22011 results about "Natural language" patented technology

The invention discloses a power transmission and distribution production task cooperation system and method based on an intelligent agent, and relates to the technical field of power distribution production task scheduling, the system comprises six modules: a natural language input interaction module processes a user instruction and multi-modal information, and generates structured data; the electric power field knowledge enhancement analysis module establishes mapping from a natural language to business data; the dynamic interaction context memory module stores historical interaction data and generates a context feature vector through a bidirectional LSTM and an attention mechanism; the intelligent task scheduling and conflict resolution module is used for disassembling instructions into sub-tasks, dynamically evaluating priorities in combination with three-dimensional indexes and resolving resource conflicts; the agent task execution and cooperation module drives agents to execute tasks according to priorities and synchronize states in real time; the system closed-loop feedback optimization module analyzes the execution log and automatically updates model parameters; according to the system, the problems of term analysis deviation, strategy staticization and insufficient self-optimization capability of a traditional scheduling system are solved.

The invention discloses a knowledge question-answering system based on a large language model. The method comprises the steps that a power operation and maintenance knowledge base module obtains data from a data source for preprocessing, constructs a power operation and maintenance field knowledge graph, converts text content into semantic vectors and integrates the semantic vectors to form a power operation and maintenance knowledge base; the problem understanding module is used for identifying and analyzing problems and converting the user problems into semantic vectors; the retrieval and reordering module is used for performing semantic retrieval in the electric power operation and maintenance knowledge base, recalling knowledge information related to question semantics, and ordering retrieval results based on semantic relevance, knowledge confidence and information importance; and the answer generation module is used for integrating the knowledge information and the user question into input of a large language model, generating prompt information, and generating an answer in a natural language form according to the prompt information by adopting the large language model based on data fine adjustment in the field of power operation and maintenance. According to the scheme, the deep learning technology is used for understanding semantics of user questions, and accurate retrieval is carried out in the knowledge base.

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention discloses an interactive question intelligent agent system based on a large language model, and relates to the technical field of dialogue interaction systems. Aiming at the technical defects of the traditional BI tool, the technical scheme is as follows: a heterogeneous data management engine realizes unified access and secure access of cross-source data; the NLP semantic recognition engine converts a natural language into structured semantics through multiple steps, drives the hybrid SQL generation engine and provides visual parameters; the generation engine realizes precise generation from semantics to SQL through a two-stage architecture based on metadata and authority rules; and the visual management console is combined with multi-party configuration and parameters to visually build a number-asking agent. The method is used for realizing intelligent conversion from a natural language to a structured query language (SQL) and a data service interface, and automatically generating an interactive data visualization result.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The present invention provides a method and system for extracting and analyzing long text information and association based on a large model, which relates to the technical field of natural language processing, including determining sliding window parameters based on semantic density values ​​to divide long text into blocks, extracting feature vectors of text blocks and calculating semantic relevance, and constructing a hierarchical clustering feature matrix to merge text blocks to obtain semantic fragments. Afterwards, the semantic fragments are syntactically analyzed to construct a semantic dependency tree, and the semantic network is extracted and the entity relationship strength value is calculated to construct a semantic association graph. Finally, the node features are extracted using a pre-trained large model, the semantic representation is fused through a hierarchical attention mechanism, the features are enhanced by propagating an adaptive graph network, and candidate paths are generated through a hierarchical decision network, the optimal path is selected, and the associated path and probability distribution are output. The present invention can effectively extract key information from long texts and analyze the association relationship between entities, thereby improving the accuracy and efficiency of information extraction.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The invention discloses a power plant operation and maintenance knowledge intelligent query method based on a large language model and an RAG technology. The method comprises the following steps: constructing a power plant operation and maintenance knowledge vector library covering structured, semi-structured and unstructured data; receiving a natural language question of a user, inputting an improved instruction to align a preprocessor, and generating a question semantic vector and an intention tag; relevant knowledge fragments are retrieved and sorted through a semantic matching retriever in combination with the intention labels; constructing a large language model cue word structure based on the retrieval result and the original question, generating candidate answers and recording a reference path; and finally, performing term specification and consistency verification according to the expert rule base, and outputting a structured and traceable final answer. According to the invention, the improved RAG technology is fused to realize intelligent query of the operation and maintenance knowledge of the power plant.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a dynamic question and answer matching method and system based on context awareness. The method comprises the following steps: acquiring history of multiple rounds of dialogues and user background information, and generating a semantic understanding model of a current dialogue; based on the semantic understanding model and the current user input, matching the most relevant answer from the question and answer library, and dynamically adjusting the matching strategy; acquiring real-time feedback data of the user, updating a personalized recommendation algorithm based on the interest change of the user, and generating a personalized answer recommendation list; dynamically updating the content of the question and answer library according to the personalized recommendation list and the user behavior data; and inputting the user feedback and the updated question and answer library data into a personalized recommendation system to generate final personalized answer recommendation. Through context sensing and real-time dynamic adjustment, the matching precision of the question answering system is remarkably improved, the interaction experience of the user is improved, and the adaptability and flexibility of the system are enhanced.

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

A computer-implemented method for automatically managing applications in environments using AI driven autonomic application management framework is disclosed. The computer-implemented method includes obtaining items of data from electronic devices associated with users, and databases; determining semantics and structure of natural language texts associated with the service level agreements (SLAs) based on analysis of natural language texts, using a first AI model; extracting service level objectives (SLOs) and associated metrics corresponding to services specified in SLAs; obtaining first real-time data including actual performance levels, and service level indictors, of the services, from monitoring platforms; determining whether actual performance levels of services, are compliant with expected performance levels; automatically updating the SLOs and associated metrics based on deviations of actual performance levels of the services from expected performance levels; and generating insights associated with actions, to be applied to corresponding services, to be performed to automatically manage the applications.

The invention discloses a Text2SQL (Structured Query Language) implementation method and system, and relates to the field of data processing, and the method comprises the following steps: firstly, receiving a natural language query, and analyzing a query intention, field classification and a key entity through a planner; the searcher obtains domain knowledge, entity information, a database table structure and a historical query mode in a multi-path parallel mode based on the planning result; the generator constructs an SQL framework according to the retrieval result and generates an initial statement; the verifier carries out grammar, table field, authority and logic multi-dimensional verification on the SQL, and if the verification fails, iteration adjustment is carried out to generate logic; when the SQL is executed, the result is formatted and a natural language explanation containing query logic, a data source and a calculation method is generated if the SQL is executed successfully, and a diagnosis and error correction mechanism is started for correction and then rechecking is performed if the SQL is executed unsuccessfully. According to the method, through deep fusion of domain knowledge, whole-process verification error correction and interpretability enhancement, the accuracy, robustness and user interaction experience of SQL conversion in a professional scene are improved.

The present disclosure relates to computer-implemented methods, software, and systems for transferring contexts from a generic chatbot to a domain-specific chatbot. In one example, a registration at a first conversational interface is received that identifies at least one endpoint associated with a first entity that is separate from the first conversational interface and is associated with a domain-specific conversational interface. The first conversational interface can interact with a user through a natural language search interaction. The interface can identify at least one response associated with one of the at least one endpoints associated with the first entity, and can provide the response and an interactive response associated with initiating a transfer of the interaction from the first conversational interface to a domain-specific conversational interface of the first entity. Once accepted, the interaction is transferred to the first entity and its domain-specific conversational interface.

The invention provides an unstructured text data calculation method and system, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the multi-level processing of input unstructured text data, dynamically adjusting the word segmentation granularity according to the word frequency distribution, constructing a part-of-speech co-occurrence matrix in combination with context semantic information, and extracting entity information. And fusing the part-of-speech co-occurrence matrix and the entity information to generate a hierarchical semantic tag sequence. Feature extraction units with different convolution kernel sizes are adopted to extract feature representation, and cosine similarity between different semantic hierarchies is calculated to establish an association weight matrix. A semantic enhancement vector is constructed based on entity information, and adversarial training is carried out to obtain a multi-modal semantic feature matrix. And calculating the semantic similarity between the fusion feature vectors for clustering, setting difficulty weight sorting according to the complexity, consistency and ambiguity of class clusters, then inputting the class clusters into a classifier, and iteratively optimizing a classification result to obtain a calculation result.

The invention provides a commodity information processing and querying method and system. Belongs to the technical field of information processing. The method comprises the steps of collecting text information of commodities, preprocessing the text information of the commodities through natural language processing information, and constructing a structured commodity text database; based on the high-definition image of the commodity, key features of the commodity are extracted through machine vision and are associated with information in the text database to form multi-modal commodity information representation; the method comprises the following steps: encoding a commodity text by adopting a BERT model based on Transform, and obtaining a deep semantic representation of the text; performing feature extraction on the commodity image by using a pre-trained convolutional neural network to obtain an advanced visual feature of the image; through natural language processing and machine vision technologies, the text information and the image information of the commodity are subjected to structured and multi-modal fusion, so that the processing efficiency of the commodity information is greatly improved.

The implementations herein disclose advanced systems and methods for integrating, analyzing, and reasoning over heterogeneous data at scale. In some implementations, the system comprises a synergistic data processing infrastructure featuring: a graph database core for unified data representation; specialized loaders for concurrent ingestion and processing of structured, unstructured, and time series data; a natural language reasoning engine leveraging large language models; and a multi-modal user interface.

The invention discloses an enterprise global data analysis method based on a knowledge graph and a large language model, and the method comprises the following steps: collecting internal and external multi-source data of an enterprise, cleaning, standardizing and mapping, and constructing a unified data graph; after a user inputs a natural language query, a reasoning target and task description are generated in combination with semantic understanding, two reasoning paths are formed through structural reasoning and semantic reasoning respectively, and the two reasoning paths are fused for interactive verification and optimization. Logic conflicts and data missing are detected in real time in the reasoning process, and supplementary knowledge is automatically called for local reasoning correction. Through the corrected reasoning result, the system carries out semantic alignment and correlation analysis on multi-source data, a unified enterprise data analysis view is dynamically generated, a reasoning path and evolution information are recorded in the reasoning and analysis process, and subsequent traceable query is supported. According to the method, the data integration, intelligent reasoning and decision support capabilities of an enterprise in a complex data environment are improved.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

A system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide, via the communications module, a first encryption key of an encryption key pair to a client device; receive, via the communications module and from a conversation agent server, a fulfillment request based on a natural language input transmitted from the client device to the conversation agent server; determine that the fulfillment request includes a request for personal data; obtain the requested personal data; encrypt the personal data with a second encryption key of the encryption key pair; and provide, via the communications module and to the conversation agent server, the encrypted personal data for transmission to the client device.

The present disclosure is directed to systems and methods to enhance the process of creating an artificial intelligence (AI) generated content or content items, such as images, text, video, sounds, etc., using a text or other suitable prompt, such as via voice input. The systems and methods disclosed provide streamlined content generation with, e.g., reduced processing power and computing time. In an embodiment the systems and methods receive a prompt for generating a first content item using a generative artificial intelligence (AI) model and retrieve, based on the prompt, a collection of matching content items. The systems and methods may then receive input selecting one of the content items from the collection and identify a prompt used to generate the selected content item. The systems and methods may then merge using a trained natural language processing model, the received prompt with the prompt of the selected content item to create a third prompt. In an embodiment the systems and methods may modify the third prompt based on additional input and, based on the modified third prompt, generate a second content item.

An example operation may include one or more of storing a data store of credit card documentation, conversing with a user via a chatbot within a chat window of a software application, wherein the conversing comprises receiving natural language inputs with requests for information about a payment card, identifying a benefit obtained by the user based on the conversation, executing a large language model (LLM) on the identified benefit and the data store of the credit card documentation to generate a letter of confirmation, and generating an electronic message with the letter of confirmation attached, and transmitting the electronic message to a user device.

A novel method provides an AI language virtual agent having self-improvement features and which uses language modeling and tree search techniques. The AI language virtual agent exchanges textual discussion with users and other simulated agents. The method includes receiving a current situational description depicting natural language user input, temperament qualities and textual tendencies of the virtual agent, and indicia regarding subject matter context of a present conversation. The indicia regarding subject matter context include textual logs from recent conversational exchanges. The current situational description includes audio, visual, and tactile inputs collected proximate to the virtual agent. The method preferably utilizes an MCTS tree search in combination with self-moving modules, one or more language models, tree search techniques outputting textual responses to the current situation description, and the virtual agent responding with textual expression to verbal input in combination with the audio, visual, tactile, and other sensory inputs.

The invention provides a standard document automatic generation and multi-dimensional auditing method and system based on a large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, building a distributed database of a multi-source document, and analyzing a heterogeneous text through natural language processing to obtain a standardized knowledge network; step 2, extracting index elements based on the standardized knowledge network, and forming a structured parameter library through verification and verification; and step 3, based on the structured parameter library, constructing a template library, analyzing user demands in combination with semantic matching, and automatically generating a standard document outline. The document generation efficiency and quality are improved, the manual auditing cost is reduced, and the auditing comprehensiveness and accuracy are enhanced.

Certain aspects of the present disclosure provide techniques for preprocessing data prior to generating vector representations. The data is preprocessed to generate one or more natural language texts describing entities and relationships included in the data. Vector representations of the natural language format texts are stored in a vector database. The embeddings may be retrieved to augment a prompt for a generative model. The embeddings may be selected by performing a search of the vector database using an embedding for the prompt to determine relevant or similar embeddings. The augmented prompt can be input into a large language model. Although the model may be unaware of the data from which the embeddings of the embedding database were generated, the augmented prompt may enable the model to use the data to improve breadth and depth of responses. The preprocessing of the data improves response results of the model.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on architecture diagrams of a software architecture and descriptions of the architecture diagrams, displaying one or more prompts on a user interface, receiving one or more natural language responses associated with the software architecture in response to the one or more prompts, generating a text-based response to the natural language query submitted via the user interface based on execution of the GenAI model on the one or more prompts and the one or more natural language responses associated with the software architecture, and displaying the text-based response via the user interface.

A disclosed method facilitates translation of natural language queries into query language statements usable to retrieve data from or write data to a particular database. The method includes obtaining a pool of shots. Each shot in the pool includes a natural language query component and a corresponding database translation component. The method further provides for vectorizing the natural language query component for each of the shots into a common vector space; receiving a natural language query from a user interface; vectorizing the natural language query within the common vector space; identifying a subset of vectorized natural language query components that satisfy a similarity metric when compared to the vectorized natural language query; and generating an LLM prompt that includes shots from the pool corresponding to the subset of the vectorized natural language query.

The invention relates to the technical field of computer systems for natural language processing or semantic processing, and discloses an enterprise-level schedule planning and knowledge base-oriented intelligent collaborative question-answering system and an enterprise-level schedule planning and knowledge base-oriented intelligent collaborative question-answering method. The system comprises a vectorization processing module, an RAG knowledge base module and the like. The system converts natural language input of a user and enterprise knowledge data into semantic vectors, and retrieves related enterprise knowledge fragments from a vector database based on semantic similarity. The large language model core module analyzes the user intention, generates a preliminary answer and identifies whether a schedule type operation request is included or not; and if the schedule operation request exists, the system splits the request through the multi-agent cooperation module and distributes the request to the corresponding agent to obtain a task processing result. And finally, semantic consistency fusion is carried out on the preliminary answer and a task processing result through a context fusion module, a comprehensive answer is generated by a large language model and is returned to a user side, and unified intelligent response of enterprise knowledge and schedule service is realized.

The invention provides a time sequence knowledge graph federation collaborative optimization method, system and device based on causal inference and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an enhanced knowledge unit with a causal mark through the real-time access of a multi-field heterogeneous data stream and the execution of a space-time alignment operation; by calculating new and old knowledge conflict scores, conflict resolution and version management are realized through a decision tree mechanism, and a time sequence knowledge graph with history tracing is output; node weights are dynamically distributed among distributed nodes based on knowledge entropy, a hierarchical aggregation strategy is adopted to update an entity embedding layer and a relation prediction layer, and a global optimization model is output; the method comprises the following steps: analyzing a natural language query containing an anti-fact condition, extracting a factor sub-graph from a time sequence knowledge graph, executing intervention calculation, and generating an anti-fact influence report, thereby solving the technical problems of a traditional time sequence knowledge graph in the aspects of multi-source heterogeneous data fusion, knowledge conflict resolution and privacy protection; and the accuracy and the interpretability of the knowledge graph are improved.

The invention discloses a video content intelligent management method and system based on multi-modal analysis, and belongs to the field of video content management.The method comprises the steps that multi-modal features of video content are extracted; performing fine structure segmentation on the video content based on the multi-modal features, and segmenting the video into fine segments related to semantics; the fragments obtained after fine structure segmentation are subjected to tagging, and metadata is generated for each fragment; constructing a multi-modal semantic index of the video content based on the generated metadata; according to the method, a semantic retrieval function is provided by utilizing multi-modal semantic indexing, fine fragments required by a user are accurately recalled in combination with text label matching, measurement numerical value scoring, cross-modal vector retrieval and filtering based on a shot language and a shooting technology label, and the user queries and positions a specific time point or the fine fragments in a video through a natural language. According to the invention, the accuracy of video content analysis is improved, the user experience is optimized, and the operation complexity is reduced.