397 results about "Natural language generation" patented technology

The invention provides a query method and system for generating an SQL statement based on a natural language, and relates to the technical field of programming, and the method comprises the steps: receiving a user query request, obtaining data source configuration information, and obtaining a multi-stage locking identifier through a distributed lock management module to control concurrency; extracting a query mode from the query mode knowledge base to generate a data template based on the data source configuration information and a user historical query record; selecting an optimal query path through query path evaluation, and calling the large model server to analyze the data template to generate an SQL statement; predicting resource occupation and setting a monitoring threshold value according to the SQL statement structure, monitoring a query state in real time in an execution process, and triggering a multi-stage fault-tolerant mechanism to process exceptions; and finally, releasing the locking identifier, returning a query result, and asynchronously updating the query mode knowledge base.

The invention discloses a text robot application system based on a large model, and the system comprises the following modules: a knowledge base and analysis module which receives and processes a business document, decomposes the content into semantic units, and marks context features; the question-answer generation module automatically generates question-answer pairs through deep semantic analysis and stores the question-answer pairs in a vector database; the question extension module is used for dynamically extending similar questions by using a natural language generation technology; the semantic intention analysis module is used for accurately extracting a problem core intention through context semantic analysis and mapping the problem core intention to a service path; the text workflow management module is used for realizing dynamic routing of multiple service scenes by adopting a logic tree model; the context semantic association module is used for analyzing and optimizing problem expression through multiple rounds of dialogues; the bottom response module is used for providing multi-level answers in combination with a question and answer database, document recall and dynamic generation; and the intelligent learning module records unsolved problems and optimizes the question and answer library and semantic rules through a learning algorithm.

The invention relates to the technical field of software development, and particularly provides a self-adaptive code annotation updating method and system, a terminal and a storage medium, and the method comprises the following steps: confirming that codes are updated, extracting an updated code set, and combining and constructing an abstract syntax tree for the codes; performing semantic analysis on the abstract syntax tree by using a deep learning model to generate a semantic vector, and constructing a program dependency graph based on the semantic vector; analyzing a dependency relationship among code elements in the program dependency graph by utilizing a graph neural network; and generating a code annotation based on the semantic vector and the dependency relationship between the code elements by using a natural language generation model. According to the method, the real-time performance and the accuracy of the code document are ensured, the readability and the maintainability of the code are greatly improved, and the maintenance cost is reduced.

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 an enterprise financial management system, and the system comprises a data collection and integration module, a budget and financial execution prediction module, an intelligent decision optimization module, a financial risk prediction and automatic early warning module, an intelligent auditing and compliance guarantee module, and a real-time financial analysis and report module. The real-time financial analysis and report module provides a multi-dimensional dynamic financial analysis and customized report for a management layer by using a natural language generation and data visualization technology, and the system can dynamically process structured and unstructured data, ensure the uniformity and timeliness of the data, and improve the real-time financial analysis and report efficiency. According to the invention, the method achieves the seamless connection of budget and actual execution, supports multi-scene and multi-target decision optimization, remarkably improves the transparency, adaptability and foresight of financial management, improves the accuracy and response capability of decision, and provides an intelligent, global and dynamic solution for the financial management of enterprises.

Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-turn dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.

Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

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 AI-based new product material design method and system, particularly relates to the technical field of image material design, and is used for solving the problem of low matching degree between material design and target audiences. According to the method, user behavior data are collected and cleaned, user emotional states are recognized in combination with emotion analysis, accurate user portraits are constructed, interests and preferences are extracted, personalized design versions are generated by using a generative adversarial network based on the user portraits, and design elements are optimized in combination with real-time user feedback and a reinforcement learning algorithm. A design version is evaluated through an A / B test and a dobby machine algorithm, an optimal version is selected for adjustment and optimization, a marketing copywriting is generated by using a natural language generation technology in combination with a user portrait and design elements, a design drawing is optimized through image processing and generative adversarial network, the size, hue and layout are automatically adjusted, the display effect of marketing materials is improved, and the marketing efficiency is improved. Design version evaluation and optimization are carried out based on real-time market feedback, and the marketing effect is improved.

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.

The invention provides a question and answer model training method and device and a question and answer method and device. The method comprises the steps of obtaining sample question and answer data; the sample question and answer data comprises sample questions and real answers corresponding to the sample questions; based on the pre-training model, the semantic information of the sample problem is applied, the sample problem is converted into a pseudo code in a preset format, and the logic of the pseudo code is consistent with the logic of the corresponding sample problem; based on the illusion evaluation model, generating a sample correct answer and a sample wrong answer of the sample question by applying a pseudo code; and training a pre-training model by taking the sample question and answer data, the sample correct answers and the sample wrong answers as training samples to obtain a question and answer model. According to the question and answer model, the logic consistency of input and output in the natural language generation task can be guaranteed, the accuracy of generated answers can be guaranteed, and the illusion problem is avoided.

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.

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 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.