122 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.

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 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 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.

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 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 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.

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.

The application provides a quick retrieval method for language structure big data, according to the type of language structure data, the basic index unit to be constructed and the index content corresponding to the index unit are determined, when retrieving, the retrieval expression input by the user is analyzed, the retrieval unit corresponding to the index unit and the relationship between the retrieval units are analyzed, the result of each retrieval unit is obtained in the way of single chain binary tree postorder traversal and is combined according to the relationship between the analyzed retrieval units. In the application, the design of the index unit can utilize the lexical and syntactic information of the language data itself, realize the retrieval requirement of multi-directional language knowledge of the language structure data, the design of the structure retrieval language can enable the user to learn the retrieval language at a low cost and write the retrieval expression which is standard and meets the retrieval requirement.

System for automated consolidation and distribution of structured data includes a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to generate, using a content retrieval module, content retrieval parameters, receive input data as a function of the content retrieval parameters, process the input data by applying a scaling factor to each one of the input data, populate an action matrix as a function of the processed input data, wherein the action matrix includes action elements and each action element is assigned to an entity, generate, using an outcome machine learning model trained on outcome training data, a predicted outcome as a function of at least an action element of the action elements, and modify the at least an action element of the action elements and the action matrix as a function of the predicted outcome.

The application discloses a kind of video intelligent slice method based on multi-modal information fusion and semantic constraint, it is related to video content analysis and intelligent editing technical field, it is characterized in that it proposes semantic priority audiovisual collaborative slice scheme, obtains shot interval by shot boundary detection, constructs voice interval and effective word information by voice recognition, adopts voice interval as semantic mask to remove visual cut point in the process of voice expression, and based on fragment length and effective word, content density determination is carried out to long fragment, and further adopts visual re-cutting, audio energy driving and uniform cutting Secondary slicing strategy of division, the method provided by the application solves the problem of semantic fragmentation and content perception loss in the prior art, can obtain video fragment with complete semantics, high content density and appropriate length, and is suitable for various application scenarios such as automatic editing, short video generation and video content retrieval.

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 embodiment of the invention discloses a data optimization method and device, equipment, a storage medium and a product. The method comprises the steps that associated data and reference information of a to-be-optimized statement are obtained, the associated data comprise an analysis process of the to-be-optimized statement, the reference information comprises a rewriting example of a reference statement, supplemental content retrieval is conducted on the to-be-optimized statement, and supplemental content of the to-be-optimized statement is obtained; and rewriting the analysis process based on the to-be-optimized statement, the rewriting example of the reference statement and the supplementary content to obtain a rewritten analysis process, and generating an optimization result of the to-be-optimized statement through the rewritten analysis process. It can be seen that the analysis process of the to-be-optimized statement is rewritten by adding the rewriting example and the supplementary content of the reference statement, automatic optimization of the to-be-optimized statement can be achieved, and the optimization efficiency of the to-be-optimized statement is improved.

Systems and methods for generating content prediction data for accurate content retrieval. Such a method includes (i) determining whether to generate historical performance training data or predicted performance training data; (ii) when determining to generate the historical performance training data for a first content item of the plurality of content items: (a) generating the historical performance training data for the first content item based on historical serving data associated with the first content item, and (b) training a smart agent model using the historical performance training data; and (iii) when determining to generate the predicted performance training data for a second content item of the plurality of content items: (a) generating, using the smart agent model, the predicted performance training data for the second content item; and (b) training a content retrieval model using the historical performance training data and the predicted performance training data.

The invention provides a software V-shaped development process oriented RAG retrieval method and system, and the method comprises the steps: receiving a technical document, and carrying out the segmentation and recognition of the document, so as to obtain a document fragment set; extracting an engineering entity, an identifier and an attribute from the document, and constructing a binding relationship between a document fragment and the entity; constructing a knowledge graph containing nodes and node association relationships in a graph database or a triple; constructing a multi-dimensional vector representation of the document fragment, and writing a vector into a vector database; performing semantic retrieval on matched candidate document fragments based on a user query request, and performing neighborhood expansion on the target entity in the knowledge graph to obtain a candidate graph; aligning the candidate document fragments with the candidate atlas, and selecting a preset number of fragments which are ranked in the top to be aggregated into an evidence packet; and inputting the evidence packet and the generation task instruction into a large language model, and generating a retrieval result based on a predetermined constraint decoding strategy. By means of the scheme, the consistency of all stages in RAG retrieval can be guaranteed, and the content retrieval precision is improved.

The invention provides a social media agent simulation method based on dynamic role information, and the method comprises the steps: employing a large language model LLM to expand a role template to generate basic attributes and advanced attributes, and enabling the basic attributes and advanced attributes to serve as a role file of an agent; knowledge matched with the role file is captured from the general knowledge data set and serves as long-term memory; the intelligent agent generates a dynamic plan according to the role file and the action history; the intelligent agent obtains the posts from the social platform according to the dynamic plan, retrieves the advanced attributes according to the post content, judges whether the post content accords with the advanced attributes or not, and executes interaction actions including like, forwarding and / or commenting if the post content accords with the advanced attributes; and when the agent posts on the social platform, querying long-term memory according to a posting theme, calculating text similarity between a query result and personalized knowledge, and selecting the query result of which the similarity is greater than a preset value to generate posting content. Therefore, the intelligent agent can make reasonable actions according to the role, and the posting and comment content is highly consistent with the role information.

This disclosure provides a content retrieval method, system, electronic device, and storage medium, comprising: receiving target retrieval content and a target retrieval mode sent by a first terminal, wherein the first terminal is a terminal connected to a server; obtaining associated information corresponding to the target retrieval mode according to the target retrieval mode; retrieving the target retrieval content according to the target retrieval mode and the associated information to obtain retrieval results; and sending the retrieval results to the first terminal and / or a second terminal, wherein the second terminal is another terminal connected to the server. This disclosure achieves targeted retrieval of target retrieval content, improving retrieval efficiency. Simultaneously, it allows users to select the appropriate retrieval method according to their needs, satisfying user requirements. When the retrieval results are displayed on the second terminal, i.e., the retrieval results and the original file are displayed on different terminals, it enables simultaneous viewing of the retrieval results and the original file, avoiding the need for users to switch back and forth.

The application relates to the technical field of speech recognition, and particularly discloses an artificial intelligence education system interaction method based on speech recognition, which comprises the following steps: obtaining a speech data stream; extracting repetitive speech segments in the speech data stream; extracting acoustic feature modes of the repetitive speech segments; determining specific education needs K according to the extracted acoustic feature modes and a constructed personalized mapping library; obtaining education content materials based on generated content retrieval instructions, integrating the education content materials and current interaction context information, forming a customized interaction response sequence, and executing the interaction response sequence through an output device. The application recognizes and interprets repetitive speech modes of special children, establishes a personalized mapping library, realizes accurate demand recognition and customized calling of education content, and improves interaction understanding and education intervention effect.