion plate body and the tibia in an abutting mode; in the tibia recovery period, the fixing strength of the auxiliary handle and the tibia can be adaptively adjusted on the outer side of the fusion plate body in an inclined mode by means of the accessory assembly, the overall strength of the fusion plate is better improved, the body weight of the human body can be supported, and the healing recovery process of the tibia can be accelerated.

The invention discloses a reward function design method and device, equipment, a storage medium and a computer program product, and relates to the technical field of large language models.The reward function design method comprises the steps that abstract analysis is conducted on a task instruction and agent information, and target object activities associated with the task instruction and the agent information are determined; determining a restrictive condition according to the target object activity and the agent information, and determining a target skill rule of the agent based on the restrictive condition; filtering and converting the target skill rule to obtain a plurality of initial reward functions corresponding to the task instruction; and performing function fusion on the plurality of initial reward functions based on weight distribution to obtain a target reward function corresponding to the task instruction. The task instruction and the agent information are abstractly associated through the large language model to determine the initial reward function, and then the design of the agent reward function is closer to the preference and thinking of human through the fusion of the reward function.

The present invention aims to provide a technology that can more easily and accurately determine the posture of bones of characters that virtually represent humans, etc. in a virtual space, or robots, etc. existing in a real space. In a skeletal model in which a parent bone and a child bone are connected by a joint, the posture determination method of the present invention, which determines the posture of a child bone using a plurality of parameters defining the rotation of the child bone relative to the parent bone, comprises the steps of making a judgment on at least one of the plurality of parameters, correcting the plurality of parameters based on the judgment result, and determining the posture of the child bone using the corrected plurality of parameters. The step of making a judgment includes making a first judgment on whether any one of the plurality of parameters exceeds a first limit, wherein the first limit is expressed as a limit in which at least one parameter affects a limit related to two or more other parameters. The step of correcting the plurality of parameters includes, if the first judgment result determines that the first limit has been exceeded, a step of correcting the plurality of parameters using the first limit so that the plurality of parameters are within the range of the first limit.

It is revealed that an organ such as pancreas can be regenerated by utilizing a fact that the deficiency of an organ is complemented by injecting an induced pluripotent stem cell (iPS cell) into a developed blastocyst in a blastocyst complementation method. Thus, the present invention has solved the above-described object. This provides a method for producing a target organ, using an iPS cell, in a living body of a non-human mammal having an abnormality associated with a lack of development of the target organ in a development stage, the target organ produced being derived from a different individual mammal that is an individual different from the non-human mammal.