120 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.

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.

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 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.

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.

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 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 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 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.

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 haptic human-machine interface device comprising a rotary knob with a human interaction element, directly driven by a rotary electromagnetic interaction means comprising a stator assembly having a plurality of coils arranged on ferromagnetic teeth forming coil supports and a rotor assembly comprising a magnet having a plurality of axially magnetized poles, the position of said rotor assembly being directly measured by an angular sensor, said device further comprising an electronic control circuit controlling the intensity of the supply current to said coils as a function of the angular signal provided by said sensor on the one hand and a haptic profile recorded in a memory of said electronic control circuit on the other hand, to provide a torque that adds to or subtracts from the torque applied to the knob by said user,characterized in that said stator is fixed and has at least two ferromagnetic teeth forming coil supports, extending axially in the direction of the magnet, said disc magnet having alternating axial magnetization, interacting with said ferromagnetic stator teeth. Abbreviated figure: Figure 1,

The disclosure provides systems / methods of predicting future actions from a video. The disclosed systems and methods can use a video of human interactions with an object as input to predict future human actions. The disclosed systems and methods jointly detect the gaze of the human in the video and the action (or human-object interactions (HOI)) of the human in the video to predict a future gaze. The detected gaze and action, as well as the predicted future gaze, can be used to predict future actions (or HOIs) of the human in the video.

The present application relates to a kind of high-efficiency adaptive picture character interaction detection method and system.The system contains two kinds of operating modes: training-free mode: system utilizes pre-trained target detector and multimodal model, constructs the concept guide of multi-branch character interaction memory module, to simultaneously store specific domain visual knowledge and universal domain semantic knowledge, complete character interaction detection;Fine-tuning mode: utilize lightweight adapter based on attention mechanism, for the visual encoder in model is incorporated into instance-level priori knowledge, while updating the knowledge stored in character interaction memory module, obtain better character coding features.The model under training-free in the present application can complete character interaction detection task and obtain competitive performance in the case of only a small amount of samples, the model under fine-tuning mode can obtain more spatially sensitive pair relationship features, so that character interaction relationship detection is more accurate.

The invention discloses a face recognition identity management method and device, electronic equipment, a storage medium and a vehicle, and relates to the field of identity management. The equipment permission information comprises equipment permission information based on an identity (ID); acquiring face recognition information; binding an identity ID according to the face recognition information; binding an identity ID based on the face recognition information, and obtaining a device permission; and according to the obtained equipment authority, the equipment responds to an instruction based on the face recognition binding identity. According to the scheme, the identity ID is bound through face recognition, so that the authority barrier among the devices is broken through, and the devices which are not integrated originally can perform data interaction and data sharing based on the same identity ID. Identity (ID) is bound through face recognition, data processing is carried out on human-to-human interaction, human-to-human data interaction and data sharing based on equipment are formed, and human-to-human interaction is achieved.

A computer-implemented system facilitates controlled social interactions in public venues without exposing users outside those venues. A server receives location signals from user devices, confirms presence within a venue geofence using stored boundary data and short-range checks, and issues venue-bound session tokens. The server computes a venue “vibe” profile by fusing audio features, occupancy estimates, and explicit user feedback, and returns the profile to devices. Messaging between devices is authorized only while both present valid tokens; authorization is revoked and venue-scoped identifiers and message metadata are purged when a device leaves or a token expires. Optional features include Live-Peak time-gated live view with credit-based extensions, administrative boundary refinement, and tokenized payments to venue merchants upon acceptance of offers. The system supports interactions beyond dating, including friendship, group coordination, and professional networking.

Robot control systems, methods, control modules, and computer program products are described that utilize one or more large language models (LLMs) in order to achieve at least some degree of autonomy. The robot control parameters and / or instructions may advantageously be specified in a natural language (NL) and communicate with the LLM via NL prompts or queries. The NL response from the LLM may then be converted into robot control parameters and / or instructions. In this manner, the robot control system may utilize LLM to enhance autonomy of various operations and / or functions, including but not limited to task planning, motion planning, human interaction, and / or reasoning about the environment.

An apparatus and method for automated preparation and delivery of a beverage. A first cup-transfer-mechanism is configured to receive a cup from a cup dispenser and to move the cup along a first axis within the apparatus during the preparation of the beverage. A second cup-transfer mechanism is configured to receive the cup containing the beverage from the first cup-transfer mechanism after the beverage has been prepared, wherein the second cup-transfer mechanism is configured to move the cup along at least one of a second axis and a third axis within the apparatus, wherein the first axis, the second axis, and the third axis are mutually perpendicular. A pick-up station where the customer picks up its beverage is configured to receive the cup from the second cup-transfer mechanism and to make room to receive subsequent cups without requiring human interaction. Multiple beverages can be prepared and delivered simultaneously.

Human interaction with products and services can be difficult to measure or quantify. As an example solution, a computer-implemented method is provided. The method includes receiving human perception data in response to an external stimulus over a period of time. The human perception data includes a plurality of time periods. The method further includes determining an emotional response based on the human perception data. The method further includes assigning a numerical value to the emotional response. The method further includes calculating a change in the emotional response over the plurality of time periods over the period of time.

The present invention provides a liquid hazardous chemical loading and unloading controller and a loading and unloading control method thereof, comprising: obtaining user identity information, retrieving a list of loadable and unloadable article information corresponding to the user, obtaining a remote control program issued by the user, determining the liquid properties for loading and unloading hazardous chemicals and the quantity of articles for loading and unloading hazardous chemicals according to the remote control program, searching for the liquid properties in the list of loadable and unloadable article information, obtaining loading and unloading conditions for loading and unloading hazardous chemicals, establishing a loading and unloading plan according to the loading and unloading conditions and the quantity to be loaded and unloaded, controlling a preset manipulator to perform loading and unloading work according to the loading and unloading plan, supervising the loading and unloading work corresponding to each preset manipulator at the loading and unloading site during the loading and unloading process, establishing and displaying a loading and unloading report, remotely controlling the manipulator to perform work, reducing human interaction and operation interaction, and realizing unmanned and fully automatic intelligent operation on site.

AI has been evolving to be able to learn various information regarding an individual and to better express the personality and features of the individual. In addition, attempts have been made to make use of such AI to facilitate human-to-human communication. The present invention solves the problem of there being no effective method for making use of AI for smooth communication and human-to-human interaction.　A "personalized AI" is made to speak as a proxy for an individual. By proceeding with speaking, a conversational flow is created. The AI is useful for smooth communication and human-to-human interaction because the individual sees and hears the conversational flow and can trade off with the AI in the middle of the conversation or newly join the conversation as a guest, whereby the conversation can be structured more easily than when constructing a conversation from the beginning.

System and methods for adapting a smart phone or a tablet for use as a hybrid smart device able to perform different functions such as a smart camera, smart display, edge processing unit, or other hybrid functions, referred to as a modified or hybrid device. The modified device is based on a set of smart device components which has its battery removed and is instead powered by alternative circuitry. The modified device is also outfitted with a variety of modifications which allow it to operate in an extended temperature range. The modified device is loaded with software and circuitry which allow it to operate automatically without human interaction. The modified device can perform additional functions, but can also retain beneficial smart device functions and features.

The present application belongs to the field of human interaction relationship detection, and discloses a human interaction detection method based on dynamic noise reduction distillation and prior purification, wherein a global encoding feature is generated for an image sample through an encoder; a learnable query, a real query and a noise query are constructed, the real query and the noise query are added to obtain a joint query, and the learnable query and the joint query are spliced to generate a mixed query; the global encoding feature and the mixed query are input into a decoder, the output of the decoder is extracted to obtain a student prediction set and a teacher prediction set, and a total loss is calculated for optimization update. The method effectively balances the noise reduction strategy and the knowledge distillation, minimizes the influence of negative samples in query distillation, and retains positive samples at the same time.