350 results about "Content extraction" patented technology

Embodiments described herein relate to systems and methods for automatically generating content for a generative answer interface of a collaboration platform. The system receives a natural language user input identifying corresponding blocks of text or snippets using a content extraction service. A prompt is generated using the blocks of text and is used to obtain a generative response. The generative response and links to corresponding content are displayed in the generative answer interface and can be inserted into content of the collaboration platform. 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 relates to a document processing method and system based on text content extraction. The method comprises the steps that an original document containing text, image and format information is received, the encoding format of the document is automatically detected, character set conversion is executed, and hierarchical indexes including page numbers, paragraphs and tables are established for an unstructured document; the method comprises the following steps: synchronously processing text content and visual layout through a pre-trained visual-language model, extracting word-level and sentence-level semantic features by a text stream embedding layer, analyzing spatial distribution features of document elements by a visual encoder, and fusing text and visual features through a cross-modal attention mechanism; and loading the domain knowledge graph matched with the document type, and executing entity linking to associate the text mentions to the knowledge nodes. According to the document processing method and system based on text content extraction, through the synergistic effect of vision-text joint coding and knowledge enhancement, the accuracy of financial contract key clause recognition tasks is improved, the error rate is lower than that of industry benchmark products, and the semantic understanding precision is remarkably improved.

The invention relates to the technical field of knowledge maps, in particular to a legal knowledge map automatic construction system and method based on machine learning, and the system comprises a metadata extraction module which reads text data from a legal document, carries out the content analysis through a machine learning model, and generates a legal entity list; carrying out attribute association analysis on the legal entity list, and establishing a preliminary metadata index; according to the method, through automatic analysis and content extraction of legal document text data, recognition and efficient induction of legal entities are achieved in combination with a machine learning model, so that a legal entity list is constructed, and a legal knowledge set which is clear in structure and convenient to retrieve is formed through attribute association analysis and metadata indexing. Meanwhile, according to intelligent evaluation of the importance of the legal document content, differential sorting and storage of the documents are achieved, the archiving structure is optimized, and the convenience and response speed of data calling are improved.

The invention provides a report generation method and system based on a multi-agent architecture, and the method comprises the steps: firstly receiving and analyzing a report generation instruction, obtaining a theme demand, a framework specification and an initial reference material, then starting a multi-agent cooperation framework, generating an agent task distribution table, and generating an agent task distribution table; and the resource retrieval agent executes network resource directional retrieval according to the task allocation table to generate an associated resource set, and the content extraction agent performs structured conversion on the associated resource set and the initial reference material to obtain a structured content unit with chapter codes. The method comprises the following steps: performing module classification and logic series connection on a structured content unit by a report integration agent in combination with a framework specification to generate a report first draft, and finally performing content verification and optimization on the report first draft by a multi-agent collaborative framework to generate a final report text conforming to the framework specification, thereby realizing automation, intellectualization and high efficiency of report generation. And report quality is improved.

The invention belongs to the technical field of document content extraction, and particularly discloses a document content extraction method and system based on multimodal model collaboration, a terminal and a medium. Comprising the following steps: identifying the type of an input to-be-processed document, and judging the document type; on the basis of the type identification result, calling a multi-modal model to analyze the document content, and outputting space coordinates, visual features and semantic features of document elements; generating a content sequence according with a reading habit through a semantic sequence reconstruction algorithm; paragraph boundary detection, paragraph recombination and semantic association modeling of charts and texts are completed based on the multilayer attention network and the graph neural network; grammar error correction, format optimization and title hierarchy generation are carried out by using a large language model and a hierarchical classification network; and converting the identification result into a structured output file. According to the method, the processing requirements of different types of documents can be considered, and high-precision analysis and efficient output are realized under the scenes of complex layouts, multiple languages and formula tables.

The invention discloses an enterprise data link management and value management method and system, and the method comprises the steps: carrying out the real-time capturing of multi-source heterogeneous data of all business systems in an enterprise through a distributed data collection engine, and recognizing the types of original data in different formats through a preset data source adapter; if structured data is detected, a relational database connector is adopted for extraction, and if the structured data is recognized as unstructured data, a document analysis module is started for content extraction, and an initial data set containing metadata tags is obtained; performing format conversion and field mapping on the initial data set according to a pre-established data standardization rule base, eliminating duplicate records and abnormal values through a data cleaning algorithm, performing automatic evaluation on a data quality grade by adopting a naive Bayes classifier, if a data quality score is lower than a preset threshold value, triggering a data recovery process, and if the data quality score is lower than the preset threshold value, performing data recovery. And standardized data meeting a unified standard is obtained. The normativity and value utilization efficiency of data management are effectively improved.

The invention provides a contract information processing method and electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: acquiring a to-be-processed contract document, and preprocessing the to-be-processed contract document to obtain a processed contract document; content extraction is conducted on the processed contract document, key performance information in the processed contract document is obtained, and the key performance information comprises multiple performance execution tasks and performance element information of the performance execution tasks; constructing a task dependency relationship graph according to the performance key information; according to the task dependency graph, generating a performance execution plan of the to-be-processed contract document; and performing visual display on the performance execution plan according to each item in the performance execution plan. According to the method and the device, the contract document is quickly and accurately analyzed, and the contract fulfillment task is effectively planned and monitored, so that the efficiency and the accuracy of executing the contract fulfillment task are greatly improved, and the requirement of continuously increasing business volume of an enterprise is met.

The invention provides a file uploading attack interception method based on semantic entropy enhancement, and aims at overcoming the defects of an existing file uploading security protection technology in the face of complex attacks. The method specifically comprises the steps that S1, file format analysis and content extraction are conducted, hidden scripts are mined through nested content recognition, and intermediate representation is generated through grammar cleaning and coding specifications; s2, constructing an abstract syntax tree and semantic entropy calculation, tracking a pollution chain, analyzing a high-risk function, identifying a high-entropy character string, modeling and controlling flow complexity, and generating a semantic entropy vector; s3, dynamic scoring and decision making are carried out, and accurate judgment is carried out in combination with white list perception, feature comparison, multi-modal model scoring, adaptive threshold and sandbox observation; and S4, carrying out real-time interception and feature synchronization, blocking malicious file landing, generating an attack log and synchronizing an attack fingerprint. The method takes the semantic entropy vector as a core, breaks through the limitation of static features, remarkably improves the recognition rate of complex attacks, reduces missed judgment, and guarantees the safety of Web applications.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention belongs to the technical field of artificial intelligence, and discloses a knowledge extraction method and system based on semantic consistency evaluation and hybrid verifiable reward, and the method comprises the steps: constructing a training data set with evidence labeling; based on the training data set, reinforcement learning training is carried out on a pre-trained large language model, a group strategy optimization GRPO algorithm is adopted, and model output is evaluated by using a mixed reward function; and on the basis of an evaluation result of the mixed reward function, updating parameters of a large language model so as to generate structured knowledge which is correct in format, accurate in content and provided with verifiable evidence. According to the method, in reinforcement learning training, effective decoupling format, content and credibility evaluation is realized, and refined feedback is provided for the model; when the model outputs knowledge, traceable original text evidence is provided for the model, so that the credibility and the interpretability of the model are enhanced, the accuracy of outputting the JSON format by the model is improved, and the accuracy and the integrity of the model in the aspect of content extraction are enhanced.

The invention relates to a medical guide document analysis and content extraction system based on natural language processing and computer vision technology, the system comprises a layout detection model, a formula detection model, a table recognition model, a formula recognition model and an OCR (optical character recognition) model, the layout detection model is used for positioning different elements in a document; the formula detection model is used for positioning a formula in a document; and the table identification model is used for detecting table boundaries, segmenting table cells and analyzing a complex structure. The invention further provides a medical guide analysis and content extraction method applying the medical guide document analysis and content extraction system based on the natural language processing and computer vision technology. According to the invention, efficient analysis and accurate extraction of the medical guide document are realized. The system can accurately identify the text, table, formula and picture information in the document, improves the medical information processing efficiency, enables the acquisition of medical knowledge to be more convenient and efficient, and facilitates the improvement of the medical service quality and research efficiency.

The invention relates to the technical field of artificial intelligence, in particular to a reinforcement learning-based session content key point extraction method, which comprises the following steps of: preprocessing an input text sentence, constructing a composite feature vector, inputting a preprocessed session composite feature vector sequence into a Transform multi-head attention layer, and obtaining a key point of a session content; after exponential decay is applied to attention weights of historical sentences, emotional polarity and entity density are introduced as regulatory factors, attention weight calculation is optimized, and key statements are screened according to a threshold value and stored in a cache region; constructing a focus state vector and graph evolution, then establishing semantic edges and time sequence edges according to rules to form a graph, enhancing nodes through a graph convolutional network, and finally pooling active nodes to generate a vector as input of reinforcement learning; according to the method, through time decay and multi-head attention, time sequence association and semantic key points in dialogues are dynamically captured, and the timeliness of key information identification is enhanced.

The invention discloses a dynamic user portrait generation system and method based on an intention-to-problem model, relates to the technical field of dynamic user portrait generation, and is used for solving the problem that dynamically changing user requirements cannot be adapted. Semantic features are extracted in combination with current dialogue content and matched with label embedding vectors, target labels are recognized, question texts are generated, the system extracts text, voice and visual multi-modal features after dynamically scoring based on dialogue rounds and semantic consistency and judging question insertion opportunities and user responses, response vectors are generated through fusion, and the question insertion opportunities and the user responses are obtained. Constructing a semantic tag vector by combining knowledge graph reasoning, finishing adaptive scoring between user response and a property tag, triggering incremental updating of the property tag when a scoring value is higher than a threshold value, counting the number of updating times, executing weight reconstruction if the number of updating times exceeds a total reconstruction threshold value, and optimizing a tag weight by combining a time attenuation mechanism; and the user portrait accuracy is optimized.

The invention discloses a bill content extraction method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: receiving a to-be-extracted bill file and extraction elements uploaded by a user; preprocessing the to-be-extracted bill file according to the file type to obtain a to-be-extracted image-text; according to the extraction elements and the bill type template, generating adaptive cue words; and inputting the cue word and the to-be-extracted image-text into a preset visual language model to obtain bill content corresponding to the to-be-extracted bill file. According to the method, the to-be-extracted bill file is preprocessed according to the file type, so that the complex bill layout is simplified, and the to-be-extracted image-text is obtained; according to the method and the device, the extracted elements are extracted, then the flexibly defined cue words capable of being matched with the document features are generated according to the extracted elements and the bill type template, so that the situation that a complex bill file is difficult to adapt by depending on a fixed template can be avoided, finally, the adaptive bill content can be extracted based on the cue words, and the extraction accuracy of the bill content is improved.

In accordance with the present disclosure, one or more computing devices and / or methods are provided. In an example, a query may be received from a client device. In response to the query, a plurality of search results corresponding to a plurality of internet resources associated with the query may be generated. A plurality of content items may be generated based upon the plurality of search results. The plurality of content items may be generated using a plurality of content extraction tools. A language model may be used to generate a plurality of summaries of the plurality of content items. Summary scores of the plurality of summaries may be determined. A first summary may be selected from the plurality of summaries based upon the summary scores. In response to selecting the first summary, the first summary may be provided for display on the client device.

The invention discloses an intelligent routing and unified adaptation method for a large language model, and the method comprises the steps: defining all access details of the model through a declarative configuration file, and achieving the zero-code access of the large language model without writing any code for a newly-added model; a completely consistent calling interface is provided for an upstream application, the isomerism of all downstream large language models is shielded, and a unified request and response abstraction layer is constructed; through a strategy engine and a JSON path technology, complex streaming response including content thinking is precisely processed, and intelligent analysis and content extraction are carried out; dynamic configuration and intelligent strategy hot update are supported; intelligent routing of the model is realized, and an optimal large language model instance is dynamically selected; and meanwhile, enterprise-level governance capability is provided, governance functions such as fusing, degradation, current limiting and monitoring are integrated, and stability guarantee is provided for model calling. Therefore, the maintainability, the expandability and the user experience consistency of the system are comprehensively improved.

Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for deterministically identifying and extracting content from network resources, and generating focused content based therefrom for delivery to electronic users. The framework enables real-time customization of extraction tasks, which can cause tailored, accurate results that are contextually relevant and tied into the purpose of the extraction task. This data can then be compiled and / or leveraged to generate content campaigns that can target specific sets of users, geographies, time periods, trends and the like. The framework can leverage a large language model (LLM) to seamlessly extract relevant information from network resources, which enables the generation and execution of extraction requests that can be dynamically executed and updated, which can enable the framework to “drill-down” on contextual and / or topical aspects of categories of data.

The invention relates to the technical field of information extraction, in particular to an intelligent table filling method and system based on deep semantic analysis. The intelligent table filling method based on deep semantic analysis comprises the following steps: performing content extraction and format standardization on an input document to form a standard document text; a large language model is adopted, deep semantic analysis is executed on a standard document text, an entity of the document is recognized, and corresponding semantics are analyzed; analyzing semantics, position coordinates and logic partitions of fields in the target Excel template; establishing a corresponding semantic mapping relationship based on the semantic similarity between the entity of the document and the field of the target Excel template in the vector space; and based on the semantic mapping relationship, executing a hierarchical filling operation according to a semantic similarity threshold. According to the intelligent table filling and reporting method and system based on deep semantic analysis, the accuracy of automatic filling and reporting is improved through format verification, logic consistency check and integrity verification.

The invention relates to the field of large model security testing, and particularly provides a large model output content security testing method and device, and the method comprises the following steps: S1, preparing and managing a test set, a sensitive word library and a regular expression which are required by testing; s2, reading a test set, and obtaining a large model output result according to the test set and the large model interface information; s3, judging whether the output content of the large model is safe or not according to the sensitive lexicon and the regular expression; s4, extracting semantic risk features according to the output content of the large model by using the large model and the oriented Prompt, and automatically storing the semantic risk features after confidence verification; and S5, storing the information result of each request in a file. Compared with the prior art, the test time can be shortened, and the evaluation efficiency can be improved; and the security of the output content of the large model can be effectively evaluated by using a method for dynamically constructing the sensitive word bank by using the output result of the large model.

The invention provides a video content textualization method based on multi-modal fusion, which comprises the following steps of: 1, dynamically identifying effective modal information existing in a video, including subtitle information detection, audio information detection and key frame information sampling; step 2, carrying out subtitle extraction on the subtitle information by adopting an OCR (Optical Character Region) enhancement method based on regional clustering; generating a voice text for the audio information by adopting a multi-engine collaborative transcription and weight fusion strategy; generating a descriptive text for the key frame information; step 3, performing space-time alignment and semantic fusion on the subtitles, the voice text, the descriptive text and the video time axis; and feeding back and adjusting a strategy of key frame information sampling according to a fusion result, and forming self-adaptive feedback. According to the textualization method, multi-modal information can be adaptively fused, and video total elements are covered, so that the robustness and comprehensiveness of content extraction are improved.

The invention discloses a method, a system, a medium and equipment for extracting unified aggregated text content of all types of files, and relates to the technical field of file processing, the method comprises the following steps: system initialization and configuration, signature verification, extraction judgment, proxy forwarding, content extraction, and result storage and return. A single and stable service entry point (Endpoint) and a standardized API (Application Program Interface) specification are provided for all upper-layer businesses; through a standard S3 signature verification mechanism, the security and reliability of each call are ensured; in combination with an AK / SK system, unified authority control, flow limitation and post auditing can be conveniently carried out; a service-oriented architecture is adopted, and the extraction capability of different types of files is provided by an independent micro-service module. When a new file type needs to be supported, only a new content extraction module needs to be developed and registered into the mapping table, a main process and an upper layer service do not need to be changed, and the system expansibility is extremely high. And in combination with file size judgment, executing logic of combining synchronous extraction and asynchronous extraction of the file.

One method includes extracting word-elements, each corresponding to a respective element of a ground truth cell-item array from an annotated document, applying logic rules to the extracted word-elements so that the applicability, or not, of each logic rule to each element of the ground truth cell-item array is determined. Based on the applying of the logic rules, metrics are obtained that indicate, for each word-element of the annotated document, the applicability of the logic rules, and the frequency with which applicable logic rules is satisfied. A first aggregation process is performed that aggregates the metrics across a group of unstructured, and annotated, documents, and a second aggregation process is performed that aggregates the metrics regarding a model-generated cell item array that was created based on the group of annotated documents. Finally, respective outcomes of the first and second aggregation processes are compared so as to identify logic rules of interest.

The invention discloses a text generation method and electronic equipment, and relates to the technical field of deep learning, and the method comprises the steps: obtaining a plurality of candidate output texts corresponding to a text input by a user through a pre-training language model, splitting the plurality of candidate output texts into a plurality of statements, according to the method, each statement is extracted, each statement is scored to obtain the statement score of each statement, and then the target text is screened from the multiple statements according to the statement score of each statement, so that representative statements with focused semantics and rich information can be selected from the multiple statements, and trunk content extraction based on the statement scores is realized; and then the sequence-to-sequence generation model is utilized to perform optimization processing on the screened target text to obtain an optimized text, so that unified expression of the trunk content on language styles and sentence pattern structures is realized, and the accuracy of the finally generated text is improved. Therefore, the technical problem that it is difficult to ensure the accuracy of the output text by directly selecting the optimal candidate answers to determine the final output can be solved.

The invention provides a multi-modal content generation method and device, electronic equipment and a storage medium. The method comprises the steps of receiving a document uploaded by a user and performing content extraction; performing block analysis and semantic recombination on the extracted content to generate a PPT text outline and a corresponding explanation script; according to the PPT text outline, matching or dynamically generating a visual material to obtain a first edition PPT; the initial edition PPT is subjected to iterative optimization, the optimized PPT and the explanation script are synthesized to obtain an explanation video, complex structured documents or unstructured content can be processed, it is ensured that the generated outline is clear in organization and accurate in content, and multi-modal content meeting visual aesthetics is output through iterative optimization.

The invention discloses a paper archive digital quality self-inspection system based on AI detection, which relates to the technical field of archive management and comprises an image acquisition module, a content extraction module, a threshold adjustment module, a quality detection module, a result evaluation module, a feedback correction module and a data management module. The image acquisition module is responsible for acquiring images of paper archives by using a scanner or a digital camera and performing preliminary image preprocessing. According to the AI detection paper archive digital quality self-inspection system, unified execution of quality inspection standards is ensured through a rule engine, subjective differences of quality standard understanding of different quality inspection personnel are eliminated, and a detection threshold value is dynamically adjusted according to the age, the material and the storage state of the archive so as to adapt to quality inspection requirements of different archives; and particularly, the difficulty in identifying handwritings, faded handwritings, special symbols and the like in historical archives is optimized, so that the comprehensiveness and the efficiency of detection are greatly improved.

The invention provides a semantic anchor point definition method based on a guide word and a topological structure. The semantic anchor point definition method is used for solving the problem that key fields in structured bills with the same semantic content but large format difference are difficult to position. The method comprises the following steps: constructing a semantic field and guide word dictionary; carrying out OCR identification on the bill image; identifying a matched guide word block in the text block; establishing a spatial adjacency relationship between the guide words; and finally generating an anchor point structure triple containing the semantic field (S), the adjacent structure (N) and the semantic content extraction direction (D). According to the method, the structured representation for expressing the semantic layout of the bills can be constructed, good cross-format adaptability is achieved, stable anchor point support is provided for follow-up format matching and field content extraction, and the method is suitable for processing scenes of multi-type heterogeneous bills such as customs production place certificates, customs declarations and invoices.

The embodiment of the invention provides an information retrieval method and device, electronic equipment and a storage medium, belongs to the technical field of artificial intelligence, and is suitable for financial science and technology and medical science and technology scenes. The method comprises the steps that image conversion is carried out on an original document to obtain an initial document, and the document content type of the original document comprises at least one of the following types: an image class and a table; determining a target information extraction model based on the document content type; performing content extraction on the initial document through a target information extraction model to obtain target document content features; performing information query on the target document content features based on the target query problem to obtain preliminary query content; screening from the original document based on the preliminary query content to obtain a hit document position; generating retrieval basis information based on the hit document position; and performing content optimization based on the historical query context information, the preliminary query content and the retrieval basis information to obtain target retrieval content. According to the embodiment of the invention, the accuracy of information retrieval can be improved.

The invention discloses an image sensitive content auditing method and system, and the method comprises the steps: obtaining the original description of a to-be-audited image, and the original description comprises the text summarization of the visual content of the image and the text content in the image; extracting an entity area in the to-be-audited image, retrieving related external background information based on the to-be-audited image and the entity area, eliminating interference information by using a label of the entity area, and generating a retrieval enhancement result; semantic correlation screening based on sensitive topics is carried out on the retrieval enhancement results, the screened retrieval enhancement results are fused into the original description in an iteration mode, and final fusion description is generated; and constructing a training data set containing positive and negative judgment sample pairs based on the fusion description, performing fine tuning on the visual language model, and performing sensitive content auditing on a new image by using the fine-tuned visual language model. The method can effectively improve the recognition accuracy and reliability of the sensitive content of the image.

A large model memory enhancement method based on time perception consistency feedback and optimization comprises the following steps: 1, data preprocessing: obtaining a historical interaction text generated by each agent in a target scene, and processing the text by using a preprocessing module; 2, attribute mining: extracting agent personal attributes, site attributes, logic attributes and core arguments from each generated content; and 3, keyword management and memory storage: maintaining an independent keyword historical file and a memory library file for each agent, and constructing an evolvable memory which changes along with time. 4, performing memory retrieval and context construction; 5, performing multi-dimensional consistency evaluation, and generating a comprehensive score; and 6, self-adaptive optimization is carried out, and self-feedback and self-evolution of model behaviors are realized. By means of the method, the memory ability and historical consistency of the multi-agent large language model can be effectively enhanced.

The invention provides an intelligent marketing decision analysis method and system for a real-time competitive product strategy, and the method comprises the steps: collecting the marketing content of a competitive product published on a social media platform, extracting an account number, an arriving person portrait feature, a content structure label, a platform identifier and publishing time, and constructing a standardized strategy behavior vector; performing time sequence alignment on the strategy behavior vectors and our marketing indexes, and identifying competing product strategy behavior subsets which have significant influence on the our indexes; based on the subset, generating a structured response strategy containing a recommended person type, a content structure label, a delivery platform and a time period; and performing multi-dimensional matching on the marketing resource library and the content template library, and outputting an executable marketing resource combination and scheduling instruction. According to the method, a closed loop from competitive product behavior perception and causal influence identification to response strategy automatic generation and landing execution is realized, and the real-time performance, the accuracy and the automation level of marketing response are improved.