53 results about "Empathy" patented technology

The invention discloses a robot anthropomorphic interaction method based on multi-modal emotion recognition and customized portrait generation. The method comprises the following steps: S1, dynamically fusing multi-modal emotions; the method comprises the following steps: S1, synchronously acquiring voice, visual and text signals through a multi-source heterogeneous sensor, capturing a user voice stream by a high-fidelity microphone array, and extracting acoustic characteristics such as intonation and speed, S2, performing cross-modal reasoning; s3, synchronously generating contents; step S4: style migration; step S5, anthropomorphic voice and expression generation; according to the method, man-machine interaction emotion is analyzed and generated by utilizing a large language model and multi-modal information fusion, the singleness of interaction emotion and the deficiency of emotional sharing ability are avoided, a strong emotion interaction characteristic is achieved, the image of the robot is obtained through a generative technology and can be migrated to any image, the limitation that a specific image is independently made is broken through, and the interaction effect of the robot is improved. The advantage that one robot can be suitable for different scenes is achieved.

The invention relates to the field of treatment healing dialogues, and particularly discloses a large model dialogue system for psychological treatment healing, which is characterized in that word segmentation processing of consultation texts is carried out by using a deep learning-based natural language processing technology and a coding mode, and then semantic embedding coding is carried out on word granularity description of each consultation text after word segmentation, so that a large model dialogue is obtained; and intelligently generating a recommended dialogue strategy tag according to the feature jump semantic propagation representation between the word granularity description semantic embedding features of each consultation text, and obtaining a dialogue text based on the tag and the consultation text. According to the method, the intention and the emotional state of the user can be better understood, and according to the real-time emotional state and the consultation content of the user, a common-situation or question-asking strategy is flexibly selected, so that a more personalized response is provided, and the problem of improper question asking or insufficient common-situation asking in a traditional system is avoided.

A system using large language models (LLMs) with multimodal inputs that has a low latency (within 500 ms) response time is described. This low latency produces a response that leads to a more engaging and empathetic user experience with all calculations done on consumer hardware. In so doing, the LLM derives the emotional state of the user using message sentiment analysis and / or face behavior and / or voice parameters that leads to a more engaging and empathetic user experience. This occurs by reviewing: (a) the user's mood state while providing an input prompt to the LLM in the current turn; (b) the user's mood and mental states while reacting to the LLM's response in the previous turn; (c) the sentiment of the LLM's response in the previous turn; and (d) the desired user mood and mental state as determined by the system's empathetic goal.

Systems and methods are described for providing empathy corrections and predictions to various communications. In some aspects, a method may include obtaining training data to train a plurality of empathy correction models, where the training data is obtained through empathy games that elicit labels indicating an empathy score for individual statements and indicating at least one empathy characteristic upon which the empathy score is based. At least one statement from a communication platform may be obtained and an empathy score may be determined for statement using the empathy corrections models. The empathy score may be compared to an empathy threshold, and if the empathy score is below the empathy threshold, at least one correction to the statement may be determined to improve empathy of the first empathy characteristic, using the plurality of empathy correction models. The correction may then be provided to the communication platform.

The invention relates to the technical field of robots, in particular to an accompanying robot control method and an accompanying robot. The accompanying robot control method comprises the following steps: presetting a series of mood values based on different expressions; a series of interactive behaviors are preset, and different interactive behaviors correspond to different mood values; obtaining the current performance of the target, and automatically adjusting the current mood value according to the obtained current performance; outputting a corresponding interactive behavior according to the current mood value; in the scheme, the accompanying robot can adjust the own mood value by sensing the external emotion, that is, the accompanying robot can adjust the own emotion by sensing the external emotion and make the interactive behavior corresponding to the current emotion, so that not only can the target emotion be captured, but also the own emotion can be owned, and real bidirectional emotion interaction is realized; and the deep condition sharing between the accompanying robot and the target is realized.

The invention discloses a man-machine dialogue method and system based on retrieval enhancement, and relates to the technical field of artificial intelligence. The man-machine dialogue method comprises the steps that firstly, innovative example retrieval is introduced, personalized example pairs with rich information are selected, and then four cognitive relationships are recognized through a preset common sense conversion model; the understanding of the situation and implicit psychological state of the help seeker is deepened. Finally, the multi-source knowledge is fused through the support decoder, it is ensured that response generation has the same psychology and cognitive consciousness, and the dialogue experience feeling of the user is improved.

The invention discloses a psychological health support dialogue generation method based on multi-modal input. The method comprises the steps that 1, text data are acquired and preprocessed; 2, calculating context similarity to obtain a high-quality example set; 3, extracting key visual clues from the image by utilizing a visual language model, wherein the key visual clues comprise facial expressions, actions and emotions; 4, integrating an external common knowledge base to understand the emotion and intention of the user; 5, generating a consultation strategy by using a large language model; and 6, dialogue generation in combination with the context. According to the method, the visual information, the dialogue context and the consultation strategy are comprehensively utilized, the performance of a large language model in the psychological health dialogue is enhanced, more detailed and similar supportive replies can be generated, the emotion recognition and response quality are remarkably improved, the emotion state of a user can be comprehensively understood and responded, and the user experience is improved. Therefore, the mental health support effect is effectively improved.

The invention provides a customer service response system and method based on artificial intelligence. The customer service response system comprises a client, an intelligent access network, a core AI engine system, a multi-modal understanding system, a federated learning coordinator, a knowledge management system, an emotion calculation engine system, a dynamic decision system and a digital ethical arbitration system. According to the method, multi-dimensional data such as texts, voices and vision are fused through a multi-modal understanding system, more accurate user intention recognition is achieved in combination with a cross-modal entanglement network and intention conflict detection, and the misjudgment problem caused by traditional single-modal analysis is reduced; the emotion calculation engine is combined with physiological signals, voice emotion analysis and micro-expression recognition to dynamically sense the emotion state of the user, so that customer service interaction is more similar.

The invention discloses an elderly emotion bidirectional interaction method based on an artificial intelligence technology, and relates to the technical field of elderly emotion interaction, and the method comprises the steps: 1, collecting the audio data and video data of the elderly; 2, respectively extracting audio feature data and visual feature data, and converting the audio data into audio transcription text data; 3, inputting the audio feature data, the visual feature data and the audio transcription text data into an emotion calculation model to obtain a context query version, a user emotion vector and a similarities vector; 4, inputting the features into a core chat model to obtain a reply table; and step 5, calculating the final score of each answer in the reply table and sorting, and selecting a random reply from the candidate replies with the final score greater than 0.7 and the total score ranking the top 10% as a final reply. The sentiment interaction method has the advantages of improving sentiment interaction accuracy and humanization of the elderly.

To ensure a position as a friendly partner with a user by causing an empathy talking when providing information, in addition to an essential purpose to provide the information through speech.SOLUTION: A behavior control system includes: an input unit that accepts input of sentences; a processing unit that performs specific processing using a sentence generation model on the sentences accepted by the input unit; and an output unit that controls a behavior of a robot to output results of the specific processing. The processing unit identifies emotion transfer points by analyzing a sentence form including word decomposition and phrase connection of the sentences input from the input unit, converts the sentences into speech information for speaking, and assigns an emotional expression including at least one of high / low sound, speed, sound intensity, and inflection to each of the emotion transfer points when speaking based on the converted speech information.SELECTED DRAWING: Figure 3

The invention provides an intelligent text-to-speech conversion system simulating human emotion, which realizes accurate matching of user emotion recognition and emotion speech through cooperation of double agents, enables AI reply to have'emotional 'ability, avoids mechanical feeling, enables man-machine interaction to be closer to a human natural communication mode, and improves the user experience. The prefabricated emotional sound library supports multi-emotional category and multi-scene parameter configuration, can flexibly adapt to emotional requirements of different scenes, adopts a double-agent asynchronous processing architecture, decomposes emotional recognition and content generation into parallel tasks, and compared with a traditional mode, shortens the overall response time, improves the real-time performance of the system, and improves the user experience. And meanwhile, expansion of the prefabricated emotional sound library is supported, and the future technology upgrading and scene expansion requirements are met.

The invention discloses a non-contact mental and psychological assessment and estrus-sharing guide method and system, and mainly relates to the technical field of mental and psychological health assessment. Comprising the following steps: constructing an immersive VR assessment scene, and inducing an assessed person to naturally show mental and psychological states; non-contact acquisition equipment is adopted to separate text content and acoustic features of the voice answers; processing the facial micro-expression data, and processing the voice data and the acoustic features; processing the text content to obtain a text semantic evaluation vector; carrying out multi-modal integration to obtain a comprehensive evaluation vector; generating a next round of psychological assessment problem based on the comprehensive assessment vector, and adjusting text-to-voice parameters; and calculating a mental and psychological state evaluation value of the evaluated person based on the comprehensive evaluation vector, and outputting an evaluation report. The beneficial effects of the invention are that the method achieves the precise assessment of the mental psychology in a non-contact manner, improves the efficiency through multi-modal fusion, and dynamically guides the real expression in a common situation manner.

The application discloses a psychological intervention method and system based on dynamic intention routing and thought chain reasoning, and belongs to the technical field of artificial intelligence and mental health. The method comprises the following steps: performing feature scanning on user input through a double-track detection mechanism composed of a semantic similarity model and a sensitive word matching algorithm, and generating an emotional tendency score and a crisis risk weight; comparing the scanning result with a preset threshold, and shunting to a red early warning, yellow intervention or green general path; triggering a three-level early warning action for the red path; starting a thought chain reasoning engine for the yellow path, sequentially calling multiple logic operators, and generating a step-by-step traceable intervention strategy; calling a general dialogue engine for the green path; and dynamically adjusting the guidance intensity and the level of empathy based on user feedback. The application realizes millisecond-level recognition and forced interruption of high-risk intentions, ensures the professionalism and interpretability of the intervention process through structured reasoning, and significantly improves the safety, professionalism and response efficiency of the psychological intervention system.