290 results about "Intelligent document" patented technology

The invention relates to the technical field of data processing, in particular to an intelligent document retrieval and generation system based on metadata driving, which comprises a metadata extraction module, a dynamic index construction module, an intelligent routing module, a distributed retrieval module and a result generation module. The metadata extraction module extracts organization names, timestamps and technical classification tags from an input document, maps the organization names, the timestamps and the technical classification tags into structured key value pairs, and transmits the structured key value pairs to the dynamic index construction module through a streaming pipeline; and the dynamic index construction module constructs a skip list index based on discrete fields to realize rapid matching, fuses metadata features and document semantics by adopting a multi-head attention mechanism, and outputs a structured response through topic clustering. According to the method, through a metadata pre-filtering, path optimization and semantic analysis cooperation mechanism, the multi-dimensional composite query efficiency in a distributed environment is improved, and noise interference is suppressed.

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

The present disclosure relates to the technical field of document recognition, and provides an intelligent document recognition method and apparatus, an electronic device and a storage medium. The method comprises: extracting text information and layout information from a document to be recognized; acquiring a template containing a specified field; on the basis of an industry knowledge base corresponding to the document, adding a label to the text information; and, on the basis of the layout information and the label, using a large language model to recognize text information matched with the specified field from the text information to which the label has been added, the large language model being a large language model that has been trained by using the industry knowledge base. In the embodiments of the present disclosure, document recognition results more conform to industry attributes, improving document recognition accuracy.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by extracting in real-time, based on the formation of byte array objects from the live stream of image data, data fields from a ranked sequence of imagery to be processed by an optical character recognition (OCR) program resident on the client device. The OCR process extracts one or more data fields from of the financial instrument that are communicated to a remote deposit server to complete the electronic deposit.

The invention provides a semi-structured file processing method based on an LLMs (Language Language Model), which is used for efficiently analyzing a semi-structured file and converting the semi-structured file, and belongs to the technical field of intelligent document processing and multi-modal learning. The method is characterized by comprising the following steps: multi-modal fusion analysis: adopting a hierarchical cross-modal attention mechanism HCMA, and combining a multi-head attention mechanism with a graph neural network GNN to realize multi-level feature fusion of a text and an image; adaptive structured conversion: adopting a dynamic structure analysis network DPN, and combining reinforcement learning and a generation model to realize adaptive analysis of a document structure; and model generation and multi-modal reasoning: adopting a multi-modal generation reasoning network MGRN, and combining a diffusion model and neural symbol reasoning to realize joint generation and reasoning of the text and the image. According to the method, a remarkable technical breakthrough is brought to the aspects of document analysis, information extraction and intelligent reasoning, and powerful support is provided for data-driven innovation of various industries.

The invention discloses a law shield AI intelligent case handling all-in-one machine system based on a large language model, and relates to the technical field of law artificial intelligence and judicial informatization, the system comprises an integrated terminal device, a case semantic modeling module, a class case knowledge engine, a risk prediction module, a large language model service interface and an intelligent document generation module; the system constructs a case semantic graph through multi-modal information fusion and a graph neural network, performs legal rule path matching and similarity reasoning based on a class case database, outputs structured legal suggestions and standard legal instruments in combination with user context recognition and large language model multi-round generation capability, and realizes dynamic updating of the semantic graph. The method improves the automation, structuring and interpretability of case processing, and is suitable for intelligent case handling scenes such as legal assistance, litigation assistance and judicial mediation.

The invention relates to the technical field of intelligent document processing, and particularly discloses a file arrangement processing method and system. The method comprises the following steps: acquiring an input document, analyzing the structure and the style of the input document, and generating standardized format data; extracting a semantic relationship of the document through a pre-trained text understanding model, constructing a semantic relationship graph and optimizing a chapter sequence; loading a document processing plug-in to execute cross-format arrangement, and generating intermediate format data; executing processing nodes based on a task scheduling mechanism to generate optimized content; calling a multi-format output module to generate a target document, and adjusting metadata in combination with the global variable; and finally evaluating the document quality and updating the model. According to the method, automatic analysis, intelligent optimization and format unification of the multi-source heterogeneous document are realized, and the intelligent degree and the output efficiency of document processing are improved.

The invention discloses an intelligent document question-answering method and device, equipment and a storage medium, and relates to the technical field of intelligent question-answering, and the method comprises the steps: obtaining a target query question of a target user; based on keyword matching and vector matching, keyword document fragments and vector document fragments related to the target query problem are retrieved from a target document knowledge base; generating recall block information according to the keyword document fragment and the vector document fragment; and generating a large model cue word according to the recall block information, and outputting target answer information for the target query question through a large model according to the large model cue word. The answering quality can be improved.

The invention discloses an OCR (Optical Character Recognition)-based document automatic recognition intelligent management system, and relates to the technical field of intelligent document OCR processing. A preprocessing module adopts adaptive filtering and a GAN (Generic Area Network) to repair low-quality documents and separate seals from characters; the recognition engine realizes end-to-end recognition of inclined characters and mixed characters through a multi-scale feature pyramid network, and the semantic module constructs a dynamic knowledge graph and supports event evolution reasoning and cross-year correlation analysis; the classification module realizes small sample classification based on a prototype network, the security module realizes fine-grained authority control and anomaly detection through a block chain and federated learning, the text edge definition is improved after system processing, and real-time reasoning is supported. The intelligent level of document processing is improved through cooperation of multiple modules, the preprocessing and recognition module solves the complex scene problem, and deep analysis and correlation analysis are achieved through semantic understanding and a knowledge graph; the security module guarantees data security; and the efficiency and decision scientificity are comprehensively improved.

The invention belongs to the technical field of natural language processing and intelligent document processing, and relates to an intelligent document checking method and system of a visual workflow, and the method comprises the steps: generating the visual workflow according to an obtained verification demand; executing the workflow, and based on the extracted document information of the to-be-reviewed document, in combination with the review rule and the domain knowledge base, reviewing the to-be-reviewed document through a preset large language model to obtain a review result; and generating a verification report according to the verification result. The problems that the verification accuracy is poor, the verification rule configuration is not flexible, and the verification process cannot be visualized are solved.

The invention relates to the technical field of BOM data analysis, and discloses a collaborative BOM data analysis and verification system and method based on an AI model.The collaborative BOM data analysis and verification system based on the AI model comprises an intelligent document sensing and structure analysis module, the intelligent document sensing and structure analysis module monitors and identifies a list file, and sends the list file to a server; and the AI assistant dialogue engine module adopts a language model to interact with an engineer to analyze the document in the unified format field by field. According to the method, a field-level approval mechanism is introduced, each suggestion can be modified after being confirmed by a user, and the user can execute operations such as rejection, remark and pending instead of rechecking only in the final stage, so that the sovereignty, the safety and the traceability of the user are improved, the liability boundary of AI aided design is cleared, the AI is evolved from a tool to an engineering partner with learning ability, and the work efficiency is improved. Interaction intelligent experience is obviously enhanced, and adaptability is high.

The invention relates to the technical field of intelligent processing of document information, discloses a method and a system for extracting RAG type data items and checking consistency for engineering technology documents, and solves the problem that an existing scheme is difficult to consider high-accuracy extraction, cross-document consistency, strong traceability and structured landing in engineering technology document data processing. The scheme comprises the following steps of: performing layout analysis and necessary OCR (Optical Character Recognition) processing on a received document, and dividing the document into multiple levels of text blocks; carrying out vectorization processing on the multi-level text blocks by adopting an embedding model subjected to two-stage fine adjustment, establishing a vector index and recalling candidate fragments to form a candidate fragment set; a cross encoder with efficient fine tuning of parameters is adopted to screen out high-correlation fragments; performing standardization processing by extracting a target variable to form a structured record; consistency checking is conducted on the target variables, and finally structured records and checking results are bound with minimum evidence fragments and corresponding position information to be exported through a format list.

The invention discloses a document auditing method, device and equipment based on a large model and a storage medium, and relates to the technical field of deep learning, and the method comprises the following steps: obtaining document auditing data from a plurality of preset data sources, and analyzing the document auditing data; the document auditing data comprises an auditing material for representing a document auditing rule, a historical document auditing case and a standard document; constructing a document auditing database based on the analyzed document auditing data, and constructing a large model fine tuning data set based on the document auditing database; performing fine tuning on a preset document auditing large model by utilizing the large model fine tuning data set to obtain a target document auditing large model; and determining a current document auditing task, and auditing a to-be-audited document of the document auditing task by using the target document auditing large model to generate an auditing report. Automation, high efficiency and accuracy of document auditing are achieved through intelligent document analysis, auditing database retrieval and compliance judgment of a fine adjustment large model.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent document processing method, device, equipment and medium, comprising: receiving a document processing request and generating a task identifier, creating and sending a document processing task, constructing an intermediate document structure and generating a plurality of subtasks, and processing the sub-tasks, writing states and page contents into a cache, regularly querying task states, updating accessible identifiers, responding to an access request, loading the page contents, and integrating pages to generate a complete document after all the sub-tasks are completed. According to the method, the document generation task is divided into a plurality of sub-tasks which can be independently processed, and page-level asynchronous loading is realized in combination with a state cache and a regular query mechanism, so that the response speed and the system processing efficiency are remarkably improved while the generation accuracy is ensured; the problems that in the prior art, document generation delay is high, loading is slow, and user waiting time is long are effectively solved.

The invention relates to the technical field of intelligent file processing, and discloses an element extraction method and device of a test report, computer equipment and a storage medium, the method comprises the following steps: obtaining scanning document documents of a plurality of paper test reports, and screening out a multi-modal format document from the scanning document documents; identifying and extracting text features, picture features and table features in the multi-modal format document based on an optical character identification technology model; performing paragraph combination on the extracted text features; and extracting structured data from the text features, the picture features and the table features after paragraph combination by adopting a multi-modal element extraction model to obtain a complete electronic document corresponding to the multi-modal format document. Based on an optical character recognition technology, character features, image features and table features in various paper test reports are accurately recognized and extracted by using a number, image and character recognition and extraction technology, and the recognition and extraction accuracy and extraction efficiency are improved.

A computer-implemented system for automated document generation receives user input and processes it through artificial intelligence models to create professional documents. The system combines user-provided information with specialized knowledge databases containing domain-specific requirements and formatting standards to generate enhanced processing instructions. Trained machine learning models process these instructions to produce contextually appropriate content that addresses specific technical and legal requirements. The generated content is formatted using configurable templates to create completed documents that meet professional standards. The system provides adaptive user interfaces that adjust functionality based on user expertise levels, supports multiple operational modes including guided and automated generation, learns from example documents to improve output quality, and enables comprehensive workflow management through network-accessible services. Applications include patent applications, legal responses, technical documentation, and other specialized professional documents requiring domain expertise and regulatory compliance.

The invention provides a private deployment-oriented multi-modal AI knowledge base construction system. The private deployment-oriented multi-modal AI knowledge base construction system comprises a knowledge storage module, an intelligent document loading module, a document partitioning engine module, a data enhancement engine module, a multi-language semantic vector alignment module and a private deployment module, the knowledge storage module comprises a knowledge authority management sub-module and a knowledge source management sub-module, the knowledge authority management sub-module is used for managing and storing knowledge from different sources, and the knowledge source management sub-module is used for managing electronic documents and multimedia documents; according to the method, intelligent identification, partitioning, vectorization and source file storage can be carried out on different types of electronic files, knowledge graph construction is carried out for specific fields, semantic relevance between texts and topics and key entities is fully considered, the method has wider applicability, higher robustness and controllability, the data leakage risk is effectively reduced, and the method is suitable for popularization and application. The method is suitable for enterprise sensitive data protection and personal user elastic computing power requirements.

The invention discloses an intelligent document duplicate checking system and method based on a vector database and a large language model, and the method comprises the following steps: S1, collecting document data in various formats, and carrying out the preprocessing of the document data; s2, semantic coding is carried out through a large language model, and a document semantic vector is generated; s3, storing the document semantic vector into a vector database, constructing a vector index and recording historical query data; s4, carrying out preliminary candidate document retrieval, and carrying out approximate nearest neighbor retrieval based on outlier identification; s5, calculating the similarity between the candidate document and the document to be subjected to duplicate checking, and screening a final high-similarity document; s6, generating a duplicate checking report, and recording user operation behaviors; and S7, receiving user feedback, and dynamically updating the document semantic vector and the vector index. According to the method, efficient and accurate intelligent document duplicate checking is realized by utilizing the large language model and the vector database, the semantic matching capability is improved, the duplicate checking efficiency is optimized, and the intelligence and adaptability of a duplicate checking system are improved.

The invention provides an enterprise intelligent document identification and automatic classification archiving management processing method and system, and relates to the technical field of intelligent document processing, and the method comprises the steps: constructing a hierarchical cognitive attention network, extracting initial features of a document through a capsule network, and carrying out semantic processing through a memory enhancement neural network; and adaptively adjusting feature extraction parameters based on document complexity to obtain cognitive feature representation. Mapping the cognitive feature representation to a non-Euclidean manifold space, determining an optimal classification boundary by using an improved particle swarm optimization algorithm, and generating a multi-dimensional classification vector; and constructing an adaptive feedback optimization network, fusing the cognitive features and the classification vectors, carrying out iterative optimization based on the consistency evaluation score until a preset threshold value is reached, and outputting a final classification result.

The invention discloses an intelligent document analysis method of a knowledge base management system, and relates to the field of artificial intelligence large model multi-modal analysis. Comprising the following steps: step 1, preliminarily identifying a document format according to a document extension name, and performing corresponding analysis according to the type of the document format: performing text analysis for pdf, docx, doc and txt document formats, and extracting texts and images in the pdf document format for the pdf document format; aiming at the docx document format, analyzing the compressed package in the docx document format according to the natural sequence of the document, and extracting a text and a picture; aiming at the doc document format, identifying and extracting all text information in the doc document format; for a txt document format, character codes are recognized and processed, and various languages and symbols are extracted; the method comprises the following steps of 1, analyzing a text, 2, processing the analyzed text by utilizing a large model, namely segmenting the text, performing vectorization processing, converting the text into a numerical form which can be understood by the large model, and storing the converted text in a knowledge base for information retrieval, data analysis and knowledge mining, and 3, performing visual output on the text at the same time.

The present disclosure herein addresses the problem of synthesizing a series of documents and extracting or summarizing meaningful information or content embedded as tacit knowledge in the series of documents. The embodiment of the present disclosure provides a system and method for tacit knowledge generation using large language model (LLM) in document synthesis. The method of the present disclosure performs intelligent document generation orchestrating a generative artificial intelligence solution workflow. In the present disclosure, tacit knowledge of subject matter experts in a knowledge base or in a series of documents is extracted. Further a content capturing the tacit knowledge is generated leveraging a large language models (LLMs) framework as the underlying architecture. The system of the present disclosure is artificial intelligence (AI) accelerated, cloud agnostic, latency defined, and security enabled.

The invention relates to a field information analysis method and system based on RAG and a knowledge graph, and belongs to the technical field of artificial intelligence, and the system comprises a multi-source heterogeneous data collection module which is used for collecting field-related original data; the intelligent document processing engine is used for carrying out cleaning and structured processing on the data and extracting entities, relationships and events; the multi-source knowledge graph construction and management module is used for constructing and fusing multi-sub-field knowledge graphs to form a unified knowledge graph; the RAG retrieval enhancement generation dynamic fusion sequencing module is used for receiving user query, performing multi-channel information retrieval by querying a text vector index and a knowledge graph, performing fusion resequencing on a result, and generating an analysis result by utilizing a large language model; and the agent workflow engine is used for decomposing query into subtasks and scheduling professional agents to cooperatively complete analysis and report generation. Deep semantic understanding, intelligent retrieval and automatic analysis of domain information are realized, and the accuracy and efficiency of information processing are improved.

The invention belongs to the technical field of intelligent document processing, and provides a WEB-end intelligent bidding document structured processing system based on a hybrid AI analysis engine, comprising: a multi-modal document analysis module extracts key information of a bidding document by using a fuzzy starvation game algorithm, and dynamically sorts core terms according to semantic association; the bidding document blind box analysis module safely disassembles the encrypted bidding document through a block chain technology to generate a structured review matrix; the cloud collaborative review module integrates the multi-dimensional data board, simulates bid evaluation by using a Monte Carlo algorithm, and generates a risk thermodynamic diagram; the risk early warning module identifies dispute points, provides compliance suggestions in combination with a knowledge base, and synchronizes the compliance suggestions to all review terminals; according to the method, automatic and structured processing of the WEB end bidding document is realized by fusing the mixed AI analysis engine, so that the efficiency and accuracy of bidding document review are improved, manual intervention is reduced, the review process is accelerated, and the risk of misjudgment and omission is reduced.

The invention relates to an intelligent document information real-time retrieval method and system based on the RAG technology, and the method comprises the steps: analyzing documents of various formats, extracting a text, maintaining the content continuity through adaptive semantic partitioning processing, and building a character-level position index at the same time; after vectorizing the text blocks, generating a plurality of rewriting queries for the original query; respectively carrying out mixed retrieval (combining keywords and semantic retrieval) for each rewriting query, and fusing and reordering results to obtain candidate text blocks; after correlation filtering, inputting a large language model according to correlation to generate an answer, and if the result is negative, triggering secondary retrieval and reordering; and finally, outputting a structured answer containing position information and supporting front-end visualization. According to the method, the semantic integrity maintenance, the multi-format document processing efficiency and the key information retrieval accuracy are effectively improved.

The invention belongs to the technical field of intelligent document processing, and discloses an intelligent data processing method for collected files based on artificial intelligence, which comprises the following steps: acquiring a multi-source multi-version file to carry out layout rectification and OCR (Optical Character Recognition) to obtain a page fragment object set; using a controlled decoding language model to extract clause records according to preset Schema to form a clause record set; performing cross-version alignment on homonymous or synonymous terms, and constructing a time sequence knowledge graph; compiling the cross-document consistency rule into an executable rule set which can be evaluated on the time sequence knowledge graph by adopting an executable collection constraint language; evaluating the scope of action of each rule to generate conflict candidates; and calculating a minimum hit set to obtain a minimum conflict interpretation set, positioning a root cause term instance, and generating a minimum repair suggestion according to a normalization result of the operator and the node violating the rule. According to the method, the unification of clauses and calibers and the automatic judgment of the latest effective state, the accurate positioning of conflicts and the minimum cost repair are realized.

The invention provides an intelligent document management method and system, and the method comprises the steps: obtaining an uploaded file set, processing each file through a text analysis algorithm, an image analysis algorithm and a structured data analysis algorithm, generating a first feature set, and storing the first feature set in a temporary database; obtaining a distribution record from the first permission configuration file, writing the distribution record into an audit database through a log audit module, and generating a first operation traceability chain; according to the first operation tracing chain, a storage management module is called to perform fragmentation processing on the file and upload the file to cloud storage, a first storage index is generated, and the metadata table and the archive position are associated; and according to the second storage index, calling a disaster recovery backup algorithm to perform multi-region copying on the file, generating a first backup confirmation signal, and recording the first backup confirmation signal into an operation and maintenance log. According to the system and the method, intelligent classification, sensitive information protection and whole-course traceable management of the files are realized, the automation level and the safety of document management are improved, and a comprehensive solution is provided for document management.

A platform which provides a system and method for intelligent document processing with anomaly detection and predictive analysis comprising a user interface which allows platform users to upload documents, a data acquisition engine that leverages one or more machine and / or deep learning algorithms to classify, validate, and enforce compliance of the uploaded documents, and an artificial intelligence engine that constructs and maintains the models developed from the machine and / or deep learning algorithms. The platform may utilize various bespoke APIs to integrate validated data with third-party systems when an authorized entity initiates the process. The platform can function as a system of record and central, secure repository for an applicant's documentation and information required for various application processes. In some embodiments, the platform utilizes a trained generative AI model to assist platform users and to provide predictive analysis responsive to user submitted queries.

The invention discloses an intelligent document generation system and method for bidding documents, and the system comprises a preprocessing module which is used for obtaining bidding document files in various formats, and converting the obtained bidding document files in various formats into structured bidding document files; the analysis module is used for analyzing the converted bidding document file based on the large model cue word to obtain a structured bidding document file analysis result; the directory generation module is used for generating a directory of the bidding document file according to the analysis result; the retrieval module is used for generating a corresponding knowledge content set according to the bidding document file directory; and the content generation module is used for generating the text content of the bidding document file based on the knowledge content set and the structure information of the bidding document file directory. And the content assembly module is used for performing multi-dimensional feature extraction and classification on the generated bidding document file text content, and completing content assembly according to a preset document structure tree to obtain a standard document meeting bidding document requirements.

The invention discloses an intelligent document reviewing and traceability positioning method based on LLM natural language processing. The method comprises the steps that 1, a reviewing document enters an OCR analyzer to be split into text blocks and positioning information; 2, vectorizing the text blocks and storing positioning information corresponding to each text block into a vector database; step 3, vectorizing the review rule; step 4, performing enhanced hybrid retrieval based on the vectorized review rule; step 5, rearranging the most relevant K text blocks with the positioning information retrieved in the step 4; step 6, the large language model extracts key information from the rearranged text blocks according to an examination rule based on a cue word project; 7, the big language model returns a judgment result and positioning information based on the cue word project according to the review rule and the key information; and 8, displaying a judgment result and positioning traceability information. According to the invention, the efficiency and accuracy of examination can be improved, and accurate traceability and positioning of error contents can be realized.

The invention relates to an AI intelligent document processing method based on a large language model, and the method comprises the steps: receiving the input of an original document, and obtaining the text features, visual layout features and semantic structure features of the document through a multi-modal feature extraction module; processing the multi-modal features by using a layout-aware attention mechanism, and generating an incidence matrix of document elements; inputting the extracted features into a pre-trained large language model, and generating an intermediate representation containing an association relationship between document elements; and dynamically selecting a processing path according to task requirements, wherein the processing path comprises but is not limited to document classification, information extraction, content generation or question and answer response. According to the AI intelligent document processing method based on the large language model, through an attention mechanism of layout perception and an experiment on a standard data set, table structure recognition F1-score is improved, the cross-page element association accuracy is greatly improved, the vision-text feature alignment loss is reduced, the processing time of a 100-page PDF document is greatly shortened through a dynamic partitioning processing strategy, and the processing efficiency of the 100-page PDF document is greatly improved. And the memory occupancy peak value of the GPU is reduced.