2396 results about "User intent" patented technology

The present invention provides a large language model-based system and method for data processing in application developed by codeless platform. The invention includes identification of intent of a user to process procurement, supply chain, application integration, application restructuring or development scenarios.

The invention relates to the technical field of AIGC content generation, discloses an AIGC content generation method and system based on multi-modal fusion, and aims to solve the problems of decentralization, low efficiency and insufficient originality of a traditional content generation tool. Multi-modal data such as texts, images, videos and audios are integrated, user intentions are analyzed in combination with intelligent retrieval and a domain knowledge base, automatic generation from multi-modal input to high-quality creative content is achieved, a cross-modal collaborative generation technology is adopted, semantic features are dynamically aligned, and logically coherent content is generated. The content emotional value is enhanced through an emotional analysis and dynamic optimization strategy, the homogenization bottleneck is broken through, meanwhile, an automatic quality evaluation and format adaptation mechanism is integrated, deep application of scenes such as text travel, advertisement, e-commerce and interactive network television service is supported, marketing copywriting, short videos and cross-platform distribution schemes can be efficiently generated, and the market competitiveness is improved. And the content production efficiency and the creativity transmission are obviously improved.

The invention discloses a dynamic visual content collaborative generation method based on a cognitive logic chain, and belongs to the technical field of visual content generation, and the method comprises the following steps: S1, user intention analysis and data input; s2, dynamically constructing a cognitive logic chain; s3, intelligent scheduling of the multi-modal generation module; s4, cross-modal content collaborative generation is carried out; s5, collaborative editing and real-time feedback are carried out; s6, iterative optimization of logic chain driving; s7, multi-dimensional quality evaluation: constructing an evaluation matrix containing semantic consistency, visual attraction and user participation degree, predicting a content propagation effect in combination with a deep learning model, and generating a quantitative improvement suggestion report; and S8, updating the self-adaptive knowledge reversely marking the cognitive logic chain according to the finally adopted content version, extracting a new association rule, and injecting the new association rule into the rule base. Through deep semantic analysis and dynamic logic chain construction, the system accurately captures a core creation target of a user and converts the core creation target into an executable visual strategy.

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses a cockpit active recommendation system and method based on a knowledge graph and semantic reasoning, and relates to the technical field of intelligent cockpits. The system receives natural language voice input of a user, executes voice recognition and semantic analysis, extracts user intention, keywords and slot entities, generates structured semantic information, constructs or calls a knowledge graph structure with semantic relation edges in combination with environment context information, and obtains the knowledge graph structure with the semantic relation edges. Semantic path reasoning is carried out based on the path dependence weight and the semantic similarity, a semantic edge label guided graph attention mechanism is introduced to calculate a path consistency score, a candidate recommendation set is generated, the semantic fitting degree and the path score are fused to sort and output recommendation content, and the graph edge weight and the user portrait are updated based on user feedback. According to the method, semantic understanding precision, recommendation path interpretability and system adaptive capacity are improved, and the method is suitable for personalized voice recommendation, man-machine interaction and scene linkage control tasks in an intelligent cockpit.

The invention discloses a dynamic interaction method based on cooperation of a multi-modal dynamic fusion large model and an intelligent agent. The method comprises the following steps: performing feature extraction on user voice information to obtain a voice coding vector, a text semantic vector and an emotion feature vector; performing dynamic weight feature fusion on the voice coding vector, the text semantic vector and the emotion feature vector through a multi-modal dynamic fusion large model to obtain a fusion feature vector; inputting the fusion feature vector into an intention-scene coupling network, and identifying to obtain a user intention label; and identifying according to the user behavior log to obtain a user portrait tag, inputting the user intention tag and the user portrait tag into an autonomous decision-making agent, generating a target decision-making action through a lightweight policy network, and then interacting with the user according to the target decision-making action. The intelligent interaction efficiency and accuracy of the customer service system are improved, the interaction experience of the user is also improved, and the method can be widely applied to the technical field of artificial intelligence.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention provides a smart glasses AI voice interaction method, which comprises the following steps: simultaneously acquiring a user gesture image and a voice signal, acquiring a user historical interaction record, extracting key point coordinates and pointing direction information of the gesture image, acquiring hand distance information, generating a gesture motion path, simultaneously extracting frequency spectrum information and an intonation peak value of the voice signal, and generating a gesture motion path; forming a voice beat sequence; extracting a spatial semantic mode of the gesture and voice fusion data, and recognizing a core object of a user pointing instruction according to pointing coordinates of a gesture motion path and an intonation peak value of a voice beat sequence; after the core object pointing to the instruction is recognized, the user intention is determined in combination with the spatial semantic mode and the historical interaction record of the user; and the complete intention analysis result is output to the intelligent glasses display module to execute corresponding operation, and is fed back to the acquisition module to adjust the next capture parameters including the acquisition frequency and the recognition sensitivity, so that the response speed and the accuracy are improved.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention provides a virtual human design and application platform, method and device based on artificial intelligence, and relates to the technical field of virtual digital humans. The method comprises the steps of performing local anonymization on multi-modal input data on user equipment, encoding generated anonymized multi-modal features to obtain a multi-modal feature vector, and inputting the multi-modal feature vector into an emotion calculation model to obtain a user emotion intensity quantized value; inputting the multi-modal feature vector into a context sensing model, and generating a user intention vector after context correction in combination with a knowledge graph; generating an updated personality parameter matrix according to the user emotion intensity quantized value and the user intention vector; and outputting voice waveform data, facial muscle motion parameters and skeleton joint coordinate data based on the personality parameter matrix, and driving the virtual digital human three-dimensional model to perform real-time rendering. According to the scheme, the naturalness, emotional resonance and long-term user retention rate of virtual digital human interaction can be improved, and user privacy data security is protected.

The invention discloses a dialogue type data analysis method and device, a storage medium and electronic equipment, and relates to the technical field of data analysis. The method comprises the steps that a natural language text input by a user is acquired; obtaining corresponding semantic pre-analysis information based on the natural language text; matching a corresponding mode mapper based on the semantic pre-analysis information; obtaining corresponding semantic information based on a mode mapper; converting the semantic information into corresponding semantic information conversion parameters; analyzing the semantic information conversion parameter to obtain an index value corresponding to the to-be-queried index; performing anomaly detection on the index value corresponding to the to-be-queried index; and performing analysis result display on the index value corresponding to the to-be-queried index passing the anomaly detection. According to the method, information arrangement and information analysis are carried out on the natural language text input by the user, the intention of the user is known, the index needing to be queried by the user is fed back, the operation complexity is reduced, the interactivity and the real-time performance are improved, and convenience is provided for data analysis work.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a page generation method and device based on a natural language, equipment and a medium, and the method comprises the steps: obtaining a demand description in a natural language form, analyzing the demand description based on a preset intention recognition material, and generating a user intention; retrieving in the page feature vector library according to user intention to obtain a page template, performing graphical user interface rendering on the page template to generate a visual component set, injecting associated data into a target component in the visual component set to generate a target page, publishing the target page or publishing and collecting use effect data after modification, and displaying the use effect data. And updating the page feature vector library by using the use effect data. By combining natural language analysis, intention recognition, vector retrieval, graphical rendering and effect data updating, automation from demand input to page generation is realized, manual intervention and development cycle are greatly reduced, and page generation efficiency and quality are improved.

The invention discloses an agent question-answering method and device fusing different large models, a medium and a product, and relates to the field of large models, agents and artificial intelligence, and the method comprises the steps: carrying out the task planning of an obtained question through a first large model associated with the agents, obtaining a target processing task for answering the question, calling a second large model to execute the target processing task, obtaining external knowledge required for answering the question; an answer to the question is generated based on the question and the external knowledge. Wherein the reasoning ability of the first large model is higher than that of the second large model, and by combining the first large model and the second large model, the intention of the user can be more accurately understood, and the waiting time of the user can be shortened, so that the requirements of the user can be better met, and the question and answer experience of the user is improved.

The invention relates to the technical field of user interaction, in particular to a live broadcast bullet screen real-time feedback method and system based on interaction semantic matching, and the method comprises the steps: receiving and caching user bullet screens in real time, carrying out the density analysis and priority evaluation of bullet screen flows through the combination of filtering and resource optimization rules, dynamically adjusting the response frequency, and screening key bullet screens; a distillation processing mechanism intention unit is introduced, a user intention label in a key bullet screen is extracted, a vector is generated, historical behavior data of a sending user is obtained at the same time, the intention label, the vector and current real-time scene data of a live broadcast room are fused, and a bullet screen vector is constructed; performing similarity calculation in combination with the live broadcast content and user historical behaviors to obtain first feedback content; based on a reinforcement learning sentiment analysis strategy, combining user preference memory to generate second feedback content; and embedding the second feedback content into the current live broadcast interface through the client. According to the invention, real-time forward feedback and intelligent guidance of the live broadcast bullet screen are realized through interactive semantic matching.

The invention discloses a course data intelligent multi-dimensional retrieval system based on deep learning, and relates to the technical field of course retrieval. The course data retrieval platform is in communication connection with a knowledge graph construction module, a user intention analysis module, a user portrait construction module, a personalized recommendation engine module and a retrieval display module, and all the modules are in electric signal connection; the knowledge graph construction module is used for collecting course data from multiple sources and constructing a course domain knowledge graph. According to the natural language processing technology based on deep learning, deep semantics queried by a user can be deeply analyzed, fuzzy or implicit requirements can be understood, limitation of traditional keyword matching is broken through, intent features and entity relationships can be extracted by combining a knowledge graph, specific entities and relationships queried by the user can be accurately mapped, and user experience is improved. Therefore, a more accurate retrieval result is provided, and personalized requirements of users are met.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The invention provides a cross-platform user behavior data intelligent aggregation and analysis processing method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-platform user historical behavior data, carrying out the time sequence sorting, carrying out the session segmentation based on the time sequence data, obtaining a behavior session sequence, and constructing a behavior migration probability graph; an intention recognition agent and a behavior prediction agent are deployed through a decentralized federal reinforcement learning framework, cross-platform cooperative training is realized by using a differential privacy mechanism, and anti-factual reasoning is performed based on a multi-layer causal relationship graph to generate a user intention portrait, so that the cross-platform data analysis accuracy is improved, and the privacy protection capability is enhanced.

The invention provides an intelligent interactive question-answering method and system based on deep multi-modal learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal interaction information of a user, extracting a feature vector, constructing a sequential memory enhanced multi-modal feature graph through a recurrent neural memory unit and a graph neural network, obtaining a fusion feature vector, and carrying out the fusion of the fusion feature vector; and inputting the data into a symbolic neural inference framework, performing user intention recognition in combination with domain rule symbolic embedding and knowledge distillation, retrieving answer information from a preset knowledge base, and generating a multi-modal response.

The invention discloses a voice interaction system and method based on an artificial intelligence technology, and relates to the technical field of voice interaction, and the method comprises the steps: employing the double-layer cooperative recognition of a main channel Transform model and an auxiliary channel CTC-Attention mixed architecture, obtaining a text with a multi-dimensional feature label, carrying out the scene classification and dependency syntactic analysis through a bidirectional recurrent neural network, and obtaining a multi-dimensional feature label text; retrieving associated entities in real time and generating structured intention data in combination with a dynamic knowledge graph, executing three-dimensional analysis through an interpretability engine and generating a multi-dimensional traceability report, and triggering an intelligent decision fusion engine through a confidence threshold to generate structured enhanced interaction data; according to the method, scene classification and dependency syntactic analysis are carried out through the bidirectional recurrent neural network, the associated entities are retrieved in real time in combination with the dynamic knowledge graph, and the structured intention data are generated, so that the intention of the user is accurately understood, and personalized interaction services are provided.

In an approach to coordinating a conversational agent with a large language model for conversation repair, one or more computer processors receive a failure indicator from a first conversational agent. One or more computer processors retrieve a descriptive prompt associated with the first conversational agent. One or more computer processors transmit the descriptive prompt to a large language model. One or more computer processors transfer control of the failed conversation from the first conversational agent to the large language model. One or more computer processors determine the intent of the user associated with the failed conversation using the large language model. One or more computer processors determine whether the intent of the user associated with the failed conversation matches a capability of the first conversational agent. One or more computer processors transfer by one or more computer processors, the user back to the first conversational agent.

The invention relates to a biological information MCP service calling method, system and device and a medium, and relates to the technical field of bioinformatics and artificial intelligence. The method comprises the steps of obtaining user request information; querying MCP service information matched with the user request information based on the user request information; building enhanced prompt information corresponding to the MCP service information which contains the user request information and is matched with the corresponding user request information, and sending the enhanced prompt information to the large language model to generate calling parameters; and calling the target biological information MCP service based on the calling parameter. The automatic discovery of the MCP service is realized, and the expandability and the flexibility of the system are improved; the user intention can be more accurately understood, and the tool discovery accuracy is improved; an MCP specification is used for assisting LLM to accurately generate calling parameters, and the use threshold of a user is lowered; interoperability and expandability are ensured based on an MCP protocol; and a large-scale biological information MCP service scene is effectively supported.

The invention provides an intelligent interactive question and answer method and system based on multi-modal large model intention recognition, and relates to the technical field of intelligent question and answer, and the method comprises the steps of feature extraction, cross-modal semantic mapping, intention matching, key information extraction and response generation, achieves the unified processing of multi-modal information, can accurately recognize the intention of a user, and improves the user experience. And the association network is dynamically constructed to extract key information, so that the accuracy and semantic coherence of intelligent questions and answers are improved, and the man-machine interaction experience is enhanced.

The invention relates to the technical field of resource matching, and discloses a cross-platform e-commerce resource dynamic matching search method, which comprises the following steps: carrying out real-time analysis on commodity description information from different e-commerce platforms, extracting a core attribute of a commodity and generating a unified semantic representation; when cross-platform data conflicts occur, a conflict resolution strategy is dynamically generated, and the conflict resolution strategy is executed by a local preprocessing module; acquiring data content after conflict resolution, selecting a target edge node based on the data content in combination with user geographical location information, and dynamically configuring a cache data set of the target edge node; and generating corresponding multi-dimensional matching features based on user intention information in a natural query language in combination with the commodity data in the configured cache, determining a candidate commodity set and a corresponding search result based on the multi-dimensional matching features, obtaining feedback information of the user on the search result, and updating the intention analysis model and the matching strategy based on the feedback information. According to the invention, efficient cross-platform resource matching can be realized.

The invention discloses a video generation system based on a video large model, and belongs to the technical field of video generation. A trajectory control unit; a physical rule constraint unit; the scene generation unit is used for constructing a high-fidelity dynamic environment and enhancing the scene immersion of the video; a timing consistency unit; a multi-modal fusion unit; the user interaction unit is used for realizing man-machine collaborative creation and real-time control and improving the generation efficiency; a quality evaluation unit; the post-processing optimization unit is used for improving the terminal applicability of the output video and adapting to multi-platform requirements; and a distributed deployment unit. Millimeter-level regulation and control of the object motion trail are realized through a multi-dimensional accurate control mechanism, and physical rule constraints are combined to ensure that the generated video conforms to the reality mechanics law, so that the content authenticity is remarkably improved; the multi-modal fusion technology integrates text, voice and image input, accurately restores the intention of the user and realizes lip shape synchronization and emotion migration.

The invention provides an AR intelligent shopping guide interaction method and system applied to store management, and the method comprises the steps: firstly obtaining an interaction data set of a user in a store, covering a user position movement track, a commodity attention action sequence and voice consultation content, carrying out the user intention analysis of the interaction data set, and carrying out the user intention analysis of the interaction data set; the method comprises the steps of obtaining commodity demand characteristics and interaction preference characteristics, generating AR shopping guide interaction content, including commodity three-dimensional display information and an interaction guide instruction, adaptive to a user based on the commodity demand characteristics and the interaction preference characteristics, aligning and fusing the AR shopping guide interaction content with a store physical scene space, generating an AR shopping guide interaction scene capable of being displayed in real time, and displaying the AR shopping guide interaction scene in real time. And finally, responding to user interaction operation in the AR shopping guide interaction scene, generating feedback information and updating the interaction data set to trigger interactive process iterative optimization, thereby providing personalized and immersive shopping guide experience, and improving store operation efficiency and customer satisfaction.

The invention discloses a scheduling system of a content distribution network based on edge computing, and relates to the technical field of computer networks. An edge resource sensing module; an NLP semantic analysis module; reinforcing a learning scheduling engine; a semantic and resource linkage module; and a federation collaboration module. According to the scheduling system of the content distribution network based on edge computing, semantic understanding is carried out on a request of a user to analyze a deep intention of the user, edge node multi-dimensional state sensing data and a semantic label vector are integrated, a scheduling action is generated based on a node state and the user intention, and the scheduling effect is improved. Therefore, each edge node is fully utilized while user requirements are met, the edge nodes have caching, computing and scheduling capabilities, the limitation of surface feature matching of a traditional CDN is broken through, technical upgrading from traditional file type recognition to intention understanding and from a fixed rule to dynamic intelligence is realized, and finally, the resource efficiency and service quality of the CDN are improved.

The invention relates to the technical field of artificial intelligence natural language processing, and provides an intelligent multi-round question-answering system based on a large model, comprising a semantic understanding module for generating a semantic understanding state value by extracting a semantic feature vector input by a user and analyzing context association; the context association module is used for extracting semantic features and time sequences of dialogue history based on the state value and generating a context association parameter set; the intention recognition module is used for analyzing the relationship between the user intention and the context according to the context parameter set and outputting an intention recognition parameter set; the answer generation module is used for generating multiple rounds of answer generation values in combination with the knowledge base and the real-time semantic data; the semantic prediction module is used for predicting a semantic trend through a large model, analyzing context change and outputting a semantic prediction value; and the dialogue feedback module is used for performing error analysis according to the semantic predicted value, optimizing answer generation and finally outputting a multi-round dialogue automatic optimization scheme. According to the invention, the accuracy, coherence and adaptive ability of multiple rounds of dialogues can be improved.

The invention discloses a data query caching method, device and equipment and a storage medium, and is applied to the field of big data, and the method comprises the following steps: obtaining a query request, and analyzing the query request to obtain a query condition; judging whether a matched query record matched with the query condition exists in the cache library, and if yes, directly returning the matched query record as a query result; otherwise, executing the query task based on the query condition to obtain a query result, and updating the cache library according to the query condition and the query result. By analyzing the query condition, the intention of the user can be more accurately understood, and a foundation is laid for cache matching and query execution. When the cache library is hit, the result can be quickly returned without repeated calculation, so that the waiting time of the user is shortened, the query efficiency is improved, and the calculation resource consumption is reduced. When the query result is not hit, the query task is executed to obtain the query result, and the new result is stored in the cache library, so that cache support is provided for subsequent same query, effective cache data is accumulated step by step, and the query efficiency is continuously optimized.