29 results about "Emotion perception" patented technology

The application provides an emotion perception-based Internet of Things information technology customer service method and system, and relates to the technical field of information technology services.The method comprises the following steps: obtaining real-time message data of a user and a customer service, and transmitting the data to a cloud server in real time through an Internet of Things platform; performing text analysis on the obtained real-time message data, and analyzing the current emotional state of the user through an emotion analysis method; analyzing the intention of the user according to the real-time message data, and generating reply content through a reply database according to the current emotional state of the user; continuously monitoring the interaction data between the user and the customer service, and dynamically adjusting the reply content.The application can analyze the emotional state of the user through emotion perception technology, thereby better understanding the demand and emotional state of the user, adjusting the reply content according to the emotional state of the user, and providing more personalized, warm and considerate services that are closer to the psychological demand of the user.

This application discloses a multi-agent emotion intervention system and method based on emotion focus, relating to the field of artificial intelligence application technology. The system includes an active emotion perception module, a user profile database, and a multi-agent intervention module. The multi-agent intervention module comprises multiple agent units. The active emotion perception module generates structured emotion quadruples based on multimodal data published by the user and stores these structured emotion quadruples in the user profile database. The multi-agent intervention module loads the target user's structured emotion quadruples from the user profile database and, according to a preset emotion-focused therapy process logic, controls multiple cooperating agent units to engage in dialogue with the target user. Each agent unit is responsible for a specific stage of the dialogue. This application can improve the reliability of emotion intervention.

The application provides a personalized music automatic generation system and method fusing multi-modal emotion perception, relates to the technical field of personalized music automatic generation, collects multi-modal perception data of a target user, performs emotion cross-modal extraction on the multi-modal perception data to obtain an emotion state vector; determines a music preference vector based on historical music preference data, performs expected tendency on music preference of the target user according to the emotion state vector and the music preference vector to obtain each music expectation value of the target user under a current emotion state; predicts a current expected music type of the target user according to each music expectation value and a music matching threshold to obtain a music response degree of the target user for each music type; and generates a personalized music recommendation table according to each music response degree. The application can fuse the current emotion state of the user and the historical preference data, realize real-time dynamic adaptation driven by emotion, and improve the accuracy and timeliness of personalized music recommendation.

The application discloses a kind of fusion semantic label and BAN's multi-modal emotion autonomous identification method, it is related to cross-modal affective intelligent computing technical field.First, the semantic label is aligned to visual and speech feature space, realizes cross-modal semantic consistency mapping;Then, for the problem that local feature interaction is insufficient between modalities leads to fine-grained emotional information loss, introduce bilinear attention network, capture the high-order interaction relationship of visual-speech local feature;Finally, for the challenge that global semantic and local interaction are difficult to optimize synergistically, introduce cross-modal contrast learning mechanism, strengthen global semantic consistency.The application discloses a kind of fusion semantic label and BAN's multi-modal emotion autonomous identification method fuses multi-modal emotional data, can real-time, accurate identification human emotional state, timely intervene potential risk, provide technical basis for real-time adjustment of personalized service strategy, and can be expanded to intelligent customer service, medical monitoring and other cross-industry affective sensing scenarios.

This invention relates to an audiovisual bimodal emotion recognition system and method for assisting robot decision-making, belonging to the field of artificial intelligence and pattern recognition technology. The invention first extracts and aligns features from visual and audio signals; then, it uses domain adversarial learning to remove speaker identity interference and learn cross-domain invariant emotion features; simultaneously, it introduces mask contrast learning to enhance robustness to speech loss; next, it utilizes a video-guided attention gating mechanism to achieve dynamic adaptive feature fusion; finally, it inputs the emotion recognition results into a decision-making module and performs end-to-end joint training with the recognition task using reward signals based on interaction effects. This invention effectively solves the problems of poor generalization ability, insufficient robustness to modal loss, static fusion strategies, and perception-decision disconnect in existing technologies, providing assistive robots with more accurate, robust, and context-adaptive emotion perception capabilities, thereby driving more natural and effective human-computer interaction.

This application relates to the field of vehicle technology, and in particular to a method, device, vehicle, and storage medium for calming children's emotions. The method includes: acquiring the child's current state data; inputting the current state data into a preset emotion recognition model to obtain the child's dominant emotion, wherein the preset emotion recognition model is trained from state data labeled with emotions; matching a set of calming actions from a preset strategy library based on the dominant emotion, and controlling the vehicle to execute the calming actions based on the set of calming actions. This solves the problems of delayed emotion perception in children's emotional processing and static, singular calming strategies, enabling proactive execution of corresponding calming actions based on the child's emotions, thus ensuring driving safety.

This application provides a method, apparatus, electronic device, and storage medium for outputting perceived voice emotion tags, applied in the field of smart home device control. The method includes: acquiring user voice input information and corresponding text input information; extracting voice emotion features from the voice input information and text emotion features from the text input information; performing multimodal fusion of the voice and text emotion features to identify the user's emotional state; generating corresponding emotion tags based on the user's emotional state; and controlling at least one smart home device to perform corresponding scenario-based operations based on the emotion tags. The perceived voice emotion tag output method provided in this application enables smart home systems to proactively provide personalized service responses based on the user's current true emotions, achieving an intelligent upgrade from passive command reception to proactive emotion perception, effectively improving the naturalness of human-computer interaction and the comfort of the user experience.

This invention provides a progressive stress resilience assessment system based on a large-scale intelligent agent; it primarily addresses the problems of limited interaction methods and lack of dynamic adaptability in existing psychological state assessments. The system employs a digital human interactive agent, an emotion perception and scoring agent, an emotion guidance and planning agent, an empathy expression generation agent, and a scale scoring agent. The emotion perception and scoring agent calculates negative emotion scores based on questions and answers. The emotion guidance and planning agent generates the next question based on semantic understanding and score feedback, achieving an adaptive assessment path. The empathy expression generation agent generates system responses with emotional connection and guidance based on multiple inputs from the current question, user answers, and the next question. The digital human interactive agent presents outputs via voice and facial expressions, achieving natural interaction. The scale scoring agent manages and scores the scale items for the ruminative thinking and stress resilience stages, ultimately obtaining the stress resilience assessment result.

This application provides a method and apparatus for processing continuous emotion data in embodied dialogue agents. It achieves effective emotion perception through multimodal features and semantic mapping. An emotion modeling mechanism is constructed, combining a baseline network and contextual modulation to establish a reliable state estimation strategy. Expression control is introduced, ensuring the naturalness of emotional expression through collaborative generation and temporal planning. This method effectively addresses the shortcomings of traditional technologies in emotion recognition, state modeling, and expression control, providing technical support for embodied dialogue agents.

The application discloses an interactive psychological intervention robot dog with real-time emotion sensing capability, comprising a bionic robot dog body, a signal acquisition module, an emotion evaluation module, an execution control module, a collaborative intervention module, a remote monitoring module and a man-machine interaction module, which can monitor the physiological state of a user in real time, automatically identify the emotional state, and actively guide slow breathing training, mindfulness meditation, muscle relaxation and biofeedback training, cooperate with intervention measures such as vibration stimulation and sound and light stimulation, continuously monitor the emotional changes, adjust the intervention measures, realize closed-loop control, and meanwhile, the physiological regulation effect of the user can trigger positive behavior feedback of the robot, and improve the treatment participation degree. The application is suitable for the old, patients with anxiety and depression, and people with low motivation and low digital literacy, and can realize personalized and high-compliance psychological health support in family, nursing homes and other scenes, and has the advantages of mobility, intelligence, closed-loop regulation and control and strong interaction.

The application discloses an AI voice interaction method of an intelligent thermal printer, and relates to the technical field of thermal printers.The method comprises the following steps: S1, collecting an audio signal and processing and converting the audio signal into text, extracting voice features and voiceprint information; S2, extracting keywords through a local rule engine and matching the keywords with a keyword library; S3, if the matching is successful, generating a local instruction and voice reply; S4, if the matching fails, uploading the audio text to the cloud; S5-S8, starting a printing template, LLM reasoning, emotion model and TTS synthesis service in the cloud, determining the printing template and generating an emotional response audio stream; and S9-S10, issuing data in the cloud, playing the audio in the printer and performing a printing operation.The application realizes voice interaction and emotion perception, combines local rapid response with deep reasoning in the cloud, adapts to customized needs in multiple industries, improves operation convenience and intelligent experience, and guarantees data safety and stability.

The application relates to the technical field of game animation, and particularly provides a game animation self-adaption method based on emotion perception, which comprises the following steps: collecting multi-source signals of a player and performing pretreatment, simultaneously obtaining a current game context vector, inputting the pretreated multi-source signals and the game context vector into an emotion decoupling model, and outputting a four-dimensional emotion vector E(t) with a confidence score C(t); calculating an animation behavior identifier A id and an animation adjustment parameter set A params according to the emotion vector E(t); a game engine completes animation rendering according to the animation behavior identifier A id and the animation adjustment parameter set A params , and outputs the result to a screen; and the game engine dynamically calculates a fluency index by analyzing new player data flow generated after executing animation adjustment, and reversely fine-tunes parameters of the emotion decoupling model and a probability mapping diagram according to the fluency index. Through the above scheme, the problems of high delay, poor robustness and lack of coupling with a game context in animation self-adaption are solved.

This application provides a robot-based collaborative processing method and system for emotion perception. The method includes: acquiring and preprocessing emotion feature data to obtain effective emotion feature data; calculating a comprehensive computing load value based on real-time computing load data; dynamically adjusting a lightweight model cluster accordingly to obtain an effective lightweight model; generating multi-dimensional emotion feature vectors through fusion of the effective lightweight models; and processing these vectors to obtain preliminary emotion categories after desensitization based on user intention levels. The preliminary emotion categories and desensitized feature vectors that meet the criteria are uploaded to the cloud for analysis to obtain refined emotion analysis data, which is then distributed to the edge. The edge executes a guidance strategy while simultaneously collecting user emotion feature variable data and uploading it to the cloud to complete model optimization. This achieves collaborative processing of emotion perception between the edge and cloud, balancing dynamic computing power adaptation with user privacy protection, and improving the real-time performance, accuracy, and iterative optimization capabilities of the robot's emotion perception.

This application provides a closed-loop control method and apparatus for an emotion-aware robot, achieving accurate emotion recognition through multimodal features and deep learning. A decision-making mechanism is constructed, combining safety constraints and resource scheduling to establish a reliable execution strategy. Closed-loop optimization is introduced, ensuring continuous improvement of control through experience learning and strategy updates. This method effectively addresses the shortcomings of traditional technologies in emotion recognition, behavior control, and strategy optimization, providing technical support for emotion-aware robots.

The application discloses a kind of machine dog voice interaction systems and methods for emotional accompaniment, it is related to human-computer intelligent interaction technical field, including multimodal emotion perception and state real-time construction module, it is used to gather user voice data and facial image data, generate speech emotion vector based on voice data, generate emotion label and its confidence probability based on facial image data, establish emotion state table based on speech emotion vector and emotion label;Intelligent decision and content generation module based on emotional context, generate the interactive action and voice data of machine dog based on speech emotion vector and emotion label;Real-time interaction management and safety interrupt module, according to user feedback data, interactive action and voice data are divided into effective interaction data and invalid interaction data;Effective interaction corresponding interactive action and voice data are updated to emotional interaction memory bank;Realize from "functional interaction" to "emotional interaction" leap.

This invention discloses a speaker recognition and emotion perception method based on AR glasses. The system combines microphone arrays, cloud computing, and multi-model fusion technology to achieve real-time recognition and display of speaker identity information, speech content, and emotional state. The method first acquires multi-channel speech signals through a microphone array and improves speech quality using preprocessing techniques such as beamforming, noise reduction, speech enhancement, and voice endpoint detection (VAD). Subsequently, the system runs an ECAPA-TDNN voiceprint recognition model in the cloud to obtain speaker embedding vectors. Through acoustic features such as MFCC, Fbank, and LPCC, and an AAM-Softmax training mechanism, it identifies gender, age, and speaker identity. A CTC end-to-end speech recognition model is used to complete real-time speech transcription. Finally, an emotion recognition model based on Wav2Vec 2.0 and Transformer, combined with MFCC features, emotion embedding, and attention-weighted fusion strategies, achieves high-precision recognition of multiple emotions such as "happy, angry, sad, and calm."

The application discloses an intelligent customer service dialogue system based on multi-source data fusion and emotion perception. The system constructs a six-layer core architecture covering the user and channel layer, the interface layer, the workflow arrangement layer, the core capability layer, the state and data service layer, and the infrastructure and external dependency layer. Based on this bottom layer architecture, the system sequentially executes four core technical processing procedures: first, multi-source data hierarchical retrieval is carried out through a large model drive to fuse cross-source information; second, emotion-driven risk control is carried out to dynamically map intervention strategies; for multi-round interaction, accurate commodity context follow-up question recovery is realized; finally, a large model reasoning tool is called, and external tools are cooperated to close loop complex business. The application effectively suppresses the large model illusion, and significantly improves the retrieval accuracy, emotion perception depth and business automation processing rate of the system.

An interface customized to a detected emotional state of a user is provided. Audio signals are received from at least one microphone, the audio signals being indicative of spoken words, phrases, or commands. A wake-up word (WuW) is detected in the audio signals. An emotion is also detected in the audio signals containing the WuW. An emotion-aware processing system is configured according to the detected emotion. A voice control session is performed using the emotion-aware processing system configured according to the detected emotion.

The application discloses an emotion perception game device and an interaction method based on electroencephalogram, and belongs to the technical field of emotion recognition and human-computer interaction. The device comprises an electroencephalogram acquisition unit, a wireless sending module, a receiving and emotion recognition unit, a stable state decision unit, an application interaction unit and a display and interaction interface. The interaction method comprises the steps of electroencephalogram acquisition and sending, receiving end preprocessing and individualized emotion recognition, steady state decision, multi-modal resource mapping interaction, individualized calibration and model iteration. The application takes electroencephalogram signals as the core of emotion perception, adopts multiple discrete emotion categories, suppresses recognition jitter through confidence threshold gating and continuous counting zero mechanism, and constructs a complete interaction closed loop from electroencephalogram acquisition to virtual character motion, expression and voice linkage. At the same time, the application supports user individualized model training and edge deployment, takes into account recognition accuracy, low latency, privacy protection and environmental robustness, and is suitable for landing application of various immersive emotion interactive game products.

This invention provides a method for quantifying emotion state perception based on a continuous emotion space in speech, relating to the field of mental health assistance. Its features include the following steps: audio acquisition and preprocessing, speech representation extraction, continuous emotion state regression, derived dimension calculation, global emotion output, fixed-period emotion timeline generation, emotion state transition quantification, and structured interface output. The advantages of this invention are: it transforms the original speech signal into a continuous emotion state vector, replacing discrete emotion labels; it generates an emotion timeline sequence based on a fixed time window, enabling real-time tracking of dynamic emotion changes; it adapts to mental health scenarios, generating multi-dimensional derived dimensions to improve the scenario adaptability of emotion perception; and it constructs a full-link language emotion recognition system, achieving continuous quantification, dynamic tracking, and transfer calculation of language emotions, providing highly reliable language emotion recognition output for psychological AI.

The present application relates to the technical field of emotion recognition, in particular to a pet multi-modal emotion recognition method with individuality perception, comprising the following steps: collecting sound signals and all sound events in a long time period in a pet's calm state, extracting the mel-frequency cepstral coefficient, pitch envelope, formant and short-time energy of the sound signals, and establishing an individualized emotion steady-state baseline. The present application can effectively capture the acoustic behavior patterns of different pets in a stable emotional state by collecting pet sound signals in a calm state and extracting multi-dimensional acoustic features. Based on the baseline, the feature deviation of new sound samples is measured to form an acoustic emotion offset vector with directionality and individuality difference, which improves the accuracy and pertinence of emotion perception. At the same time of acoustic processing, visual signals are synchronously collected, and behavior parameters highly related to emotion such as tail shaking frequency and ear posture angle are extracted, so that the timing characteristics of visual behavior and acoustic characteristics form a logical pairing relationship.

Embodiments of the present application provide an information processing method and device of a smart phone system, a storage medium and a program product. In the embodiments of the present application, the recognized text information and its accumulated character number and meta information such as a silence event are input into three parallel channels of an emotion perception fast response channel, a local intention understanding pre-communication channel and a complete intention understanding service response channel; when the accumulated character number reaches different thresholds, emotion analysis and local fused intention recognition are triggered respectively, and corresponding response information is generated for response; when a silence event exceeding a set time length occurs, complete intention recognition is performed based on all the text information, and a target service system is called through a service routing engine to complete service processing and generate a response. The scheme realizes progressive response of the emotion layer, the semantic layer and the service layer, effectively eliminates call interaction delay and blank waiting period, and improves the real-time performance, continuity and user experience of telephone voice interaction.

The application discloses a large language model recommendation method based on social thinking chain reasoning, which innovatively combines social emotional feedback and thinking chain reasoning. First, an emotional perception user portrait is constructed by extracting emotional terms from user reviews and quantifying emotional preferences using a reward model. Second, user social network information is introduced, and a social thinking chain reasoning framework is constructed to simulate interpersonal influence by integrating social emotional feedback into the reasoning unit. Finally, the thinking chain is innovatively divided into emotion-based paths, and human evaluation operations such as backtracking, discarding, retaining, and aggregating are applied to simulate delicate emotional cognition. The application significantly improves accuracy in recommendation tasks, effectively alleviates confirmation bias in large language model feedback loops, enhances the explainability of reasoning results, and improves the accuracy and user experience of the recommendation system.

This invention provides a personalized emotion intervention method, device, and electronic device. The method includes: collecting multidimensional data from a target user; the multidimensional data includes physiological signals, behavioral performance, environmental context, and cultural background; constructing an emotion inference model based on the multidimensional data and formulating an emotion intervention strategy for the target user; executing the emotion intervention strategy and collecting feedback information from the target user; and optimizing the emotion inference model and the emotion intervention strategy based on the multidimensional data and the feedback information. This invention enables machines to understand the dynamic interaction of emotions, achieving a closed loop from emotion perception and state inference to dynamic and precise intervention, thus solving the problems of poor accuracy and adaptability in emotion inference in existing related technologies.

This invention discloses an intelligent fragrance control system and method based on environmental perception and emotion analysis, which has the following advantages: 1) It realizes multimodal and cross-sensor environmental and emotion perception, ensuring more comprehensive perception and higher robustness; 2) It systematically incorporates the mechanism of aromatic substances and the dose-time effect of inhalation into decision-making, improving the scientific nature and safety of fragrance strategies; 3) It adopts an online / offline learning mechanism based on time-series feedback to achieve adaptive optimization of fragrance control, improving the effect of emotion regulation and atmosphere creation; 4) It supports scenario-based and personalized configuration (such as meetings, families, treatments, etc.), taking into account privacy protection and user preferences, and providing controllable and verifiable fragrance services; 5) Through real-time data analysis and decision feedback, it enhances the system's responsiveness to dynamic environmental and emotional changes, ensuring the personalization and continuity of the fragrance experience.

This invention discloses an artificial intelligence-based automatic music composition and lyrics generation system and method, specifically relating to the field of automatic music composition and lyrics generation technology. It models the attenuation characteristics of melody pitch distribution and dynamic changes in a target acoustic environment and extracts the main emotion-carrying frequency band. It calculates an emotion transmission attenuation coefficient based on the degree of spectral attenuation, couples the emotion transmission attenuation coefficient with the temporal distribution relationship of the lyrics semantics, constructs a temporal evolution sequence of emotion perception shift, analyzes the alignment relationship between lyric syllable accents and melody beats, calculates a rhythmic clarity degradation index, quantifies the degree of rhythmic ambiguity, and unifies the emotion transmission attenuation coefficient and rhythmic clarity degradation index into an acoustic adaptation degradation state vector. Based on this vector, it jointly adjusts the melody pitch distribution, note value configuration, and lyric syllable density, adaptively optimizing the emotion expression path and rhythmic structure for different playback terminals and acoustic environments.

The application relates to the field of browser usability auxiliary technology in artificial intelligence, and discloses a webpage self-adaptive method and system based on multi-modal emotion perception. The method collects multi-source initial signals of a user, performs synchronization and pretreatment, carries out multi-modal fusion reasoning in a local edge model, obtains an emotion vector and a confidence degree, selects a strategy package S from a strategy library in combination with page context classification and individualized threshold value, executes webpage rendering adjustment on DOM, CSS and ARIA attributes through a browser content script, dynamically changes information density, readability of layout, dynamic effect and interaction complexity, records user feedback, and adopts online strategy updating with constraints on the local side; adaptive degradation is executed when resources are limited; only intermediate features are saved under privacy and permission management, and original audio and video are not externally transmitted. The application realizes real-time closed loop and individualized adaptation of emotion-strategy-page, and significantly improves the online experience of special groups such as people with attention disorders and people with anxiety.