206 results about "Intelligent document" patented technology

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 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.

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 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 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 relates to the technical field of intelligent document processing, and discloses an intelligent document fragmentation method and system based on structural semantic fusion. The method comprises the following steps: performing structured analysis processing on a target document to obtain a uniform structure node tree; performing semantic unit boundary identification on the unified structure node tree to obtain a target segmentation point; performing constraint fragmentation optimization on the target document based on the target segmentation point to obtain a structured fragment adaptive to the large model, the constraint fragmentation optimization including semantic quality constraint and length adaptation constraint, the semantic quality constraint being configured to standardize semantic logic association and context cohesion relationship of the fragment, and the length adaptation constraint being configured to standardize semantic logic association and context cohesion relationship of the fragment; the length adaptation constraint is configured to match an input length upper limit and a processing capability threshold of the large model. According to the method, the logic integrity of the fragment content is guaranteed; and the understanding efficiency and the processing precision of the large model on the fragment content are effectively improved, and a high-quality structured input basis is provided for subsequent downstream tasks.

The invention discloses an intelligent extraction method and equipment for structural data of a scanned copy and a medium, and relates to the cross technical field of computer vision and intelligent document processing. The method comprises the following steps: acquiring a to-be-processed scanned copy image, and performing joint feature extraction on the scanned copy image based on a preset multi-modal neural network model to generate intermediate feature representation; constructing an extensible structured index template library containing field rules, value domain constraints and cross-field verification logic; based on an attention mechanism, performing intelligent semantic matching and mapping on the intermediate feature representation and a target field in an extensible structured index template library to output preliminary structured data; based on a verification rule preset in an extensible structured index template library, executing automatic logic verification and conflict resolution on the preliminary structured data to generate standard structured data conforming to business specifications; and carrying out serialization output on the standard structured data according to a predetermined format.

The invention provides an intelligent document understanding method and system combining natural language processing and deep learning, and relates to the technical field of natural language processing and information management. Fusing the semantic pre-annotation result and the original text feature of the document, inputting the fused semantic pre-annotation result and the original text feature of the document into a cross-modal semantic enhancement model to obtain a document enhanced semantic representation, performing bidirectional semantic interaction with a dynamic business knowledge network based on the document enhanced semantic representation, generating an associated interaction result, and constructing a structured semantic asset; finally, the structured semantic assets are input into an intelligent document application engine, semantic feedback data are collected to optimize model parameters, document understanding accuracy and practicability can be improved, and diversified business requirements are met.

The invention relates to the technical field of document intelligent review, in particular to a multi-mode digital publishing intelligent review system and method based on a large model, and the system comprises a sample construction module, a multi-mode analysis module, a structure recognition module, a term verification module and an annotation output module. According to the method, a model configuration data set is generated by analyzing a subject-object combination relationship in a text and jointly screening part-of-speech, sentence pattern and semantic structure, the accuracy of semantic recognition and structure judgment is improved, and local features of semantic offset and image-text expression separation are recognized by combining a comparison relationship between an image action path and a text behavior object; a paragraph logic structure and a theme connection mode are analyzed based on semantic offset information, the problems of content dislocation and theme jump between paragraphs are effectively revealed, matching change tracks and part-of-speech continuation fluctuation in term cross-paragraph contexts are recognized, label overlapping and coverage redundancy conditions are analyzed from sentence group hierarchy, and the term cross-paragraph contexts are obtained. And forming a label combination suggestion and constructing an annotation structure record.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent file editing and auditing system and method and a storage medium, and the method comprises the following steps: receiving a purchase file; vectorization processing is carried out on the purchase file; newly establishing a purchase document; relevant data are recalled from the vectorized data according to target information required by the chapters, structured conversion is carried out, and the data are added into the corresponding chapters, so that compilation of the purchase document is completed; the compiled purchase document is preprocessed; the method comprises the following steps: reading review requirements from a preset document review point library, extracting related review contents from vectorized data of a purchase document, performing review judgment on the related review contents according to the review requirements by utilizing a large language model, and outputting a review judgment result, thereby completing intelligent review of the purchase document. According to the method, manual content retrieval, extraction, integration, filling and auditing work can be replaced, the workload and time are greatly saved, and meanwhile the accuracy and speed of information retrieval are obviously improved compared with manual information retrieval.

The invention discloses an intelligent document compliance auditing system and method based on multi-modal deep learning, and belongs to the technical field of information processing, and the system comprises a data recognition module which is used for recognizing multi-modal data in a target document, the multi-modal data comprises a text, an image, a table and a scanning copy, carrying out information integration on the multi-modal data, and carrying out information processing on the integrated information; the information integration comprises the step of carrying out association analysis on data in images and tables and character description in texts; the compliance auditing module responds to information integration and is used for conducting compliance auditing on the target document after information integration, and compliance auditing comprises illegal content and format errors. According to the method, efficient and accurate compliance auditing is realized by identifying and integrating the multi-modal data in the document, illegal contents and format errors can be accurately identified, the risk level is evaluated, targeted countermeasures are formulated, and the auditing efficiency and accuracy are improved.

The invention provides a document editing method based on a natural language instruction, client equipment and a system, relates to the technical field of artificial intelligence, in particular to the technical fields of natural language processing, large language models, model cooperative calculation and the like, and can be applied to scenes such as intelligent document editing, automatic content generation and human-computer interaction interfaces. According to the specific implementation scheme, a natural language instruction input by a user is received; processing the natural language instruction based on the intention discrimination model to generate an instruction type discrimination result; the instruction type comprises selected-area-free text modification, selected-area text modification, revocation operation, redo operation and refusal operation; according to the instruction type discrimination result, calling a corresponding processing module to execute document editing operation; wherein the intention discrimination model is a pre-trained small-scale neural network model. According to the scheme, accurate, efficient and low-resource-consumption intelligent document editing is realized through intention judgment, selection judgment and cooperation of large and small model capabilities.

This invention relates to the field of image recognition technology, and more particularly to a general-purpose structured document recognition method and system based on deep learning. The method includes: acquiring document image information and preprocessing the acquired images; inputting a standardized document image into a text detection network to locate text instances in the image and saving the location information to a file; extracting text images from the document image based on the detected text instance location coordinates and inputting them into a text recognition network for recognition, saving the recognition result after the location information; using a key text extraction network to classify the text entities in the recognition result, removing non-key types, and then saving the classification result after the recognition result; and performing structured processing on the key text extraction result and displaying it. This invention can extract key text information from complex document images, enabling intelligent document reading, and is applicable to various types of documents.

The invention discloses an intelligent document generation system and method based on generative artificial intelligence, and relates to the technical field of generative artificial intelligence and document processing, the system comprises an intelligent document processing service layer and a data resource layer, when the intelligent document processing service layer executes a task, required data is read from the data resource layer; the intelligent document processing service layer comprises a content generation module, a verification module and a coordination management module, and the content generation module is used for generating preliminary content; the verification module is used for verifying the preliminary content, generating structured feedback information when the verification is not passed, and driving the content generation module to perform iterative correction according to the feedback information; and the coordination management module is used for receiving tasks and performing overall scheduling of the system. According to the method, the AI system is endowed with self-checking and self-correcting capabilities, the problems of reliability and structure maintenance of content generation can be systematically solved, and the quality and credibility of automatic document generation are remarkably improved.

The invention relates to the technical field of intelligent retrieval, in particular to an intelligent document question answering and knowledge base retrieval method and system based on a large model. The method comprises the following steps: acquiring an initial query and performing semantic analysis, generating pseudo-fact fragments containing positive, negative and neutral contexts by using a first large language model in combination with a domain entity word list, and splicing the pseudo-fact fragments into a fused imaginary document; respectively vectorizing the query document and the imaginary document, carrying out weighted fusion according to a query-centrality coefficient to generate a retrieval vector, and retrieving in a knowledge base to obtain a candidate document set; generating a draft answer by using a second large language model, performing traceability comparison on each statement, and marking non-traceability or fact conflict items; and taking the draft and the mark as a correction instruction to be input into the model for correction, and generating a final answer. According to the scheme, the retrieval precision can be improved through multi-view document fusion, illusion is restrained through a closed-loop verification correction mechanism, and evidence reliability is ensured.

An intelligent and multi-layered approach that uses real-time analysis to identify and confirm the authenticity and inauthenticity of bulk digital documents. Support Vector Machine (SVM) learning is implemented to perform significant attribute validations, such as barcode validation, image-specific validations, and signature validations. An SVM classifier is implemented to compare, analyze, predict the accuracy of the document (i.e., quantify the certainty of authenticity) and decision the documents as either valid / authentic or invalid / tampered-state. Neuro-symbolic Artificial Intelligence (AI) technology is subsequently implemented to confirm or deny the authenticity decision resulting from the SVM classifier.

Document parsers, document parsing methods, and products are provided that use Visual Large Language models and / or eForms to generate structural representations to train artificial intelligence used in intelligent document processing. These structural representations are enhanced with the Visual Large Language models with geometry data from the documents and the results are correlated with a training sample. The data set is then curated for errors and omissions and reintegrated into the initial structure of the form. Auto-generated synthetic documents can be used in certain embodiments. Standardized outputs such as eForms and from an Electronic Document Interchange can be used in certain embodiments to enhance efficiency and synchronization of intelligent document processing. A multi-modal transformer-based machine learning model is built that can then be used to create an output in intelligent document processing.

The invention belongs to the technical field of intelligent document processing, and particularly relates to a large model information extraction and structure restoration system for a long text document. The system comprises an extraction point defining and text preprocessing module which is used for carrying out deep preprocessing and analysis on an input unstructured document, extracting text and coordinate information and identifying and protecting a special structure; meanwhile, an intelligent dynamic blocking strategy based on semantic boundaries is adopted, a dynamic overlapping mechanism is combined, and an input document is divided into text blocks keeping semantic integrity; the high-concurrency processing and scheduling control module is used for processing the text blocks in batches, calling a large language model to carry out distributed reasoning and outputting a dispersion result; and the extraction reasoning and result fusion module is used for performing semantic deduplication, entity alignment and confidence fusion on the dispersion result returned by the large language model to generate globally consistent structured output. The method has the advantages of being high in precision, high in speed, controllable in cost and high in robustness.

A system and method for secure and efficient automated workflows includes two complementary components. A digest embedding system verifies the integrity of workflow event sequences using a rolling SHA-256 digest salted with microsecond-precision timestamps. A template-caching extractor adaptively processes heterogeneous electronic documents. The digest system enables decentralized verification without querying centralized audit logs. The extractor uses a layout hash derived from document structure to route documents through either a low-latency, rule-based extraction path or a fallback artificial intelligence model path. New templates are generated for previously unseen layouts exceeding a confidence threshold. The disclosed methods improve latency, resource utilization, energy utilization and scalability in sectors including finance, healthcare, and logistics, offering advantages over existing prior art in terms of integration, specific mechanisms for timestamp salting, hardware security module utilization for workflow events, layout-based template caching, and adaptive learning.

The invention provides a document generation method and device, an intelligent agent, equipment, a medium and a product, relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, natural language processing and large models, and can be applied to the field of intelligent documents. According to the specific implementation scheme, in response to a received document generation request, document description information carried in the document generation request is input into a large model, document outline information of an output target document is obtained, and the document outline information comprises chapter description information used for describing a target chapter; determining the chapter content type of the target chapter based on the chapter description information; determining a target tool from the plurality of candidate tools based on the chapter content type and the respective tool attribute information of the plurality of candidate tools; based on the tool calling information of the target tool, calling the target tool to generate chapter content of the target chapter; and generating a target document based on the chapter content of the target chapter.

The invention provides a PowerPoint data processing method and device, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of intelligent documents, webpage development, deep learning and the like. The method comprises the steps of obtaining presentation data based on a preset structured format, wherein the presentation data comprises respective structured description information of a plurality of presentation elements; converting the PowerPoint data into a page description format supported by a webpage editing environment to obtain initial page description data; drawing a plurality of presentation file elements in a webpage editing environment; in response to the detected target operation on the multiple presentation file elements, obtaining target page description data of the webpage editing environment after the target operation is described; comparing the target page description data with the initial page description data to determine an editing intention; based on the editing intention, determining a change instruction which conforms to a preset structured format and aims at the presentation file data; and updating the PowerPoint data by using the change instruction.

The invention discloses a PDF document-oriented formula structured analysis and semantic understanding method and system, and relates to the technical field of document intelligent processing, and the method comprises the following steps: sequentially carrying out rendering, normalization and tilt correction processing on a pre-obtained PDF document to obtain a preprocessed document image; based on a context information fusion mechanism and in combination with a preset formula region detection model, detecting the preprocessed document image to obtain a formula image; and based on the attention mechanism, utilizing a preset formula analysis model to perform structured analysis on the formula image to obtain formula two-dimensional structure information. According to the method, the deep learning model is adopted for formula detection and analysis, high precision and high robustness are achieved, formula structuring is achieved, the information utilization efficiency is improved, and the labor cost is reduced.

A system and method for secure and efficient automated workflows includes two complementary components. A digest embedding system verifies the integrity of workflow event sequences using a rolling SHA-256 digest salted with microsecond-precision timestamps. A template-caching extractor adaptively processes heterogeneous electronic documents. The digest system enables decentralized verification without querying centralized audit logs. The extractor uses a layout hash derived from document structure to route documents through either a low-latency, rule-based extraction path or a fallback artificial intelligence model path. New templates are generated for previously unseen layouts exceeding a confidence threshold. The disclosed methods improve latency, resource utilization, energy utilization and scalability in sectors including finance, healthcare, and logistics, offering advantages over existing prior art in terms of integration, specific mechanisms for timestamp salting, hardware security module utilization for workflow events, layout-based template caching, and adaptive learning.

The invention discloses a method for generating a controlled file template based on deep learning, and relates to the technical field of intelligent document processing, and the method comprises the steps: collecting document data, obtaining a multi-modal data set through preprocessing, and generating a domain knowledge graph; based on an optimization learning strategy, optimizing the template generation and content evaluation task through a reinforcement learning algorithm to obtain a file template; performing format specification evaluation and content rationality evaluation on the file template by adopting a self-supervised learning method, and adjusting a template layout structure and content terms and details to generate an optimized file template; according to the real-time feedback data, feedback learning and template adjustment are conducted on the optimized file template through an incremental learning method, and a controlled file template is generated. The template quality is improved, manual intervention is reduced, and a basis is provided for continuous optimization.

This invention provides an intelligent document generation system and its control method, characterized in that: the system includes: a dynamic template engine module; an intelligent content generation module; an adaptive format optimization module; and an automatic detection module; the dynamic template engine module determines logical branches through XML tags in a dynamic template language, which can overcome the limitations of traditional placeholder replacement, decouple document structure and data, and adapt different logical branches according to different data, effectively improving the adaptability of data dimensions; the intelligent content generation module can combine a pre-trained model to generate coherent natural language text descriptions, and dynamically render charts on this basis, embedding icons into the text in the form of vector graphics, realizing the collaborative generation of text and charts, and reducing the cost of manual writing, drawing and typesetting by collaboratively generating text and charts that conform to the context.

The invention discloses an intelligent document approval method and system and terminal equipment, and belongs to the technical field of intelligent auditing, and the method comprises the steps: analyzing the layout structure of a to-be-approved document, and extracting a plurality of document information; according to the document information, performing process identification and approval classification on a to-be-approved document, and determining an approval classification result of the to-be-approved document; wherein the approval classification result comprises a business process and an approval decision; and transferring a to-be-approved document to an approver according to the business process, and monitoring an approval process of the approver based on the approval decision to obtain an approval result. Therefore, by implementing the method, the problem of low document approval efficiency in the prior art can be solved.