176 results about "Human interaction" patented technology

The present invention introduces a universal AI-powered pet management platform that establishes an entirely new category of technology, transcending conventional pet training systems. This comprehensive system integrates a modular wearable pet device with interchangeable sensors, sophisticated AI processing capabilities, and diverse output modules to create a unified ecosystem for holistic pet care. Unlike traditional training devices focused solely on behavior modification, this platform simultaneously manages multiple domains including real-time health monitoring, environmental safety assessment, emotional well-being analysis, autonomous training, emergency response, and seamless integration with external systems. The platform's universal architecture enables dynamic adaptation across diverse applications from companion animals to service animals, wildlife monitoring, and specialized deployments. By leveraging advanced artificial intelligence models, multimodal communication pathways, and a universal API for third-party integration, the system creates an interconnected technological framework that fundamentally transforms the relationship between pets, technology, and human interaction, rendering isolated pet devices obsolete.

The invention relates to the technical field of operation error recognition, and discloses an operation error recognition method and system based on human-computer interaction, and the method comprises the steps: collecting man-made interaction behaviors through a front end; carrying out behavior analysis on the interaction behavior, and carrying out primary recognition on errors; transmitting the operation instruction of the interaction behavior which is not judged to be wrong in the primary identification to an execution part, and carrying out secondary error identification based on mechanical constraint in the execution part; analyzing the execution part behaviors to finish the final recognition of errors; after identification is completed, execution partition is performed on each result which is identified as an error, and an execution part rejects an interactive behavior of execution errors, so that operation errors are prevented. Safety and accuracy in complex interaction tasks are greatly improved, and the limitation that in the prior art, operation errors are difficult to detect and process in real time is overcome.

A system includes a hardware processor configured to execute a machine learning (ML) model training pipeline to train an ML model using data relevant to a world of a digital persona to provide a dialogue model, generate, using the dialogue model, first conversational outputs, train the dialogue model, based on the first conversational outputs, to avoid hallucinations and / or undesirable expressions to provide a guardrailed dialogue model, generate, using the guardrailed dialogue model, second conversational outputs, train the guardrailed dialogue model, based on the second conversational outputs and persona data identifying interaction characteristics of the digital persona to provide a persona-specific model, generate, using the persona-specific model, a response to a scripted question, determine a quality score for the response, and further train the persona-specific model or validate the persona-specific model for human interaction, depending upon whether the quality score fails to satisfy or satisfies a quality criterion.

The invention discloses a virtual human interaction method and device, related equipment and a computer program product, and relates to the technical field of artificial intelligence, and the method supports the creation of more than two virtual human roles, can respond to a virtual human role creation request, can create more than two virtual human roles according to role identifiers, and improves the user experience. And respectively generating video streams of the virtual human roles through different virtual human engines. More than two virtual human roles can be controlled at the same time, the corresponding video frames in the video streams of more than two virtual human roles are merged, and the merged target video stream is generated and pushed to the client for playing. According to the method, more than two virtual human roles can be combined into one video stream, the same-screen interaction of more than two virtual humans is realized, the number of the interactive virtual human roles is increased, the diversity of virtual human display is improved, the expressive force of the virtual humans is enriched, the virtual humans can be more vivid in a more complex scene, and the user experience is improved. And personalized requirements of users are met.

Dynamic outputs for inducing interactions with autonomous vehicles is described. In one or more implementations, a system includes an output device configured to produce audible signals in an environment outside an autonomous vehicle, and at least one processor operatively coupled to the output device. The processor is configured to detect one or more characteristics of the environment, dynamically generate, based on the characteristics, an audio output for inducing human interactions with the autonomous vehicle, and cause the output device to transmit the audio output as the audible signals produced in the environment for facilitating a human interaction with the autonomous vehicle.

The invention belongs to the technical field of computer vision, and is mainly developed around character interaction detection (HOI). Aiming at the defects of weak semantic understanding ability, insufficient classifier semantic clue utilization, prominent long-tail distribution problem and the like of an existing HOI detection method in a complex scene, such as difficulty in accurately capturing interactive semantic essence due to shielding and illumination influence, incapability of mining tag semantic association due to random initialization weight, poor detection effect of rare interactive categories and the like, the invention provides a method for detecting the semantic association. The invention provides a character interaction detection method based on multi-modal information fusion. According to the method, a text label semantic enhancement classifier is introduced, multi-modal features are fused into a character interaction detection classification head, a multi-modal instance classifier is realized based on CLIP, and finally, results of the two classifiers are fused. The method specifically comprises the steps of extracting image features through a backbone network, performing set prediction through a Transf ormer encoder-decoder, extracting text embedding features through a multi-modal label semantic embedding module, initializing a linear classification layer weight by using a text embedding vector to perform interactive prediction, performing prediction by using a multi-modal instance semantic classifier, and fusing prediction results through a multi-source fusion module. And finally, a detection result with higher precision is obtained.

The invention provides a virtual digital human interaction method and system based on digital twinning, and the method comprises the steps: S1, obtaining an operation instruction of a digital human, carrying out the preprocessing of the obtained operation instruction of the digital human, and obtaining a processed operation instruction of the digital human; s2, triggering a cloud rendering configurator to complete interaction between the digital human and the digital twin system based on the processed digital human operation instruction, and updating the real-time state of the digital twin system; and S3, encoding the updated real-time state of the digital twin system into a pixel stream video, and transmitting the pixel stream video to a client to realize real-time state synchronization of the digital twin system.

Components for a medical infusion fluid handling system, such as an APD system, in which one or more lines (such as solution lines), spikes or other connection ports may be automatically capped and / or de-capped. This feature may provide advantages, such as a reduced likelihood of contamination since no human interaction is required to de-cap and connect the one or more lines, the spikes, or the other connection ports. For example, a fluid handling cassette may include one or more caps that cover a corresponding spike and include a raised and / or recessed feature to assist in removal of the one or more caps from the cassette. A solution line cap may include a hole and a recess, a groove or other feature to engage with a spike cap and enable removal of the spike cap.

The invention discloses a memory network enhancement method for experimental interaction behavior recognition and application, and belongs to the character interaction behavior recognition technology in video image recognition, a video level graph network is used for extracting global features of each category in a data set, and memory items of the memory network are initialized to serve as supplementary information to reduce the intra-class distance; extracting local features of a single video by using a region-level graph network, capturing detail information of an interaction behavior, and fusing the detail information with global features in a memory network to enrich sample features; and designing memory embedding loss to enlarge the inter-class distance between similar samples, thereby realizing accurate identification of the interaction behavior, and further improving the model robustness. According to the invention, accurate identification of experimental interaction behavior categories in the experimental video is realized, and the overall performance of the model is excellent.

The present invention facilitates communication between sign language users and machines by translating sign language and text using AI models, deep learning computer vision, and word embeddings. Users interact via sign language, captured and processed through deep learning and NLP modules. The system converts sign language videos into text, constructs coherent sentences, and generates contextually appropriate responses using a Retrieve and Generate (RAG) model. Responses are translated back into sign language videos, spelling out words not found in the dictionary. If requested, a human agent can respond. Key features include high-accuracy recognition, context-aware response generation, dynamic vocabulary updates, and optional human interaction. The method ensures efficient processing with LLM, embedding techniques, and deep learning, optimizing translation accuracy and user experience. The system adapts to multiple languages and dialects by training on specific sign languages, making it applicable globally.

The present application relates to an Artificial Intelligence (AI) hand type device that emulates human hand capabilities, delicately to autonomously identify and pick up small and fragile objects, including but not limited to nails, screws, and microchips, with precision. Equipped with advanced sensors, algorithms, and AI, the device accurately locates object positions and angles using computer vision technology and a high-resolution camera. It performs quality inspection analysis, object placement, movement and mechanical operations autonomously, based on AI decisions according to the real time perspective of its surrounding environment.The device's AI-driven robotic arms adapt their grip strength, pressure and temperature for handling tiny delicate items such as eye-glass screws and larger objects such as motorized drill machines. With the ability to learn from domain experts, the present device autonomously executes tasks based on past experiences.Notably, the device allows human interaction through voice commands, user interface, or physical assistance to teach it object identification, precise location detection, quality control readings and mechanical operations to perform a certain skilled task.

Components for a medical infusion fluid handling system, such as an APD system, in which one or more lines (such as solution lines), spikes or other connection ports may be automatically capped and / or de-capped. This feature may provide advantages, such as a reduced likelihood of contamination since no human interaction is required to de-cap and connect the lines, spikes or other connections. For example, a fluid handling cassette may include one or more caps that cover a corresponding spike and include a raised and / or recessed feature to assist in removal of the cap from the cassette. A solution line cap may include a hole and recess, groove or other feature to engage with a spike cap and enable removal of the cap.

The present invention belongs to the field of smart education and virtual reality technology. The present invention discloses a smart teacher system and method for integrated teaching of virtual humans and real teachers. The system includes: an MR access synchronous classroom module, which is used to access the first-person video stream in the MR device into the synchronous classroom for live broadcast; a virtual human interaction module, which is used to interact with the virtual human through voice, and the virtual human interaction module includes a voice control SDK and client voice interaction logic. The present invention can reduce the live broadcast delay, making it convenient for students to observe the teacher's live demonstration through the smart blackboard screen; it can realize the function of users interacting with virtual humans through voice; it can automatically adjust the position of virtual content when the user moves or the environment changes to ensure its synchronization with the real world, provide users with a smooth and efficient remote interaction experience, and realize seamless connection and intelligent assistance of teaching activities.

The invention relates to the technical field of computer vision, and discloses a figure interaction detection method based on spatial fine-grained context interaction feature fusion, which comprises the following steps of: firstly, performing target detection on an input image to obtain a target detection result set and a person-object pairing feature; performing gridding projection on the image to obtain image global features, and inputting the image global features into a spatial fine-grained feature learning module to obtain spatial fine-grained features; inputting the spatial fine-grained features and the human-object pairing features into a spatial context interaction feature fusion module to obtain spatial context interaction features, and inputting the spatial context interaction features and the spatial fine-grained features into a visual encoder to obtain enhanced human-object pairing features; obtaining an interaction category score according to the feature and a text embedding feature of a character interaction category; and iteratively optimizing the character interaction detection model until convergence. According to the method, the description of local details of a visual space is enhanced, the context relationship between a person and an object is enhanced, and the accuracy of person interaction detection is improved.

The invention provides a virtual human interaction action generation method for unmarked augmented reality equipment, and the method comprises the steps: obtaining a head posture, a hand posture and a first-person visual angle image sequence of a user through a built-in sensor of an augmented reality helmet, and jointly reconstructing a whole-body posture of the user on the basis; generating an interaction action of a virtual human according to the reconstructed user posture; according to the method, the interactive action of the virtual human can be more natural and real and is kept consistent with the actual behavior of the user; in the field of man-machine interaction, the prior art depends on user control and needs additional hardware equipment and manual participation to maintain an interaction process; or depending on predefined conditions, generalization and practicability are poor; compared with the prior art, additional manual intervention and predefined design are not needed, interaction with the natural response virtual human can be achieved only through the lightweight augmented reality helmet, hardware dependence and labor cost are reduced, automation and usability of the method are promoted, and interaction convenience and immersion are improved.

The invention belongs to the technical field of smart education and virtual reality, and discloses a smart teacher system and method for fusion teaching of virtual people and real teachers, and the system comprises an MR access synchronous classroom module which is used for accessing a video stream of a first visual angle in MR equipment into a synchronous classroom for live broadcast; the virtual human interaction module is used for interacting with a virtual human through voice, and the virtual human interaction module comprises a voice control SDK (Software Development Kit) and client voice interaction logic; according to the invention, the live broadcast time delay is relatively small, and students can conveniently observe the live broadcast demonstration of teachers through the smart blackboard screen; the function that the user interacts with the virtual human through voice can be realized; according to the technical scheme, when the user moves or the environment changes, the position of the virtual content can be automatically adjusted, synchronism of the virtual content and the real world is ensured, smooth and efficient remote interaction experience can be provided for the user, and seamless connection and intelligent assistance of teaching activities are achieved.

The invention relates to the technical field of advertisement design, and discloses an AI virtual human interaction method and system, and the method comprises the steps: receiving original demand data inputted by a user, generating a brand feature vector, and generating first virtual human data, first line data and first product model data based on the brand feature vector; and performing cooperative adjustment based on the ith interaction instruction to generate a virtual human image. By implementing the method and the system, the functions of full-process intelligent design, data processing, collaborative generation, interactive response, scene adaptation and the like are improved, an efficient and complete virtual human image customization scheme is formed, and the problem that multi-source heterogeneous data are difficult to comprehensively analyze when user requirements are processed by a traditional method is solved. The problems of large deviation between generated content and user expectation and poor scene adaptability caused by lack of a collaborative adjustment mechanism of a virtual human image, lines and a product model, low processing efficiency of a modification instruction and incapability of ensuring cross-modal consistency in the prior art are solved.

Dynamic outputs for inducing interactions with autonomous vehicles is described. In one or more implementations, a system includes an output device configured to produce audible signals in an environment outside an autonomous vehicle, and at least one processor operatively coupled to the output device. The processor is configured to detect one or more characteristics of the environment, dynamically generate, based on the characteristics, an audio output for inducing human interactions with the autonomous vehicle, and cause the output device to transmit the audio output as the audible signals produced in the environment for facilitating a human interaction with the autonomous vehicle.

The invention discloses a virtual human marketing method, system and device based on AI personality analysis, and the method comprises the following steps: collecting interactive dialogue data of a user and a virtual human in real time through a natural language processing technology, and extracting a multi-dimensional personality feature vector of the user through a deep learning model; according to the user personality feature vector, virtual human interaction parameters most matched with user personality communication are generated through a dynamic parameter adjustment model, and an emotional value is provided for the user; analyzing a short-term emotion demand vector of the user based on the real-time dialogue data; the system background constructs an emotion demand value vector matrix of the commodity library. Based on personality features and emotional features of the user, the virtual human can be close to the emotional demand of the user, and the use stickiness of the user to the system is improved; on the basis, the win-win situation of commercial value is achieved, virtual human parameter dynamic adjustment is added to enable the average dialogue duration of the user, more information of the user can be obtained, and the user portrait is more accurate.

Various arrangements for monitoring for contactless human interactions and health using a single radar transmission modulation mode are provided. Radar chirps may be output by a radar sensor operating in a burst mode. The burst mode radar data stream may be monitored for a contactless human interaction performed by an active user. The burst mode radar data stream may be converted to a virtual continuous mode radar data stream. Health monitoring of an inactive user may be performed using the virtual continuous mode radar data stream.

The invention discloses a hand contact state detection method and system, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: inputting a to-be-detected image, and extracting bounding boxes and initial features of hands, objects and people in the to-be-detected image through a backbone network and an auxiliary detector; analyzing a spatial and semantic relationship between a hand and a detected object through a hand-object relationship module, and outputting a confidence score related to hand-object interaction; modeling an interaction relationship between the hand and the detected person through a contrast space refining module, improving the interpersonal contact recognition capability in a complex scene by the contrast space refining module based on contrast learning and spatial relationship enhancement, and outputting a confidence score related to hand-person interaction; and predicting the contact state of each detected hand in combination with the confidence score related to the hand-object interaction and the confidence score related to the hand-human interaction. The method can be applied to human-computer interaction, video monitoring, social behavior analysis, virtual reality and the like.

The present invention relates to the field of human-computer interaction technology, and specifically relates to a human motion prediction method, device, and medium for human-computer interaction scenarios. The present invention extracts human motion features and robot motion features from the historical motion sequences of the human body and the robot, aligns the human motion features and the robot motion features, and then extracts the features generated by the human body during the interaction process based on the aligned motion features of the two, that is, human interaction features. Finally, according to the human interaction features, the trajectories of each joint point of the human body are predicted. From the above analysis, it can be seen that the present invention not only considers the influence of the robot on the human motion trajectory in the human-computer interaction scenario, but also aligns the motion features of the two to alleviate the inherent differences between them, and finally improves the prediction accuracy of the human motion trajectory.

Machines, devices, and other objects are configured to use authorization tokens to verify object identities without human input. In examples, the object uses a password to validate the object's identity to an authorization server to obtain an access token for use in multiple applications. In another example, the object uses a certificate to validate the object's identity to an authorization server to obtain an access token. In other examples, any other suitable identifying data may be used to validate the object's identity to an authorization server to obtain an access token. The process of using passwords, certificates, or other validation processes to obtain tokens or other authorization mechanisms allows the object to authenticate themselves without human interaction and to use a single identity to access services from multiple service providers that trust a central authorization server.

The invention discloses a local boar remote semen collection training method based on simulation human interaction, and particularly relates to the technical field of artificial auxiliary breeding, the method comprises the following steps: obtaining environment parameters, calculating a situation interaction adaptation index, and when the situation interaction adaptation index accords with a preset condition, carrying out remote semen collection on a local boar; executing a humanoid behavior path, collecting boar response data to construct behavior cognition connection indexes, inputting the two indexes into a training model to output a cognition transition prediction score, if the score is lower than a threshold value, reconstructing the behavior path and performing re-evaluation, and if the score is continuously higher than the threshold value for m times and the fluctuation of the connection indexes is smaller than a set range, triggering a real person intervention process; according to the method, quantitative evaluation of pig house environment adaptability, dynamic perception of target boar behavior response and training decision closed-loop control based on double indexes and a machine learning model are achieved, it is ensured that semen collection training is efficiently promoted under the conditions of high environment adaptability, sufficient individual response and stable cognitive trust, and the semen collection efficiency is improved. The training success rate and the intelligence level are improved.

The invention relates to a social subject memory simulation system and method based on a large language model, and belongs to the technical field of computers. According to the method, online and offline multi-source information fusion is realized, and behavior and situation characteristics of social subjects can be comprehensively described; a large language model semantic comprehension capability is introduced, so that the intelligent level of memory retrieval and information fusion is improved; dynamic dump and index retrieval of long-term memory are supported, so that subjects can keep semantic coherence and behavior consistency in multiple rounds of interaction; the authenticity of information spreading and interaction between social subjects is enhanced, and technical support is provided for an intelligent social system, virtual human interaction and social simulation.

Disclosed are a system and method for multimodal robot-human interaction using hand gestures and facial recognition for automated service robots. The system includes a robotic system with an integrated camera for capturing visual data, enabling hand gesture recognition and facial profiling. A control module processes this data to identify gestures and facial profiles, facilitating real-time interaction and decision-making. The gesture recognition module interprets hand gestures to control the robotic system's operations, while the facial recognition module identifies customers for personalized interactions. A communication interface supports interaction via text, voice, and video. The system is further enhanced by sensors for improved accuracy and a display module for visual feedback. The method involves capturing and processing visual data, interpreting gestures, and executing predefined actions, thereby streamlining tasks such as order and confirmation processes.

The application discloses a human-object interaction detection method based on AutoHOINet, comprising the following steps: obtaining the visual modal vector of the to-be-detected image from the to-be-detected image; through a candidate image construction module, extracting the human object and the object object in the to-be-detected image, and reconstructing the same to generate a candidate image; using a human latent relationship mining module, performing human correlation analysis and latent relationship mining to find various latent interaction relationships between the human object and the object object; with the help of a human relationship reasoning module, screening out target relationships from the latent relationships to generate pseudo labels to guide the learning process of the HOI model; finally, using the generated pseudo labels to guide the learning of the HOI model, so as to realize the detection of human interaction. The application can be better than the current weakly supervised HOI model using image-level interaction labels on two benchmark datasets without using artificial labeled HOI label data and weak supervision.

1. The name of the design product: service type single-arm robot. 2. The use of the design product: used as a service type single-arm robot, used for personal care, health physiotherapy, rehabilitation assistance, or related services in the context of human interaction in commercial service scenarios. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

This application provides a video annotation method, electronic device, and storage medium. The method includes: acquiring the original video and user-annotated information for the first frame of the original video; generating an initial contour mask based on the SAM3 model and the first frame and its annotation information; determining whether the number of frames in the original video exceeds a preset frame count threshold; if so, dividing the original video into multiple sub-videos; performing temporal propagation annotation on each sub-video sequentially based on the initial contour mask to obtain the initial contour mask for each frame in the original video; and generating the annotation result of the original video based on the original video and the initial contour masks for each frame in the original video. This application generates the initial contour mask based on the SAM3 model, requiring only minimal human interaction to automatically locate the target and identify its state, significantly reducing the workload of manual frame-by-frame annotation and lowering labor costs.

A zero emissions, autonomous vehicle system includes a zero emissions, autonomous vehicle configured to navigate one or more roadways independently of a human driver and including an electrical power source electrically coupled to a motor of the vehicle; a passenger compartment within an interior volume of the vehicle and configured to support human passengers; and a vending system mounted at least partially in the interior volume and electrically coupled to the electrical power source. The vending system includes an accessible enclosure that includes at least one temperature-controlled compartment configured to store vending products; and a control system including a user interface and configured to operate the accessible enclosure to provide human access to the temperature-controlled compartment based on human interactions with the user interface.