206 results about "Dialogue management" patented technology

The invention relates to the technical field of information retrieval, in particular to an intelligent RAG knowledge base system fused with a dynamic knowledge graph. Comprising a data processing module for preprocessing original data to obtain a standardized data set; the graph construction and updating module is used for carrying out knowledge extraction on the standardized data set by utilizing a large model, constructing a knowledge graph and carrying out dynamic updating; the dialogue management and understanding module is used for recording complete information construction of a current dialogue, updating a dialogue state and calculating a retrieval weight adjustment amount; the mixed retrieval module is used for performing mixed retrieval in combination with DPR and PPR retrieval methods to obtain a first retrieval result and a second retrieval result, setting an initial retrieval weight according to the question type, and obtaining a comprehensive retrieval result in combination with the retrieval weight adjustment amount; and the response optimization module is used for outputting an optimal response according to the comprehensive retrieval result and user feedback by utilizing reinforcement learning based on strategy gradient. According to the method, the retrieval and response generation effect can be optimized, and the user satisfaction is improved.

The invention discloses a multi-modal large model dialogue method and system based on context awareness and a medium. Relates to the technical field of intelligent dialogues. According to the scheme, the dialogue request information and the perception feature information are generated in the context perception dialogue management model, and the answer strategy is determined according to the dialogue request information and the perception feature information; on the basis of various data of the user, system hot word auxiliary information provides personalized reply for the user and is more suitable for life links, accurate context perception is achieved, and enterprise product operation and development are served.

The invention discloses an intelligent customer service interaction method and system, and relates to the technical field of intelligent customer service interaction, and the method comprises the steps: obtaining a user request; calculating a comprehensive emotion score through multi-modal emotion analysis based on a user request; according to the comprehensive emotion score, classifying the user emotion state, according to a classification result, initializing a dialogue context, and constructing a dialogue context graph; dynamically adjusting the response mode of the intelligent customer service according to the emotional state and the user question in the dialogue context graph, and collecting the feedback information of the user; and dynamically adjusting the dialogue strategy based on the feedback information of the user and the dialogue context. According to the method, the emotion state of the user can be comprehensively captured from multiple aspects such as texts, voices and facial expressions through multi-modal emotion analysis, so that the accuracy and robustness of emotion recognition are improved, a reliable basis is provided for subsequent dialogue management and response modes, and the experience satisfaction degree and the service quality of the user are improved.

The invention relates to the technical field of multi-round dialogue processing, in particular to a multi-round dialogue processing method based on RAG technology dynamic cue word retrieval. Comprising the following steps of question input by a user, initial retrieval and cue word generation, question splicing and answer generation, historical dialogue management and context coding, multi-round question analysis and retrieval enhancement, complex question decomposition and parallel retrieval, multi-source information fusion and generation model optimization, iterative feedback and model tuning, and exception handling and fault-tolerant mechanism. According to the method, related information is retrieved from an external knowledge base, the model can utilize wider and more specific knowledge to generate answers and better track dialogue history and context, so that answers consistent and coherent with the dialogue history are generated, the model can generate answers on a short input sequence by retrieving and splicing related cue words, and meanwhile, the model can be used for analyzing the dialogue history. Cue words are dynamically generated according to user input and retrieval results, so that the model can better adapt to different dialogue scenes and user requirements.

The invention discloses a customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention discloses an AI (artificial intelligence) digital human interaction system, which comprises a dialogue management module for receiving emotional state information transmitted by an emotional recognition module and other information input by a user feedback processing module; tracking a dialogue state, selecting a proper response mode according to a dialogue strategy, and generating a corresponding system behavior; the system behavior instruction is transmitted to an action and expression generation module; the user feedback processing module is used for collecting and analyzing feedback data of the user; and the feedback module analyzes and processes the feedback data, extracts valuable information, and transmits an analysis result to the dialogue management module and the reinforcement learning module for guiding the improvement and optimization of the system. Comprehensive information acquisition is realized through the multi-modal information acquisition module, continuity and individuation can be enhanced through the dialogue management module, an interaction strategy is optimized through the reinforcement learning module, and continuous improvement and real-time feedback are ensured through the user feedback processing module; and the interaction effect of the whole system is more natural and vivid.

The invention discloses an intelligent form filling method based on voice interaction. A form filling system is composed of an intention recognition module, an entity recognition module, a data mapping module, an intelligent error correction module and a dialogue management module. According to the system, the traditional manual form filling operation is replaced by a voice input mode, the complexity and time cost of user input are greatly reduced, and the recognition precision of terminologies is remarkably improved by utilizing a voice recognition and natural language processing model optimized for the financial field, so that the user experience is improved. Specific fields of the form can be accurately recognized according to intention recognition and data mapping functions customized for a specific scene of financial reimbursement, the system is designed based on a multi-round dialogue management module, a user is actively guided to complete complex form filling step by step, the learning cost and the operation difficulty are remarkably reduced, and the user experience is improved. Full-process automation of voice recognition, data mapping and intelligent verification is realized, and manual intervention is reduced.

The invention relates to the field of modern man-machine interaction, in particular to an LUI and GUI method and system based on an emotion module. According to the technical scheme, the LUI and GUI method and system based on the emotion module comprise a dynamic GUI generation and adaptation module; compared with a traditional GUI system which generally depends on a preset interface design and a fixed interaction mode when interacting with a user and lacks active perception and active response ability to the emotion of the user, due to the defect, the user experience is not personalized enough, dynamic adjustment cannot be carried out according to the instant emotion state of the user, and the user experience is poor. According to the LUI and GUI system based on the emotion module, the language user interface is introduced to interact with the user, so that the defects in the prior art are overcome, the LUI can have a dialogue with the user through a natural language processing technology, and the system can call a dialogue management function by analyzing the emotion of the user, so that a response and interaction page conforming to the current emotion of the user is generated.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a law and regulation question answering system and method based on an industry large model, and the system comprises a data layer, a model layer, a service layer and an application layer. The data layer is responsible for collecting, storing and managing law and regulation data; the model layer comprises an industry large model and an RAG module; the service layer provides question and answer generation, multi-round dialogue management and security control core services; the application layer provides diversified application scenes for different user groups; the method has the beneficial effects that the general large model is finely adjusted in a targeted manner by using massive professional data in the legal field, so that the model can deeply learn logic association between connotation and extension of legal concepts and provisions and judgment goals of cases. Through the fine adjustment, the model can accurately grasp the core of the question when facing a complex legal question, provides an answer meeting a legal professional standard, and ensures the accuracy and preciseness of the answer, thereby effectively solving the problem of understanding deviation of professional knowledge.

The invention discloses an intelligent customer service dialogue management method and system fused with multi-layer memory, and belongs to the technical field of computers, and the method obtains a three-dimensional context through multi-mode context awareness, achieves the dynamic scheduling of a memory system through a memory coordinator, and combines a dynamic construction mechanism of a service situation portrait, thereby achieving the intelligent customer service dialogue management. The problem that the response of the intelligent customer service system is not matched with the complex and dynamic service demand of the user due to the single situation awareness dimension and the rigid memory scheduling mechanism is effectively solved, and the adaptive personalized service response can be generated according to the real-time change of the dialogue situation.

The invention relates to the field of data analysis, in particular to a customer service interaction system and method based on natural language processing. The system comprises a multi-channel access module used for supporting interaction of at least one channel and providing a uniform interface; the semantic understanding module is used for carrying out semantic analysis on the input problem through a natural language processing technology so as to determine a problem type and a corresponding intention; the knowledge base retrieval module is used for maintaining a knowledge base containing common questions and answers and quickly retrieving related answers through a semantic similarity matching algorithm; the dialogue management module is used for tracking the dialogue state, understanding the context and managing multiple rounds of dialogue processes; and the natural language generation module is used for converting the answers retrieved by the knowledge base retrieval module into natural languages and replying. And intelligent customer service interaction is realized by utilizing a natural language processing technology, so that efficient and accurate solutions are provided, and the customer service efficiency and satisfaction are improved.

An end-side-cloud three-in-one active chat robot system for high-emotional merchants comprises an end side, a local edge server and a cloud end, and the end side is used for collecting multi-modal data of a user and performing local lightweight real-time processing and response execution; the local edge server is used for receiving and fusing the multi-modal features and the context information from the end side, and carrying out sentiment calculation, dialogue management and active trigger decision making with medium complexity; the cloud end is used for operating a super-large-scale model and providing global knowledge management, long-term user portrait storage and model training optimization; and the end side, the local edge server and the cloud end carry out cooperative communication through an encrypted channel to form a distributed intelligent processing architecture. According to the method, global optimization is realized by integrating end-side lightweight sensing, edge multi-modal fusion and cloud long-term memory. A composite finite state machine (FSM) active questioning mechanism is combined with sentiment calculation, and is different from traditional rule type triggering. Off-line and on-line fusion scheduling and multi-agent role playing are combined to be applied to a high-emotional-quotient interaction scene, and the simulation is improved. Multi-modal emotion perception circulation is introduced, and the problem of single text emotion misjudgment is solved.

The invention provides a service data voice processing method and system based on artificial intelligence, and relates to the field. The service data voice processing method based on artificial intelligence comprises multi-mode voice preprocessing, domain enhancement voice recognition, knowledge graph driven semantic analysis, a dynamic dialogue management mechanism and a closed-loop feedback optimization system. According to the method, high-precision recognition, semantic understanding and structured processing of the service voice data are realized by fusing the domain knowledge graph, the dynamic model optimization and the context sensing technology, and the service data processing efficiency and accuracy are improved; secondly, the system comprises a voice acquisition and preprocessing module, an AI voice processing module, a business data processing module, a model optimization module and a data interface module, the efficiency and accuracy of business data voice processing can be remarkably improved, and the system is suitable for various professional scenes such as finance, customer service and medical treatment.

The invention discloses a toy intelligent control method and system based on multi-round dialogues, and relates to the field of intelligent control. A toy intelligent control system based on multi-round dialogues comprises a dialogue management module, an information estimation module, a dialogue analysis module, an intelligent control module and a feedback adjustment module. According to the method, the intention label and the dialogue action label of the user are obtained through the dialogue analysis model, the dialogue analysis model combines the time decay function with the multi-head self-attention network, the global context is considered, meanwhile, the recent context is focused on, the model can better utilize the historical context, and the user experience is improved. And context information with different attention degrees is screened out for different characters by using character-level attention, so that the recognition capability of the model is improved, and effective semantic recognition on multiple rounds of dialogues is realized.

The invention provides a collection strategy generation and execution system and method based on debtor portrait driving, and belongs to the technical field of data processing, and the system specifically comprises a portrait access module which is used for obtaining a user portrait of a debtor, a strategy generation engine which is used for automatically generating a collection strategy of the debtor based on the user portrait, and an execution module which is used for executing the collection strategy. The execution control module adopts an event-driven architecture to be responsible for automatic execution and real-time control of a collection strategy, the communication management module adopts a natural language processing and dialogue management technology to be responsible for intelligent communication and interaction management in the collection process, and the strategy optimization module is responsible for intelligent management. And online learning and reinforcement learning technologies are adopted to continuously optimize the collection strategy based on the execution result and effect feedback, so that the individuation and pertinence of collection processing are improved.

An intelligent, predictive customer loyalty system for social media marketing, consisting of: a data acquisition unit configured to collect user interaction data, demographic attributes, and sentiment analysis metrics from multiple social media platforms via platform-specific application programming interfaces (APIs), web scraping techniques, and direct data ingestion pipelines; a real-time engagement prediction engine comprising a machine learning model trained on historical engagement patterns, sentiment fluctuations, and content performance metrics, with the model dynamically predicting user responses, determining the optimal engagement time, and prioritizing interaction opportunities based on a probabilistic ranking technique; a real-time engagement prediction processing unit configured to generate and optimize social media posts, ads, and interactive media using a neural network-based natural language generation (NLG) model, where the model processes contextual user behavior and trending topics to dynamically adapt marketing content; an AI-powered automation unit for interactive engagement, comprising a deep learning-based chatbot engine integrated into a dialogue management system, with the chatbot engine analyzing the user's intentions, sentiment development, and engagement history to enable predictive, context-aware conversations with users; a cloud-connected processing unit that includes an AI inference accelerator configured to perform deep learning calculations for real-time engagement prediction units, personalized content recommendations, and chatbot response generation, with the processing unit integrated into a distributed computing framework to optimize inference latency.

The invention discloses an accompanying dialogue system and method based on real-time environment perception and knowledge graph enhancement. The system comprises a multi-mode real-time environment perception module, a knowledge graph construction and enhancement module, a natural language understanding and dialogue management module, a personalized recommendation and narration generation module and an immersive multi-mode interaction module. The method comprises the steps of multi-modal real-time environment perception and situation data generation; performing dynamic association, query and enhanced reasoning on the knowledge graph; natural language understanding and dialogue management, personalized recommendation and narrative generation, and immersive multi-modal interaction presentation and feedback reception. According to the method and the system, the concern point of the user, namely scenery or details, can be accurately positioned, and context information required by subsequent service intelligence is provided, so that the problems of poor environment perception ability, weak interaction immersion, dull knowledge service, lack of individuation, insufficient intelligent accompanying experience and the like in the existing tourism auxiliary technology are solved.

The invention provides an enterprise-level intelligent assistant-oriented multi-modal dialogue management and task execution system establishment method, which is characterized by comprising the following steps of Step 1, multi-modal data acquisition and preprocessing; step 2, carrying out cross-modal information alignment and fusion; 3, multi-mode dialogue management is carried out, and after data fusion is completed, the system enters a dialogue management stage; step 4, multi-modal task execution: in the dialogue management process, when a user initiates a specific task request, the system enters a task execution stage; automatically distributing and executing a plurality of tasks in the enterprise by using the fused multi-modal information; step 5, designing and optimizing a system architecture; and an integrated multi-mode intelligent assistant architecture is designed. By integrating multi-modal data processing and natural language understanding (NLU) technologies, the accuracy and efficiency of the enterprise intelligent assistant in task execution are remarkably improved. The system can process various modal information such as texts, voices and images at the same time, and seamless connection of cross-modal dialogue management and task execution is achieved.

The invention discloses an enterprise data analysis system based on natural language interaction, and relates to the field of enterprise data analysis, the system comprises seven core function modules: a dialogue management module supports a user to create a new dialogue and automatically jumps to an initial page; the report interaction module realizes query support and switching functions of a real-time report and a T + 1 report through a floating button; the file import module allows uploading of Word, PDF and JPG format files to supplement question and answer contexts; the model configuration module supports dynamic switching of different AI service interfaces; the interaction auxiliary module provides a quick question button and a dialogue interruption function; the report preview module integrates Excel and PDF plug-ins to realize detail data preview; the session sharing module generates a sharing link and sets an access permission rule; dynamic model configuration optimizes response speed and accuracy; the operation process is simplified by a suspension button and a quick questioning function; the session sharing mechanism supports cross-team cooperation; and the real-time collaborative design with the T + 1 report meets the multi-dimensional data analysis requirement.

The invention discloses a multi-round interactive AIAgent agent based on multi-module collaboration and an implementation method thereof, and relates to the field of artificial intelligence. The intelligent agent comprises an information acquisition module, a dialogue state judgment module, an intention recognition module, a parameter extraction module, a rule engine module and a dialogue strategy module which work cooperatively. The information acquisition module is used for maintaining tool structured information and a historical dialogue library; the conversation state judgment module distinguishes task continuation from new tasks; the intention recognition module recognizes a user intention; the parameter extraction module extracts structured parameters; the rule engine module verifies parameter integrity and legality; the dialogue strategy module guides the user to supplement input. According to the implementation method, multiple rounds of interaction are completed through multiple steps. Modular design is adopted, model illusion is avoided, multi-round dialogue management is achieved, system expansibility is enhanced, and tool calling accuracy and user experience are improved.

The invention discloses an intelligent dialogue management method of an outbound system, and relates to the technical field of intelligent management.In the system operation process, multi-dimensional data of clients are collected in real time, a client voice feature file is established through the voice recognition technology, cleaning, noise reduction, feature extraction and standardization are conducted on the data, and a preprocessed feature matrix is generated; according to an analysis result, a deep learning algorithm is applied, and after calculation, a timbre adaptation coefficient Zys and an intonation matching coefficient Zyt are obtained; a timbre and intonation model highly matched with customer features is dynamically generated; voice is synthesized in real time and dynamically adjusted and output is performed; a current scene is judged and an outbound content and an intonation style are optimized; selecting an intonation style according to a scene demand, collecting real-time feedback in an outbound process, continuously optimizing a tone and intonation matching model through deep learning, calculating to obtain a comprehensive adaptation index Zsa, comparing the comprehensive adaptation index Zsa with a preset adaptation threshold A, and judging whether adaptation succeeds or not.

The invention relates to an intelligent agent method and device based on an intelligent language model, equipment and a medium, and the intelligent agent method based on the intelligent language model comprises the steps: setting corresponding interaction parameters according to the type of a communication protocol, so as to establish a data interaction channel with a target customer service system; on the basis of the intelligent language model, multiple rounds of dialogue management processes are executed on the user input text, corresponding user consultation content is generated, and the user consultation content comprises holiday and festival consultation verbal skills generated for a target area in the user input text; receiving response information returned by the target customer service system based on the user consultation content, and extracting structured field information used for optimizing the multi-round dialogue management process; generating and pushing a corresponding question-seeking verbal skill; according to the invention, the docking efficiency in a multi-platform and multi-protocol environment is effectively improved, the dependence on manual configuration is reduced, and the processing efficiency and accuracy of an intelligent customer service system in a high-concurrency scene are improved.

The invention discloses an artificial intelligence-based intelligent question and answer method in the human society field, and relates to the technical field of artificial intelligence and government affair services. Comprising the following steps: collecting and cleaning laws and regulations, files, common questions and case library contents related to human society through a knowledge graph construction module; constructing a structured human social knowledge graph, and performing intention recognition and slot extraction on the content input by the user by using a pre-training language model through a natural language understanding module; context memory and state tracking are carried out through a dialogue management module, and multi-round dialogue logic is controlled for an interaction mode of inquiry, clarification and guidance; providing a voice and character dual-channel input and output interface; an answer generation module generates a natural language answer based on a human society knowledge graph query result, the natural language answer is used for visualized display of structured data, and an API interface can be called to obtain real-time data; user interaction behaviors and identity information are recorded through a user portrait and personalized recommendation module, personalized policy interpretation and service recommendation are provided in combination with a user portrait, and historical question and answer record retrieval and reuse are provided; and obtaining question and answer effect feedback through a feedback learning and adaptive optimization module, carrying out question and answer strategy optimization by utilizing a reinforcement learning algorithm, automatically discovering uncovered questions and triggering a knowledge base updating process.

The invention relates to the technical field of dialogue management, and discloses an algorithm AI customer service dialogue management method and system based on big data, and the method comprises the steps: obtaining user input content and a verbal skill atlas; performing keyword retrieval according to the user input content and the verbal skill atlas to obtain verbal skill tags and activate corresponding verbal skill content; constructing a verbal skill directed graph according to the verbal skill label and the verbal skill map, and performing greedy optimization to obtain a verbal skill priority list; outputting all the activated verbal skill contents according to the verbal skill priority list, and ending the dialogue after the verbal skill is output; performing emotional state evaluation according to the user input content, and when it is detected that the emotion of the user is negative, outputting a pacifying verbal skill and ending a dialogue; and performing convergence degree calculation according to the user input content, and when the topic convergence degree is greater than a preset convergence degree threshold value, ending the dialogue. The method has the following effect that the customer service dialogue efficiency can be optimized.

The invention relates to the technical field of artificial intelligence, in particular to a large model dialogue management method and device.The method comprises the steps that dialogue information of a user input dialogue initiated by a user to a large model is intercepted; obtaining user information corresponding to a user; filling the first cue word template based on dialogue information of a dialogue input by a user and user information to obtain a first cue word; sending the first cue word as first-round user input dialogue information to the large model, so that the large model generates large model output dialogue information for the first-round user input dialogue based on the first cue word; and in response to establishment of the update trigger condition, updating the user contour information based on the historical round dialogue of the current session and / or the dialogue content of the current round dialogue. According to the method, the structured user background information is provided for the large model at the beginning of the dialogue, so that the reply accuracy and pertinence of the large model are remarkably improved, and cross-session personalized interaction experience is realized.

The invention provides a multi-agent system and a multi-agent dialogue management method. The system comprises a main agent, a dialogue management module and a plurality of sub-agents, and the sub-agents correspond to processing of tasks in different business fields respectively; the main agent is used for calling the dialogue management module under the condition that a user request is received; the dialogue management module is used for acquiring historical information corresponding to the user request, analyzing the historical information and returning an analysis result to the main agent; the main agent is further used for determining corresponding request task data and a target sub-agent in the multiple sub-agents according to the analysis result and the user request, and sending the request task data to the target sub-agent; the sub-agent is used for processing based on the request task data, obtaining a task processing result and returning the task processing result to the main agent; and the main agent is also used for returning a corresponding natural language reply to the user according to the task processing result of the sub-agent.

The invention relates to the technical field of psychological consultation, in particular to an interactive psychological consultation session system, which comprises a dialogue management module used for receiving consultation information of a current user and determining a context process and sequence when the user consults; the language processing module is used for analyzing the consultation information and understanding the emotional tendency expressed by the current user; the consultation knowledge module is used for acquiring historical data consulted by the user and determining an emotion mode and consultation degree information consulted by the user in combination with the emotion tendency of the consultation information; the consultation recommendation module is used for determining user preferences according to an emotion mode and consultation degree information consulted by the user; recommending a consultation scheme according to user preferences; the more personalized and appropriate consultation service is realized, and the psychological consultation efficiency and accuracy of the user are improved.

The invention discloses a multi-module cooperative intelligent role playing system, which belongs to the crossing field of artificial intelligence and human-computer interaction technology, and mainly comprises a dynamic role definition module, a dynamic constraint module, a long-term memory module, an RAG module and a React module. Through combination of long-term memory, emotion recognition, dynamic role definition, dynamic constraint, real-time response and feedback and retrieval enhancement generation (RAG) technologies, role simulation and dialogue generation in a complex interaction scene are realized. The method is especially suitable for situational dialogue management and real-time decision support, and an error prevention mechanism is integrated to improve the robustness and adaptability of the system. According to the method, an innovative solution is provided for scenes such as enterprise-level virtual assistants, game intellectualization and educational training, and the use threshold of non-technical personnel in a complex interaction system is remarkably reduced.