168 results about "Content retrieval" patented technology

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

Disclosed are methods, systems, devices, apparatus, media, and other implementations for improved search-time content retrieval performed by an information retrieval platform. The implementations include a method including determining by a searching system, at a first time instance, one or more first search results for a first query comprising one or more query terms associated with a first concept, modifying the query, based on the determined one or more first search results, to include one or more modified query terms associated with one or more concepts hierarchically related to the first concept, and determining at a subsequent time instance one or more subsequent search results for the modified query comprising the one or more modified query terms.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention relates to an information retrieval method and device for hierarchical planning reinforcement learning based on retrieval enhancement. The method comprises the following steps: acquiring a natural language query request of a user; calling a large language model of hierarchical planning reinforcement learning training based on retrieval enhancement to generate semantic keywords; the high-level strategy splits the natural language query request into a series of sub-query requests; the low-level strategy generates a retrieval query according to the context of the current sub-query request, and obtains a result verification condition from an external knowledge base through an RAG model; calling a search engine to execute multiple rounds of fine-grained information retrieval to obtain a plurality of candidate retrieval results; and performing deep semantic matching analysis on the candidate retrieval results and the result verification conditions by utilizing the post-trained large language model to calculate a matching degree score, and screening the candidate results according to the matching degree to obtain a final information retrieval result. Compared with the prior art, the method has the advantages that high-quality content retrieval and result display of complex query problems of users in various scenes can be realized.

Disclosed in the present application are a data processing method and apparatus, and a device and a readable storage medium. The method comprises: obtaining M candidate results which are sorted and displayed on a page, wherein the M candidate results are retrieved on the basis of an initial query statement; if an interaction operation meets a re-retrieval condition, acquiring historical interaction information, which comprises N historical query statements and a set of historical interaction results, wherein the N historical query statements comprise the initial query statement, and the set of historical interaction results comprises a candidate result which is included in an interaction record corresponding to an object; and generating context content vectors corresponding to the historical interaction results in the historical interaction information, and generating an updated query statement by means of the context content vectors and the N historical query statements, wherein the updated query statement is used for being displayed in a retrieval prompt box, and the retrieval prompt box is used for guiding the object to perform re-retrieval by means of the updated query statement. By means of the present application, the accuracy and efficiency of content retrieval can be improved.

The invention belongs to the technical field of brain-computer interfaces, multi-modal feature alignment and information retrieval, and discloses a visual content retrieval method based on electroencephalogram signals. Cross-subject standardized electroencephalogram samples are obtained and input into an electroencephalogram encoder to extract low-dimensional electroencephalogram signal feature vectors; an image encoder is adopted to process the corresponding retrieval images to extract visual feature vectors; the low-dimensional electroencephalogram signal feature vector and the visual feature vector are jointly input into a prototype attention enhancement module to form a dynamic prototype pool, and prototype enhanced electroencephalogram signal representation is obtained through processing; the method comprises the following steps: performing classification training according to existing prototype enhanced electroencephalogram signal representation and image data pairs; for a plurality of time slices of the to-be-queried electroencephalogram signal sample, calculating a reconstruction error or a signal-to-noise ratio of each time slice to obtain a confidence coefficient; and adopting a weighted average or voting mechanism to fuse a plurality of time slice results, and outputting a stable and robust final visual retrieval classification result.

An example may provide at least one first generative machine learning model (GMLM) instruction and an intent to a GMLM. The at least one first GMLM instruction is to cause the GMLM to use the intent to generate first GMLM output. The first GMLM output includes GMLM-generated output sections. A device may provide the first GMLM output including the GMLM-generated output sections and at least one second GMLM instruction to the GMLM. The at least one second GMLM instruction is to cause the GMLM to use the intent, the GMLM-generated output sections, and a first data set to generate second GMLM output including at least one first digital element. A device may validate the second GMLM output by comparing the at least one first digital element to at least one second digital element. The at least one second digital element is accessible via a second data set.

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 relates to an academic paper abstract generation method based on an image-text fusion index, and belongs to the technical field of artificial intelligence and multi-modal information processing. The method comprises the following steps: acquiring academic paper data from a public data source; according to academic papers in the academic paper data, segmenting paper contents according to logic, establishing association between fragments and images, and constructing an image-text fusion index; performing semantic retrieval on four dimensions in the academic paper to obtain a final retrieval content set of all the dimensions; and generating an image-text fusion abstract based on the retrieval content set of the four dimensions. According to the method, by constructing the image-text fusion index, the image-text association strength and the chapter context coherence are enhanced, deep coupling of the image and the text in the semantic level is achieved, and the image-text collaborative expression ability of the abstract is improved; by designing a structured dimension template and a content retrieval mechanism driven by a standard problem, the structured coverage of key information is ensured, and the logic continuity and readability of the abstract are remarkably improved.

Apparatus and methods for automating pre-procedural coordination workflows in a digital environment include at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to generate, using a trained content retrieval data structure, a plurality of content retrieval parameters, receive, from a first entity, an input including an input data structure as a function of the plurality of content retrieval parameters, populate, using the input data structure, a content queue comprising a plurality of content elements, query a second entity using at least a content element of the plurality of content elements, update the content queue as a function of at least a query response received from the second entity, generate a recommended course of action as a function of the updated content queue, and perform the recommended course of action.

The invention relates to an infant teaching resource intelligent searching and recommending system based on an AI large model. The system comprises a resource acquisition module, a label indexing module, an A I semantic understanding module, a resource screening and adapting module, a recommendation sorting module and a compliance control module. Teachers input teaching requirements through natural languages, voices, teaching plans or pictures, and the system performs semantic analysis and keyword extension based on a large language model and completes multi-modal content retrieval in combination with a resource map. The system introduces a complexity analysis and right age adaptation mechanism, intelligently simplifies and reconstructs contents, and generates a personalized recommendation list based on teacher portraits and a collaborative filtering algorithm. And the resource use process is constrained by the compliance control module to ensure that the content copyright is legal. The system can significantly improve the lesson preparation efficiency of kindergarten teachers and the teaching resource adaptation degree, and is suitable for a digital teaching platform and an intelligent education system.

The invention discloses an investigation report generation method based on large language model deep thinking and content retrieval, and belongs to the technical field of artificial intelligence. Generating requirement information input by a user is obtained, cue words are constructed according to the generating requirement information, the cue words are input into a preset large language model, and an article generating plan is generated; analyzing and partitioning the generated plan to obtain a plurality of structured chapter abstract contents and corresponding networking search keyword data; circulation is carried out until all the chapters are generated, retrieval is carried out according to networking search keywords needed by the current chapter, and reference content needed for writing the current chapter is obtained; and the reference content obtained through retrieval, the written previous chapter content and the abstract content needing to be written by the current chapter are used for constructing cue words required by writing of the current chapter. And inputting the cue word into a preset large language model, carrying out deep reasoning and thinking, generating the content of the current chapter, and giving a corresponding reference in a place where the related retrieval content is cited.

The embodiment of the invention provides a ship industry standard document intelligent processing method, which comprises the following steps: S1, document analysis: carrying out structured analysis and restoration on a ship industry standard document to generate a structured text supporting vectorization storage and semantic retrieval; s2, database construction: constructing a document vector index database based on the structured text to form a semantic retrieval task-oriented efficient data structure; and S3, content retrieval: performing user-oriented query, constructing a semantic retrieval mechanism, and completing high-precision matching from a natural language problem to a structured document content and result return. According to the embodiment of the invention, the document structure can be accurately restored, the content segmentation is more reasonable, the retrieval matching is more accurate, the retrieval result has more content depth, the technical path is clear, and the project landing is easy.

The invention provides a multi-path voice stream real-time separation and content retrieval method, which comprises the following steps of: constructing a local voiceprint library by using a registered voice sample, and extracting and normalizing voiceprint characteristics of a target speaker through a deep voiceprint encoder; performing frame-level feature analysis on the mixed voice signal, calculating a semantic distance between a current voice frame and a target voiceprint, and generating a dynamic confidence score; feature conflict detection is carried out by combining a confidence coefficient threshold value and a de-jitter mechanism, and a conflict perception attention gating module is triggered to realize frequency band enhancement and suppression; a continuous feature confusion region is dynamically identified based on a sliding window, and the separation precision of the confusion region is improved through local reconstruction and time-frequency mask optimization iteration. According to the method, high-precision recognition and separation of the voice of the target speaker in the mixed voice are realized, non-target components are effectively inhibited, and the separation effect and robustness are improved.

Aspects of the present disclosure relate to hierarchical content organization techniques, which may thus enable improved content indexing and / or retrieval, among other benefits. In examples, a content platform maintains a content hierarchy, where, for example, each category within the content hierarchy has an associated set of categories / subcategories. It will be appreciated that any number of hierarchical levels may be used. Accordingly, a post or other content item may be associated with one or more categories / subcategories (e.g., one or more nodes) of the content hierarchy, thereby facilitating subsequent retrieval of the content (e.g., responsive to a user query and / or to provide to a search platform). Thus, in contrast to discussion boards and other content management systems, the disclosed aspects ensure content is organized when created, thereby improving later retrieval and / or any of a variety of subsequent processing, among other benefits.

The invention discloses an unstructured document multi-modal analysis and semantic metadata deep extraction method and system based on multi-agent collaboration. The method comprises the steps that multi-modal elements such as texts, images and tables are extracted in parallel through a multi-format unified analysis engine; a four-agent (table structure analysis, multi-modal image analysis, semantic text understanding and metadata extraction) collaborative architecture is adopted, and task decomposition, asynchronous scheduling and result fusion are realized based on a multi-agent collaborative scheduling algorithm; the performance is improved by combining dynamic concurrency control, memory optimization and an intelligent cache mechanism; and finally generating structured output. The system integrates the modules and supports micro-service architecture and stability guarantee. According to the method, multi-modal deep semantic understanding is achieved, the processing efficiency is improved by 3-5 times compared with that of a traditional method, the text / table analysis precision reaches 98% and 95% respectively, localized efficient processing and incremental updating are supported, and a high-quality data source is provided for enterprise knowledge management and content retrieval.

The invention relates to a data retrieval method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the steps that to-be-retrieved content is acquired, and multi-modal to-be-retrieved features of the to-be-retrieved content on multiple different dimensions are determined; determining target attention weights of the multi-modal to-be-retrieved features and an original document vector index in a preset three-dimensional feature vector container on a plurality of different dimensions; based on the target attention weights on the multiple different dimensions, determining a comprehensive matching score between the multi-modal to-be-retrieved feature and an original document vector index; and determining a target document vector index from the original document vector indexes based on the comprehensive matching score, and retrieving a result according to the target document vector index and the content corresponding to the to-be-retrieved content. According to the method and the device, the accuracy of the obtained content retrieval result is improved.

A method of retrieving visual content includes receiving user input defining an initial search query from a client application. The initial search query and a meta prompt are then delivered to a refined query generating model which is trained to analyze the initial search query to determine user intent and to generate a refined search query based on the initial search query and the meta prompt. The refined search query is delivered to a visual content retrieval model which retrieves aesthetic visual content with reference to a visual content index. Retrieved aesthetic visual content is returned to the client application.

The invention discloses a multi-mode private illegal content retrieval method and system based on semantic granularity perception, and belongs to the technical field of image-text multi-mode content retrieval. In order to solve the technical problem that private and illegal contents are difficult to effectively retrieve through explicit text description, the method comprises the following steps of: constructing an image identifier consisting of clustering, matrix decomposition, quantization and unique coding, and realizing generative learning of an image and the image identifier by utilizing a multi-modal large model; and constructing a soft label based on the similarity between the image and the text representation, and outputting a corresponding image identifier through text-driven generative retrieval learning. According to the method, the illegal image content with semantic matching can be retrieved from the private text description, and the method is suitable for scenes such as illegal content examination, risk identification and content filtering.

The invention discloses a document question and answer method and device, a storage medium and a program product, and the method comprises the steps: carrying out the content retrieval of a first document database based on an inquiry statement, so as to determine a corresponding intermediate retrieval result; performing content retrieval on a second document database based on the inquiry statement and the intermediate retrieval result to determine a retrieval matching result, the first document database and the second document database being databases constructed based on at least one document; detecting whether the retrieval matching result meets a reply condition of the inquiry statement or not; and when the retrieval matching result meets a reply condition, constructing a reply answer according to the retrieval matching result. Therefore, the question answering system can adapt to questions with different semantic complexities and logic levels through a document retrieval mechanism mixed with the neural symbols, and high-quality answers can still be efficiently produced when complex questions are processed.

Method, systems and computer programs for content provision are provided. A requestor node generates an anonymized and compressed representation of the user profile using a user data embedding machine-learning model inputting user data of a user profile. The content retrieval platform receives the anonymized and compressed representation of the user profile and a content retrieval request and inputs the anonymized and compressed representation of the user profile and the content retrieval request to a content determination machine-learning model to determine content in response to the content retrieval request.

The invention discloses an enterprise knowledge base content retrieval method and device based on an event graph, equipment and a storage medium, and relates to the technical field of natural language process.The method comprises the steps that candidate event information is extracted from an initial enterprise knowledge base text to obtain a structured event sentence set, all the candidate event information in the event sentence set is set as nodes, and the nodes are stored in the enterprise knowledge base text; setting a time sequence relationship among the candidate event information as an edge to obtain an event graph; analyzing user query content to obtain a query field, determining a candidate node set based on the query field and the event graph, performing path search in the candidate node set to obtain a candidate event path, and performing time-aware reasoning on the candidate event path based on a preset time chain thinking prompt strategy to obtain a reasoning result; and performing structured analysis, event path alignment and confidence evaluation processing on the reasoning result to obtain an enterprise knowledge base retrieval text with a reference basis, a time label and a confidence label so as to improve the efficiency of retrieving the content of the enterprise knowledge base.

The invention discloses a science popularization content intelligent retrieval interaction system and method combined with a knowledge graph. The system comprises a knowledge graph construction unit, a multi-layer multi-head attention mechanism collaborative Transform coding unit, a knowledge graph embedding and Transform feature fusion unit, a retrieval result sorting unit based on fusion features, an interactive intention capture unit and a result optimization output unit. The method comprises the following steps: constructing a knowledge graph for multi-source science popularization data, extracting retrieval keyword features by applying Transform coding, fusing the knowledge graph and the retrieval keyword features, and calculating the similarity with science popularization content to finish initial sorting; and meanwhile, capturing user interaction behavior analysis intention change in real time, and readjusting and outputting an initial result in combination with knowledge graph semantic association. The method corresponds to operation steps of all units of the system, and intelligent retrieval interaction of popular science content is achieved. According to the system and the method, the problems of insufficient semantic understanding, poor interaction experience and the like of traditional retrieval are effectively solved, and the accuracy and the interaction intelligence of popular science content retrieval are improved.

The invention discloses a video content retrieval method and device based on television system voice interaction and a terminal, and relates to the technical field of video processing, and the method comprises the steps: when a video is played for the first time, extracting a picture frame from the video at a preset frequency, converting the picture frame into a multi-dimensional image feature vector and a corresponding video timestamp, and carrying out hierarchical storage in a database, constructing vectorized data containing visual semantic information; obtaining a voice retrieval instruction, performing intention recognition and semantic understanding, extracting a detection keyword, and generating a multi-dimensional retrieval feature vector; calculating a matching degree between the multi-dimensional retrieval feature vector and a multi-dimensional image feature vector of a video picture frame stored in a database, and screening out picture frames of which the similarity is higher than a preset similarity threshold to form a retrieval candidate matching set; and determining matched picture playing. The video content retrieval method is efficient, accurate and high in interactivity, and retrieval experience and operation efficiency of the user in the video watching process are remarkably improved.

Described are systems and methods for providing virtual and remote access to virtual content in a virtual safebox. A computer system includes a virtual vault database for storing a plurality of virtual safeboxes storing digital / virtual content provided by a user. The stored content includes image content representing personal identification documents of the user. The computing device includes at least one processor configured to receive an access request message from a first safebox access computing device and, in response retrieve, from the virtual vault database, the first virtual safebox. The processor is further configured to generate an inventory list identifying each content item and transmit an inventory message to the first safebox access computing device. The processor is further configured to receive a content retrieval message from the first safebox access computing device and transmit a content message to the first safebox access computing device.

The invention relates to the field of test case generation, in particular to an AI intelligent test case generation system and method.The AI intelligent test case generation system comprises a demand registration module, a data connection module, a knowledge base module, an AI generation module and a case export module, and all the modules work cooperatively. Product demand input and acquisition, historical demand data storage and retrieval, and test case set generation and export are realized; the invention further comprises an AI intelligent test case generation method suitable for the system. The AI intelligent test case generation method comprises the steps of inputting a unique identifier, obtaining demand content, retrieving an associated scene, analyzing interaction logic, generating a test case set, exporting the case set and the like. According to the method and the device, the effects of efficiently and accurately generating the test case set containing the single-module case and the cross-module closed-loop case, automatically adapting to the format of the target demand management platform and improving the test case generation quality and efficiency are achieved.

The embodiment of the invention discloses a content retrieval method and device, electronic equipment, a computer program and a storage medium. According to the embodiment of the invention, a content retrieval request is received, wherein the content retrieval request comprises retrieval data of at least one mode; feature extraction is carried out on the retrieval data of at least one mode in the same feature space to obtain a retrieval feature sequence of the mode, and the retrieval feature sequence comprises at least one retrieval feature; performing attention weighting on the retrieval features to obtain retrieval semantic features corresponding to the modals, and fusing the retrieval semantic features of the modals to obtain target retrieval semantic features; and based on the target retrieval semantic feature, screening out target content corresponding to the content retrieval request from a preset content set. According to the embodiment of the invention, the retrieval accuracy can be improved, and the calculation overhead caused by multiple independent retrieval and fusion is avoided, so that the synchronous optimization of the retrieval efficiency is realized while the semantic understanding precision is improved.

The invention discloses a structured acquisition method for edge video stream image data, and the method comprises the steps: collecting video stream image data, inputting the video stream image data into an edge calculation platform, and carrying out the segmentation and preprocessing of a video stream, and obtaining the motion range of a target; constructing a target tracking method according to the motion range of the target to obtain time sequence data of the target; according to the time sequence data of the target, constructing a target automatic labeling model, training the automatic labeling model, and updating parameters corresponding to the model; associating annotation information generated by the automatic annotation model after parameter updating with an image, establishing a corresponding relationship of data, and generating structured data with an association relationship; storing the structured data in a database, and adding an adaptive index to optimize query; according to the characteristics of an edge computing platform, the video stream image data is efficiently and accurately acquired, and the video stream data is applied to subsequent target tracking and video content retrieval after being subjected to structured processing.

The invention discloses a knowledge question and answer processing method and device based on a large model, and relates to the field of natural language processing. The method comprises the following steps: assembling a user question and disassembling a prompt big model to obtain sub-questions; retrieving related contents of the sub-questions, and then assembling an enlargement model to generate questions and answers; circulating until all sub-answers are obtained; the original questions, the sub-questions, the answers and the summary prompt are assembled, and summary content is generated and returned. The device comprises a question splitting module, a content retrieval module and the like. Through cooperation of the large model and the vector knowledge base, complex question disassembly, accurate knowledge retrieval and answer integration are achieved, the accuracy and comprehensiveness of knowledge questions and answers are improved, and the method is suitable for scenes such as intelligent customer service and online education.

This invention discloses an intelligent generation method for structural health monitoring schemes based on workflow orchestration. It automates the generation of structural health monitoring schemes using FastGPT workflow orchestration, innovatively introducing FastGPT as the core of the workflow driver to guide semantic parsing, task allocation, content retrieval, and scheme output. The standardized construction and high-hit-rate design of the structural health monitoring knowledge base involves creating a standardized scheme knowledge base where all scheme documents are divided into logical segments, using custom delimiters to control content boundaries; improving semantic retrieval accuracy and ensuring the contextual integrity of content called by FastGPT, avoiding fragmented references, and improving response accuracy. An independent management and accurate referencing mechanism for the monitoring equipment list is implemented, constructing an independent monitoring equipment knowledge base to avoid incorrect associations between equipment technical indicators and images; FastGPT searches this knowledge base when referencing equipment information, ensuring the accuracy and consistency of the referenced content in the output.