78 results about "Structured content" patented technology

The invention provides a regional wind field multi-source heterogeneous work order data fast structuring system fused with a large language model, which comprises a plurality of modules, and is characterized in that a data input module receives various work order images; the OCR layout recognition module comprehensively recognizes the data, extracts key information, determines a spatial position and outputs text fragments with coordinates and layout anchor point information, and the rule field extraction module recognizes format category fields from the text fragments and calculates rule matching confidence; the semantic field recognition module recognizes descriptive fields from unstructured content and outputs related confidence information, the fusion decision module calculates final confidence of field candidates according to a predetermined function and judges output or complementation according to a threshold value, and the data consistency verification module is connected with an asset database to verify key information. The data complementation module generates complementation candidates and confidence coefficients, the man-machine collaborative closed-loop module pushes suggestions and receives feedback, and the result output module outputs standardized data. According to the method, the multi-source heterogeneous work order data can be effectively processed.

The invention discloses a cross-platform content generation and distribution method based on a multi-modal AI, and belongs to the technical field of cross-platform content generation and distribution, and the method comprises the steps: carrying out the content analysis and feature extraction of an original material based on a multi-modal AI model, and generating a structured content label and a semantic vector. By combining a vector matching degree formula of target portrait features and platform features, an adaptation strategy is dynamically generated, it is ensured that content not only conforms to platform rules, but also can accurately reach a target group, the conversion rate and user viscosity are finally improved, visual, text and semantic vectors are aligned through a Transform multi-modal fusion model, a cross-modal joint representation vector is generated, and the user experience is improved. And in combination with an adversarial generative network, differentiated variants of the same theme are generated in batches, and a content diversity score mechanism ensures that generated contents are balanced between creativity and compliance.

The invention relates to the technical field of auxiliary writing systems, in particular to a structured auxiliary writing system based on multi-agent collaboration. The system specifically comprises a writing task demand analysis and modeling module, a multi-agent system architecture construction module, a writing task disassembly and subtask distribution module, a multi-source knowledge retrieval and integration module, a structured content generation module, a format specification and quality evaluation module, a logic verification and content adjustment module and a user feedback iteration and model optimization module. The writing task demand analysis and modeling module firstly performs surface analysis on a writing task of a user, determines the type, theme, target audience, core demand and quality requirement of the writing task, and converts a fuzzy writing demand into a quantifiable and executable structured task model; according to the invention, the limitation of a traditional writing mode and an existing single-function auxiliary tool is solved.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a search and chat generative interfaces, which may be used to produce generative answers, identify relevant content, and take actions within a respective content collaboration platform.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention provides a standard draft auditing and correcting method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a to-be-audited standard draft, and preprocessing the to-be-audited standard draft to obtain structured content data and structured format data of the to-be-audited standard draft; inputting the structured content data into a pre-trained content correction model to obtain a standard draft marked with to-be-corrected content; wherein the content correction model is obtained through reinforcement learning training based on a large language model; comparing the structured format data with a pre-constructed format knowledge database to obtain a standard draft marked with a to-be-corrected format problem; and re-checking the to-be-corrected content and the to-be-corrected format problem, carrying out content correction on the to-be-corrected content after re-checking, and carrying out format correction on the to-be-corrected format problem to obtain a correct standard draft. The auditing efficiency and accuracy of the standard draft are improved.

A system and method are disclosed for generating conversational content from human-readable documents. The method includes receiving a document comprising unstructured or semi-structured content and extracting linguistic and layout features using a language model and layout analysis techniques. The document is segmented into atomic content blocks representing discrete semantic units. For at least one content block, a natural language question is generated using a neural model, and a corresponding answer is extracted or synthesized. An optional rephrasing step modifies the surface form of the question or answer while preserving semantic meaning. Each question-answer pair is reviewed using automated or human-in-the-loop mechanisms for accuracy and alignment. Approved content is stored in a structured repository along with metadata supporting traceability and deployment. The system supports enterprise-scale generation of high-quality conversational data for downstream applications such as chatbots, virtual assistants, and retrieval-based AI systems.

The application discloses a kind of extraction methods, device, equipment, medium and product of file structured information, it is related to data processing technical field, comprising: determining the file content type of to-be-processed file;In the case where it is determined that file content type is image content file, to-be-processed file is carried out text recognition, and the text area coordinates of to-be-processed text contained in to-be-processed file and to-be-processed text in to-be-processed file are determined;To-be-processed text is carried out structured content entity identification, and the content entity coordinates of each structured content entity in text area coordinates respectively corresponding to structured content entity contained in to-be-processed text are determined;According to each content entity coordinates, the content entity relationship data between each structured content entity is constructed, and according to content entity relationship data, to-be-processed text is carried out structured information extraction, and the target structured information contained in to-be-processed file is obtained.The application can improve the accuracy and integrity of structured information extraction.

The invention provides a vehicle repair file processing method and related equipment, and the method comprises the steps: receiving a vehicle repair file uploaded by a user, and recognizing the format of the vehicle repair file; converting the automobile repair file into a Markdown format by using a conversion method matched with the format of the automobile repair file to obtain a target automobile repair file; inputting the target automobile repair file into a preset language large model to obtain file content extracted by the language large model; and exporting the file content according to a preset business data format. In the scheme, the automobile repair file format is recognized and uniformly converted into the Markdown format, the problem of complex format analysis of multi-column typesetting, table nesting and image-text mixing is solved, the content is standardized, the standardized file is input into a large language model, the natural language understanding ability of the standardized file is utilized, professional terms are precisely recognized, and the structured content is extracted; the extracted content is exported according to the preset service format, so that unified management of the automobile repair file content is realized, and the data utilization rate is improved.

A system is disclosed for generating context-aware, domain-specific responses using a pre-trained language model in combination with a semantic search engine and specialized processing modules. The system includes a classification model to identify user intent, an extraction model to determine parameters, and a plurality of parameter functions that generate outputs such as sentiment classifications, semantically similar content, and dynamically constructed prompts. By integrating retrieved structured and unstructured content into tailored prompts, the system provides accurate, domain-specific query responses without requiring retraining of the underlying language model. The architecture supports efficient and scalable workflow management in dynamic environments while reducing computational overhead compared to conventional approaches.

The invention provides a retrieval recall method and device based on table analysis, equipment and a storage medium, and is applied to the fields of finance and medical treatment. The method provided by the invention comprises the following steps: obtaining a table to be analyzed, if the layout structure of the table to be analyzed comprises unstructured content, segmenting the unstructured content of the table to obtain a subdivided table, and analyzing the subdivided table to obtain a sliced table; enabling the large language model to generate first characteristic information corresponding to the slice table according to the slice table, wherein the first characteristic information comprises an abstract, a question and answer pair and label information; obtaining a user question, and generating second characteristic information corresponding to the user question according to the user question; a retrieval resource object is determined according to the similarity between the second characteristic information corresponding to the user question and the first characteristic information corresponding to the slice table, a retrieval recall result is generated according to the retrieval resource object, and the retrieval resource object comprises the slice table. According to the method, the accuracy of the retrieval recall result can be improved.

The application provides a self-evolution presentation document generation method and system based on multi-modal and knowledge graph, wherein the self-evolution presentation document generation method comprises the following steps: constructing an enterprise-level picture meta-database containing a vector index; parsing a source document to obtain structured content and generating a presentation document outline; searching for matching candidate pictures in the enterprise-level picture meta-database based on the target page core content in the outline; querying a design decision knowledge graph to generate layout and color matching planning; assembling a presentation document according to the planning and synchronously generating design decision metadata; driving knowledge graph updating by displaying the design decision metadata and obtaining user modification feedback on the design; and the self-evolution presentation document generation system comprises functional modules for realizing the above method. The application solves the technical problems of poor picture quality, inaccurate image-text matching, rigid design, high copyright risk and difficulty in continuous learning and evolution from use in the existing automatic presentation document generation technology.

A method includes accessing a structured content item from a first database and event data from a second database, the event data including sets of event attributes in a multi-dimensional namespace and associated with a respective point in time; determining a relevancy profile characterizing a metric of relevancy of the structured content item over a respective time interval, the metric of relevancy including a distance in the multi-dimensional namespace between attributes associated with the structured content and the sets of event attributes; generating, using the relevancy profile, second digital content including a subset of the structured content item; and providing the second digital content for rendering on a device. Related apparatus, systems, techniques and articles are also described.

The application relates to the technical field of electronic bidding systems, in particular to an electronic bidding system and method based on XML structured data, which comprises the following units: a structured content division unit which divides bidding content information into multiple segments and converts the segments into corresponding XML segments; a data binding unit which establishes a mapping relationship between structured label variables and associated data in a system database or user input project information; a template storage unit which combines the screened XML segments into complete XML bidding templates and stores the templates according to industry categories; a bidding file generation unit which calls the bidding templates matched with the user input project information, automatically fills the associated data into the label variables, and generates complete bidding XML files; a bidding file generation unit which classifies the project data filled by bidders according to requirements and generates bidding XML file data; and an evaluation assisting unit which extracts the project data bound by the structured label variables and generates a horizontal comparison analysis table.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention provides a textbook data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an original textbook data file; extracting a data type of the original textbook data file, and analyzing the original textbook data file according to the data type to obtain structured content data; inputting the structured content data and the corresponding data type into a preset multi-data type hybrid model for processing, and obtaining processed result data and the data type corresponding to the structured content data; wherein the multi-modal data set is used for generating a multi-modal sample set for training the corresponding wisdom education large model according to the training demand of the wisdom education large model. According to the method, the efficiency and accuracy of constructing the multi-modal data set can be improved.

The invention discloses a title-level semantic partitioning method and device for multi-document batch processing, and the method comprises the steps: obtaining a plurality of to-be-processed input documents, and adding the plurality of input documents into a document processing task queue; allocating quotas for the document visual identification tasks in the document processing task queue to obtain page identification texts and structured contents of the documents; after all the pages of the multiple input documents are recognized, target outline titles are extracted from all the documents on the basis of page recognition texts; determining a corresponding page position of the target outline title in the document according to the page identification text and the structured content of each document; and on the basis of the corresponding page position of the target outline title in the document, executing cross-page segmentation according to the title boundary on the page recognition text of the corresponding document to generate a plurality of title-level semantic blocks capable of keeping the context continuity of the content. Therefore, a processing link from page recognition to structure understanding to semantic partitioning is realized.

The invention relates to a document style processing method and device, electronic equipment and a storage medium. The method comprises the steps that a plurality of source documents of the same content type are acquired; document structure analysis and style extraction are conducted on each source document, at least one standard style protocol is generated, and the standard style protocols comprise the mapping relation between document structure labels and style elements; in response to a style selection instruction, determining a target style protocol from the at least one standard style protocol; and generating a structured content with a document structure label according to the target style protocol, and applying a corresponding style element to content information in the structured content according to the target style protocol to generate a target document with a style. Therefore, the document style rule can be automatically summarized, the problem of non-uniform styles is solved, style processing automation and intelligence are achieved, efficiency is improved, and repeated work of similar document style adjustment is reduced.

The invention discloses an MCP data acquisition system and method for autonomously arranging and self-correcting calling of a target system interface API based on a large language model, and belongs to the technical field of enterprise information system integration and intelligent data processing. The method comprises the following steps of: firstly, automatically acquiring an API (Application Program Interface) document, interface parameters and authentication requirements of a target system, performing semantic analysis on structured and unstructured contents in the document, and extracting interface fields, parameter types, response structures and error code information; then, based on a document analysis result and actual interface calling feedback, API semantic inversion and dependency relation recognition are executed, and the field purpose, the parameter input and output relation, a paging mechanism and an interface calling link are automatically deduced. According to the method, the heterogeneous API interface document can be automatically analyzed, the calling chain can be automatically generated, self-error correction and self-adaptive optimization can be realized in the calling process, the API docking cost can be greatly reduced, and the stability and the automation degree of interface calling are improved. Meanwhile, multi-source data from an energy system, a logistics system and a supply chain system can be subjected to semantic recognition and automatic mapping, the requirements for data collection and standardization in the first range, the second range and the third range of carbon emission are met, and end-to-end automatic integration and processing of carbon emission data are achieved. The method has high universality, strong robustness and continuous evolution capability, and is suitable for various business scenes such as enterprise carbon accounting, cross-system intelligent data integration, system API management and automatic operation and maintenance.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes generative interface panel used to access a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

This invention discloses a method, device, electronic device, and storage medium for generating medical science popularization content. The method includes: parsing medical research content to be popularized to obtain structured content data; identifying medical terms in the structured content data to obtain target category labels for multiple medical terms; acquiring hot topic public opinion data and style content based on the target category labels; and inputting the structured content data, hot topic public opinion data, and style content into a pre-trained science popularization content generation model to obtain medical science popularization content. This invention can guide the science popularization content generation model based on hot topic public opinion and the desired style, prompting it to generate medical science popularization content with a hot topic public opinion orientation and a specified style based on the structured content data. The generated medical science popularization content is more accessible, approachable, and humanized, improving the reading experience and better catering to readers' interests, thus enhancing the efficiency and quality of medical science popularization.

A method includes accessing a structured content item from a first database and event data from a second database, the event data including sets of event attributes in a multi-dimensional namespace and associated with a respective point in time; determining a relevancy profile characterizing a metric of relevancy of the structured content item over a respective time interval, the metric of relevancy including a distance in the multi-dimensional namespace between attributes associated with the structured content and the sets of event attributes; generating, using the relevancy profile, second digital content including a subset of the structured content item; and providing the second digital content for rendering on a device. Related apparatus, systems, techniques and articles are also described.

The invention discloses a method for synchronizing and publishing works of multiple types of social media accounts, and relates to the technical field of social media operation, comprising the following steps: step 1, content structuring processing: analyzing the content of works created by a user; extracting text, picture, audio and video elements in the data, and storing the text, picture, audio and video elements according to a unified structured format; step 2, cross-platform content adaptation: according to the type and format requirements of the target social media platform, performing automatic adaptation conversion on the structured content; step 3, publishing strategy optimization: determining optimal publishing time and frequency based on content recommendation algorithm characteristics of the target platform and user active time data; 4, multi-platform synchronous publishing: through calling an open API interface of each social media platform, publishing the adapted content to a corresponding account according to an optimized strategy; and 5, synchronization and update management: maintaining the association relationship and state information of the content published by each platform.

This invention relates to a method, apparatus, device, and medium for acquiring electronic documents based on virtual printing, and pertains to the field of digital data information processing technology. The method includes: a client obtaining graphic and textual output content from a business system via a virtual printer; matching this content with a recognition template; marking textual content within the variable domain range recognized by the recognition template when a match is successful; performing OCR recognition on non-textual content and caching the result as variable content; correcting the recognition result according to the recognition template; organizing the variable content into hierarchical data nodes according to the recognition template; querying a local data dictionary to translate the corresponding variable content into corresponding data codes; converting the graphic and textual output content into a layout document; using the data nodes and data codes as indexes or data attachment fragments of the layout document; extracting information from the marked structured content and / or data codes according to the recognition template as metadata for the layout document; and generating an output electronic document.

The invention relates to a compression method and device for cue words of a large language model system, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring at least one structured content block and respective placeholders of the at least one structured content block in a system cue word of a large language model; constructing at least one structured content block and a mapping table of placeholders corresponding to the at least one structured content block; replacing at least one structured content block in the system cue word with a corresponding placeholder to obtain a first cue word; compressing the first cue word to obtain a second cue word; and on the basis of the mapping table, replacing placeholders in the second cue words with respective corresponding structured content blocks to obtain compressed cue words of the system cue words.

The embodiment of the invention provides an information processing method and device based on media content, a readable storage medium and a program product, and the method comprises the steps: displaying a first media content which is any media content in an information stream composed of a plurality of media contents; in response to the received information acquisition request, displaying the first structured content; the first structured content is obtained by performing content structured processing on the first media content. And switching from consumer content browsing to active information acquisition is realized. According to the scheme, the media content becomes an entry for active information acquisition, a user can actively explore information through the media content, the explored information is presented in a structured mode, and compared with linear description of the media content, the structured content in the scheme is easier to understand and memorize. And convenience is provided for the user to deeply acquire the information of the media content.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The application provides an intelligent extraction method, device and equipment of Web data and a storage medium. Through a task queue mechanism, mass URLs are converted into manageable processing tasks, realizing the orderly scheduling of large-scale data. A URL direct mode or content preprocessing mode is selected to ensure that Web content can be stably obtained under different network environments, and then the semantic understanding ability of a large language model is called to convert unstructured Web page content into structured data according to a predefined Schema, and field mapping and structure conversion are performed at the database level through a database aggregation pipeline. Finally, unqualified data is automatically identified and corrected through a quality scoring mechanism, and the re-extraction process is automatically triggered for data that does not meet the quality standards, forming a closed-loop quality guarantee system. API rate control in the whole process ensures the stable operation and cost control of the system, and finally realizes the full-automatic conversion from unstructured Web content to high-quality structured multilingual data.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention discloses a PDF (Portable Document Format) document generation method and system based on layered architecture and structured content. According to the method, a PDFWriter object is created through a layered architecture, and the PDFWriter object is driven to create a page based on semantic structure element data; according to the visual and structural attribute change content of the elements in the page, generating a PDF document; and verifying and adjusting the layout through the form control, setting the fine-grained security authority, and adjusting the processing process. Therefore, the generation efficiency is remarkably improved, the memory occupation is reduced, and the compliance and accessibility of the WCAG are enhanced. The layered architecture reduces the code maintenance amount and the cross-platform cost, rich elements and controls support improve the comprehensiveness of functions, and finally high-quality, high-efficiency and high-safety PDF document generation is achieved.