338 results about "Natural language generation" patented technology

Techniques for using a model to generate a response to a user input, where the response is associated with a personality determined to be relevant to the user input, are described. The system receives a user input and context data associated with the user input. Using the user input data and / or the context data, the system determines a personality (e.g., including a personality type and / or personality characteristics) relevant to the user input. The system generates a prompt instructing a model to generate a response to the user input that corresponds to the personality. The model processes the prompt to generate a response to the user input that corresponds to the personality. In some embodiments, the model generates a request for another component of the system to generate information responsive to the user input. The model may transform the responsive information into the personality-associated response.

The invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

The invention discloses a cloud platform auditing method based on multi-modal data processing, and relates to the technical field of cloud platform auditing. Independent features of text, image, audio, video and log data are extracted, each piece of modal information can be subjected to refined analysis in a corresponding feature space, and in a cross-modal feature fusion stage, a multi-modal feature fusion model is obtained. The correlation between different modal data is calculated by using a cross-modal consistency comparison network CMCN, and feature alignment is optimized through an attention mechanism, so that multi-modal information can be mutually verified, and misjudgment and missed judgment caused by independent auditing are reduced; in the auditing decision-making link, an auditing model based on deep learning is constructed, and end-to-end illegal content judgment is realized in combination with anomaly detection and a self-adaptive risk control threshold value, so that the intelligent level of an auditing system is improved; in addition, the suspicious areas of the image and the video are visually marked, and an auditing conclusion is generated by utilizing a natural language to generate an NLG model, so that the auditing result is more transparent.

The invention relates to the technical field of operation and maintenance management, in particular to an asset operation and maintenance decision management platform and management method based on data fusion, and the method comprises the steps: collecting asset full life cycle data, and constructing a standardized asset data set; utilizing ontology modeling and knowledge extraction technologies to construct a dynamic asset knowledge graph containing asset attributes, topological relations and operation states; training a deep learning model to realize asset fault diagnosis, constructing an asset operation and maintenance risk detection algorithm, performing real-time analysis on asset multi-dimensional data, identifying an asset security risk level, and triggering a corresponding operation and maintenance strategy; and converting the diagnosed asset fault and the generated operation and maintenance strategy into a natural language by using a natural language generation technology, and pushing the natural language to an operation and maintenance manager for execution. According to the method, multi-source heterogeneous data is fused, and intelligent operation and maintenance decision-making of the full life cycle of assets is realized.

The invention relates to the field of standard comparison analysis, in particular to a method for automatically identifying differences of domestic and overseas standard files. A method for automatically identifying differences of standard files at home and abroad comprises the following four steps of S1, standard file analysis and information extraction, S2, semantic understanding and information integration, S3, standard difference comparison, S4, compliance analysis and suggestion, and in combination with a rule engine and a natural language generation technology, evaluating difference influences and outputting adjustment suggestions. According to the method, comprehensive analysis of the standard file is realized through multi-modal integration and structured processing, and a knowledge base with cross-language and format is constructed; clause association reasoning is realized through deep semantic analysis and knowledge graph construction; according to the method, accurate difference recognition is achieved through hybrid embedding and a dynamic threshold strategy, the accuracy and efficiency of standard comparison are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for standard system analysis in international trade and technical cooperation.

An agentic artificial intelligence system processes insurance claims, medical claims, financial transactions, and sales leads by receiving and preprocessing claimant, patient, transaction, and prospect data to standardize formats, remove sensitive identifiers, and enrich records. It uses machine learning, deep learning, natural language processing, and computer vision to analyze both structured and unstructured data, identify errors, inconsistencies, or fraudulent patterns, verify eligibility and compliance, and assign relevant codes based on historical and contextual information. The system calculates expected payouts or reimbursements, assesses transaction feasibility, and generates risk scores while adapting its predictions to market conditions, contractual factors, or clinical guidelines. A multi-agent framework coordinates specialized agents for eligibility verification, coding, pricing, fraud detection, and sales outreach, supporting multi-channel communication, lead prioritization, and natural language generation of outreach messages and decision-making explanations. Continuous learning is achieved via retraining, feedback loops, federated learning, and blockchain-based recordkeeping, ensuring secure, transparent, and compliant operations across multiple domains.

The invention relates to the technical field of financial analysis, and discloses a multi-agent financial analysis system and method based on a large language model, and each agent of a multi-role agent module realizes specialized analysis through a field customization LLM; the hybrid communication protocol module integrates a structured report and a natural language dialogue, and the structured report adopts a JSON or XML format; the dynamic coordinator module is configured to dynamically allocate an agent weight according to the difference between the market stage and the confidence coefficient; the interpretability enhancing module is used for outputting an inference logic chain through a natural language generation technology and visually presenting the inference logic chain; and the data fusion and strategy generation module is used for integrating basic surface, emotion, technology and macroeconomic data and generating a dynamic investment strategy. According to the method, by simulating the organization structure and the cooperation process of a real financial analysis team, the multi-source data are integrated, and the interpretable comprehensive investment strategy is generated, so that the efficiency, the depth and the credibility of financial analysis are remarkably improved.

The invention specifically relates to the technical field of natural language processing, and discloses an intelligent interactive customer service system based on an AI large model, which comprises a multi-modal input analysis module, an intention preliminary detection module, a large model semantic understanding module, a dynamic knowledge base association module, a dialogue strategy generation module, a natural language generation module and a service feedback work order module, through deep cooperation of a multi-modal input analysis module and a large model semantic understanding module, entity and attribute information is accurately extracted, context semantic association information is mined, and through the cooperative effect of an intention preliminary detection module, a dynamic knowledge base association module and a dialogue strategy generation module, based on a semantic analysis result output by a large model, a dialogue strategy is generated. According to the method and the system, matching calculation is carried out with the constructed dynamic knowledge base, rich knowledge support is provided for solving complex problems, personalized knowledge content is recommended for different users through the natural language generation module and the service feedback work order module, and the reply matching degree and the complex problem processing capacity are greatly improved.

The invention discloses a consultation method and system based on natural language processing and a legal knowledge graph, and relates to the field of data processing, and the method comprises the steps: receiving a multi-format legal consultation demand of a user, converting the multi-format legal consultation demand into a text, inputting the text into a BERT law NLP model, and analyzing key information through word segmentation, intention recognition and entity extraction; based on a pre-constructed multi-level legal knowledge graph, carrying out accurate and fuzzy retrieval and domain filtering in combination with an analysis result, and obtaining an associated law article, a case and a legal relationship; screening conflict law articles and similar cases, and inputting the conflict law articles and the similar cases into a graph neural network reasoning model to generate a preliminary conclusion; the conclusion is converted into a spoken consultation report through a natural language generation module, and output is customized according to a user scene; and if the user feedback satisfaction degree is less than the threshold value, iteratively optimizing the storage data to the historical library. The method has the advantages that accurate retrieval is realized based on the BERT model and the multi-level knowledge graph in the legal field, the oral personalized conclusion combined with the user scene is generated through GNN reasoning, and iterative optimization is performed through user feedback.

The invention discloses an electrical work ticket intelligent generation method and device, a medium and equipment in the technical field of power systems, and aims to solve the problems of serious module decoupling, dependence on manual connection and insufficient intelligent generation capability in the prior art. The method comprises the following steps: generating a required digital knowledge base and an electrical work task description generation work ticket based on a pre-constructed work ticket by utilizing a multi-model collaborative generation architecture; the multi-model collaborative generation architecture comprises a rule driving module used for calling a corresponding template based on task element matching; the task execution unit is used for executing a specific subtask according to a pre-deployed lightweight model or rule engine; and the large language model is used for performing semantic understanding and reasoning according to the electrical work task description, calling the rule driving module and the task execution unit according to a semantic understanding and reasoning result, and generating a work ticket in combination with a digital knowledge base required by work ticket generation. The method has both the accuracy of structured knowledge recognition and the flexibility of natural language generation.

The invention provides an industrial PLC code generation system and method based on artificial intelligence, and the system comprises an input module, a model optimization module, a code generation module and a deployment module, the input module is used for receiving and analyzing a natural language, a flow chart and an I / O configuration table, and generating a structured logic constraint; the model optimization module is used for supporting low-precision reasoning and dynamic expert network load balancing by adopting an artificial intelligence model of industrial control field data fine tuning; the code generation module is used for outputting PLC codes meeting industrial standards and supporting simulation verification and error correction; and the deployment module is used for downloading the generated code to target PLC equipment or integrating the generated code to engineering software. According to the industrial PLC code generation system and method based on the artificial intelligence, a tool for generating the PLC code based on the natural language of the artificial intelligence is provided, the code development efficiency is improved, and the error rate is reduced.

Techniques for hybrid natural language generation (NLG) using a symbolic NLG engine and a large language model (LLM) are disclosed. The techniques include: obtaining a first text segment configuration; obtaining values of at least some of a first set of data variables; generating a first natural language text segment for an electronic document using a first text segment configuration, the values, and the symbolic NLG engine; obtaining an initial text segment; obtaining a second text segment configuration specifying information to use for generating a second natural language text segment using the ML NLG engine from the initial text segment; generating a second natural language text segment for the electronic document by using the initial text segment, the second text segment configuration, and the ML NLG engine; and outputting the generated electronic document.

The invention discloses a virtual power plant user incentive generation method and system based on a deep user portrait, and relates to the technical field of power demand side management. The method comprises the steps of collecting multi-source heterogeneous data and performing fusion preprocessing to form a multi-source user data set; generating a deep user portrait according to the multi-source user data set, and generating a personalized incentive strategy by using a reinforcement learning algorithm in combination with a power grid operation state and a historical response record; determining an optimal push time period through a user behavior activity prediction model, generating an incentive push message by using a natural language generation method, and actively pushing the incentive push message to a user terminal; and updating the user portrait and the incentive strategy according to the user response result and the feedback information to form closed-loop optimization. According to the method, accurate description and differential excitation of user side response behaviors are realized, privacy compliance and cost effectiveness are considered, and the adjustable capacity and scheduling reliability of the virtual power plant are enhanced while the participation rate and the response quality are improved.

Techniques for determining when speech is directed at another individual of a dialog, and storing a representation of such user-directed speech for use as context when processing subsequently-received system-directed speech are described. A system receives audio data and / or video data and determines therefrom that speech in the audio data is user-directed. Based on this, the system determine whether the speech is able to be used to perform an action by the system. If the speech is able to be used to perform an action, the system stores a natural language representation of the speech. Thereafter, when the system receives system-directed speech, the system generates a rewrite of a natural language representation of the system-directed speech based on the previously-received user-directed speech. The system then determines output data responsive to the system-directed speech using the rewritten natural language representation.

A method includes preparing a base model using an input model pretrained on at least three languages different from each other and a base vocabulary including words corresponding to two languages among the at least three languages, where the preparing the base model includes constraining the input model to the words included in the base vocabulary; training the base model using a first enhanced training dataset generated from public data, to generate a text summarization model; training the base model using a second enhanced training dataset generated from the first enhanced training dataset, to generate a text generation model; and training the base model using a third enhanced training dataset that is generated using the second enhanced training dataset and the text summarization model, to generate a next sentence generation model.

The invention belongs to the technical field of artificial intelligence and educational informatization, and relates to a multi-mode knowledge graph enhanced intelligent teaching-assistant question-answering system. According to the method, the knowledge graph construction technology, the multi-modal content analysis technology and the large language model reasoning enhancement technology are comprehensively applied, and the semantic understanding, knowledge integration and reasoning generation capabilities of the intelligent teaching assisting system in an education and teaching scene are improved. The related technology comprises layout analysis of textbook documents, semantic description generation of image content, entity and relation extraction of text content, multi-modal knowledge graph construction and question and answer reasoning and natural language generation combined with the knowledge graph. Through cooperative application of the technologies, semantic interconnection can be carried out on various modal information such as texts, images and tables in the textbook, and a searchable and traceable textbook-level knowledge network is formed.

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.

The invention provides a mathematical question and answer process teaching system, which comprises a user interaction module, a question analysis and standardization module, a cognitive diagnosis engine, a generative teaching module, a mathematical knowledge graph database and a user portrait database, teaching content and guiding questions are presented to the user in a multi-mode form; the question analysis and standardization module is used for performing semantic understanding and structured analysis on mathematical questions and generating standardized question description and at least one standard question solving path; according to the method, the structured reasoning of the knowledge graph is combined with the natural language generation ability of the generative AI, so that a closed loop from'problem solving 'to'diagnosis' to'heuristic teaching 'is realized, thinking blockage points of students can be accurately positioned and personalized tutoring can be provided like human teachers, and the learning efficiency and the autonomous thinking ability are remarkably improved.

The invention discloses a sign language translation model, a sign language translation system and a sign language translation method based on full-modal alignment. The sign language translation method comprises the steps of extracting multi-modal features of hand, face and body postures from an input video and performing preliminary fusion; deep secondary fusion and alignment are carried out through multi-scale time sequence coding and a cross-modal collaborative attention mechanism, and a spatial-temporal feature sequence of full-modal alignment is generated; performing boundary detection and dynamic segmentation on the feature sequence by using a CTC-based sequence prediction model, and outputting a discrete sign language word sequence with a timestamp; and finally, capturing a sign language grammar structure of the sequence through a graph structure enhanced Transform encoder, inputting the sign language grammar structure into a Transform decoder integrating grammar consistency loss, and generating a target text conforming to target natural language grammar and semantic rules. According to the method, the problems of continuous sign language action adhesion and grammar structure difference are effectively solved, and the sign language translation accuracy and the natural language generation fluency are greatly improved.

The invention discloses a code generation method and device based on a natural language and a medium. The method comprises the following steps: obtaining candidate functions provided by a user based on a target system, and determining function demand information described by a natural language; determining target information from the feature information according to the function demand information; wherein the feature information is determined in advance according to candidate functions; and generating a cue word according to the function demand information and the target information, inputting the cue word into the large language model to generate a code module for executing the function demand information, and integrating the code module into the target system. According to the method, the feature metadata corresponding to the candidate functions of the target system is combined with the natural language information of the user to guide and generate the code module for executing the user-defined function requirements, so that the user is allowed to generate the code module which can be integrated into the target system through the natural language on the scene site; and the processing efficiency of the user function requirements is improved.

The invention provides a method and device for generating an SQL instruction through a dialogue type natural language, and relates to the technical field of language processing.The method comprises the steps that a natural language query request input by a user is received; calling a neural symbol reasoning module to extract a query intention and a key entity list, generating a plurality of candidate SQL instructions through symbol reasoning, and evaluating confidence; if the highest confidence coefficient is lower than a set threshold value, multiple rounds of dialogue clarification are initiated and fed back to the neural symbol reasoning module; calling an adaptive SQL reconstruction engine to perform screening, reconstruction or optimization based on a dynamic data environment on the target candidate SQL instruction; and executing the final SQL instruction, obtaining a query result and returning the query result to the user. By means of the method and device, the technical problem that in the prior art, due to fuzziness of natural language query and dynamic complexity of a data environment, SQL instruction generation accuracy is low, and then query execution efficiency is affected is solved, and the query execution efficiency is improved while SQL semantic accuracy is guaranteed.

The invention relates to the technical field of intelligent customer service systems, and particularly discloses a multi-agent user portrait-driven dynamic role customization intelligent customer service system, which constructs a user portrait through a graph neural network to deeply reinforce learning to dynamically optimize customer service role selection and to collaboratively optimize a service strategy by multiple agents. And real-time interaction is realized by using a natural language generation technology. According to the method, the problem that the existing intelligent customer service system is insufficient in role setting staticization and individuation degree is effectively solved, the customer service interaction accuracy and individuation level are remarkably improved, and the method is widely applied to various intelligent customer service scenes.

The invention discloses a marketing copywriting generation method and device and a storage medium, and relates to the technical field of natural language generation, and the method comprises the steps: obtaining a to-be-optimized original marketing copywriting, a structured tag and environment variable information; inputting the original marketing copywriting, the structured tag and the environment variable information into a causal reasoning generation model; in response to a virtual intervention instruction of a user on at least one target intervention label in the structured labels, executing anti-factual reasoning through a causal reasoning generation model, generating an optimized marketing copywriting corresponding to the virtual intervention instruction, and calculating a corresponding predicted interaction amount; and outputting the optimized marketing copywriting and the predicted interaction amount. According to the method, the anti-factual causal reasoning and the large language model generation capability are fused, the user is supported to preview and optimize the copywriting and the interaction effect in real time through the virtual intervention label, multi-strategy low-cost and efficient simulation evaluation is realized on the premise that real delivery is not needed, and the intelligent level, the decision-making efficiency and the result interpretability of copywriting optimization are remarkably improved.

Techniques are provided for producing an answer to a question regarding a domain. A natural-language textual sequence representing the question is received. From a knowledge graph associated with the domain, first and second textual passages are received using rankings corresponding to the natural-language textual sequence, a first textual summary is received summarizing textual information in a first vicinity of the first textual passage, and a second textual summary summarizing textual information in a vicinity of the second textual passage is received. An answer to the question is obtained using a language model by encoding a first intermediate output based on the natural-language textual sequence, the first textual passage, and the first textual summary, encoding a second intermediate output based on the natural language textual sequence, the second textual passage, and the second textual summary, and decoding a concatenation of the first and second intermediate outputs. An output is provided.

The invention relates to the technical field of fire coal purchase decision support, and particularly discloses a fire coal purchase decision method, system and equipment based on a big language model.The fire coal purchase decision method based on the big language model.The fire coal purchase decision method based on the big language model comprises the steps that target data are acquired; inputting the target data into the large language model, and outputting a fire coal purchase basic data analysis result; according to a fire coal purchasing basic data analysis result, further analysis is carried out through a large language model, and a fuel purchasing strategy direction suggestion is generated; according to a strategy direction selected by a user, constructing a multi-target optimization model, and solving the multi-target optimization model to obtain an optimal combination of purchased coal types, quantity and purchasing routes; according to the optimal combination of the types, the quantity and the routes of the purchased coal, a customized fire coal purchasing scheme is generated through the natural language generation capability of the large language model, the fire coal purchasing decision is intelligent, scientific and efficient, and powerful support is provided for reducing cost, improving efficiency and guaranteeing safety for thermal power enterprises.

The invention relates to the technical field of enterprise ESG digital management, in particular to a sustainable management ESG platform. The platform comprises a data access layer, a twin modeling layer, an intelligent analysis layer, a report generation layer and a collaborative interaction layer. The data access layer is used for standardized acquisition, cleaning and block chain evidence storage of multi-source ESG data; the twinborn modeling layer constructs enterprise ESG digital twinborn bodies through index mapping and scene simulation; the intelligent analysis layer realizes index dynamic monitoring, abnormity early warning and risk quantitative evaluation based on time sequence prediction and causal reasoning; the report generation layer outputs a compliance report through a multi-template automatic adaptation and natural language generation technology; and the collaborative interaction layer realizes multi-role data sharing, task tracking and strategy optimization. According to the method, multi-modal data fusion and an intelligent algorithm are fused, the efficiency of ESG data integration, risk response, report generation and collaborative management is comprehensively improved, and the problems of data isolation, response lag and manual dependence in a traditional mode are effectively solved.

The invention relates to the technical field of natural language generation, in particular to a retrieval enhancement dialogue generation system and method applied to digital human shopping guide, and the system comprises an input module, a semantic analysis module, a multi-strategy memory module, a context management module, a retrieval module, a generation module and a privacy and security control module. The method has the beneficial effects that through the multi-strategy memory module and dynamic user portrait construction, personalized demands and preferences of users can be deeply understood and captured, and accurate product recommendation and customized purchase suggestions are realized. The shopping satisfaction degree of the user is improved, and the stickiness and loyalty of the user are enhanced.

The present disclosure relates to the technical field of artificial intelligence. Provided are a data generation method, a natural language processing method, a model training method, and a device and a medium. The data generation method comprises: on the basis of a target data table in a database, constructing a target structured query language, wherein the target structured query language is configured to query at least one piece of data in the target data table; using a first natural language model to generate a target natural language corresponding to the target structured query language; and on the basis of the matching degree between the target data table and the target natural language, and the target structured query language and the target natural language, generating training data for training a second natural language model, wherein the second natural language model is configured to process a received natural language to generate a corresponding structured query language. The training data generated in the present disclosure can make a trained natural language model more accurately identify a query intent in the natural language, and support a user to use the natural language to meet the requirement for database query.

The invention relates to a power grid multi-modal data compliance monitoring method based on a knowledge graph, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data of a power grid, carrying out the preprocessing, and generating structured and semantic data representation; s2, constructing an initial knowledge graph according to the produced structured and semantic data representation; s3, according to the initial knowledge graph, combining a time perception mechanism TGN to identify compliance standards and laws of a power grid operation environment changing with time, combining rule-based logical reasoning with a deep neural model through neural symbol fusion, realizing compliance judgment under a fuzzy condition, and constructing a knowledge agent; s4, based on the knowledge agent, performing multi-modal compliance analysis in combination with deep learning and symbolic reasoning to obtain an analysis result; and S5, based on an analysis result, in combination with a natural language generation technology, automatically generating an interpretation report for the violation event. According to the invention, dynamic intelligent compliance analysis is realized, and the intelligent and automatic level of power grid management is significantly improved.

Systems and methods for executing domain-specific controls on large language model-generated data are disclosed herein. The system may receive a textual communication and provide the textual communication to a first model to generate an output. Based on the output and the textual communication, the system may generate a communication profile. The system may determine that the communication profile satisfies first or second criteria. Based on determining that the communication profile satisfies the first criteria, the system may determine rulesets corresponding to domains and provide the communication to a second model to generate a second output according to these rulesets. Based on determining that the communication profile satisfies the second criteria, the system may cause execution of a termination protocol in lieu of generating the second output.