292 results about "Content analytics" patented technology

The invention relates to the technical field of training data auditing, in particular to a semantic perception black box large language model training data auditing method and system.The method comprises the following steps that content is returned based on a text generation interface, lexical elements and candidate content are analyzed for multi-round sampling distribution, and a convergence index is determined; and calculating semantic paths and weights of the real content and the candidate content, and analyzing differences between the real content and the candidate content and the reference content to obtain an affiliation adaptation judgment result. According to the method, through multi-round sampling behavior fluctuation tracking, section feature dynamic extraction, stability change judgment and sequence-level weight aggregation, fine separation of training data attribution signals is achieved, a multi-level judgment system is constructed for complex expression and diversified output, judgment accuracy is optimized through a tension comparison signal group and an attribution adaptation judgment mechanism, and the judgment accuracy is improved. And a sensitive and adaptive training data member auditing strategy is formed, so that the risk hidden danger omission is effectively prevented, and the model data security boundary control is enhanced.

The invention relates to the technical field of voice signal processing, and discloses a voice segmentation intelligent editing system based on deep learning. The system comprises a voice feature extraction module, a segmentation boundary detection module, a semantic content analysis module, an editing strategy generation module and a real-time quality evaluation module. The voice feature extraction module collects multi-dimensional voice features and timestamp information, and verifies feature integrity and timeliness; the segmentation boundary detection module identifies voice pause intervals and semantic turning nodes and divides segmentation units and boundary types; a semantic content analysis module extracts text content and emotion features of each segment, and analyzes semantic topic relevance and information density; an editing strategy generation module formulates a segmentation retention rule and a sequence adjustment scheme, and matches user preferences and scene demands; the real-time quality evaluation module monitors voice fluency and information integrity in the editing process and analyzes splicing errors and user feedback. According to the system, intelligent processing of the whole voice editing process is realized.

The invention provides an AI-based automatic test case generation method and system, and relates to the technical field of software testing, and the method comprises the steps: converting an original demand document uploaded by a user into an image format document, and calling a multi-modal large model to recognize text features and non-text features in the image format document, integrating to form an analyzed demand document; dynamically cutting the analyzed demand document to obtain a plurality of logically coherent content blocks; calling a large language model to generate a corresponding test case title for each content block, and forming a test case title set; and inputting each test case title in the test case title set and the corresponding content block into the large language model to generate a complete test case corresponding to the original demand document. The method has the beneficial effects that through multi-modal content analysis and intelligent workflow arrangement, automatic analysis and understanding can be performed on image-text features of a demand document, a high-quality test case is automatically generated, and the test period is greatly shortened.

The invention discloses a zero-configuration knowledge graph construction method, equipment and a medium, belongs to the technical field of knowledge graphs, and aims to solve the technical problem of how to overcome the hard coding defect in traditional knowledge graph construction, realize a more flexible construction mode with lower difficulty in use, further realize data source entity association and semantic fusion and improve the knowledge graph construction efficiency. According to the technical scheme, field name semantic analysis and data content analysis are conducted through a dynamic semantic analysis algorithm, and intelligent database deep analysis is achieved; the same entities in the multiple tables are automatically recognized and merged through a cross-table entity intelligent merging algorithm, and multi-table collaborative intelligent search is achieved; constructing an intelligent knowledge graph: automatically selecting an optimal construction strategy through a multi-strategy relation construction engine according to keyword role features, and independently constructing a local knowledge graph for each data table; and optimizing and intelligently exporting the knowledge graph.

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

A system and method for contextual advertising that analyzes video content through multimodal examination of visual, audio, and textual elements to create detailed contextual understanding of individual scenes. The system segments video content into discrete scenes and simultaneously processes each scene to extract contextual characteristics including objects, settings, dialogue, music, and emotional tone. These characteristics are classified according to advertising industry taxonomies and converted into numerical embeddings that enable semantic similarity matching. During video playback, when advertisement opportunities occur, the system identifies the current scene context, analyzes available advertisements using similar techniques, computes similarity scores between scene and advertisement characteristics, and selects contextually appropriate advertisements for seamless integration. This approach enables privacy-compliant advertising that matches advertisement content with scene context rather than relying solely on user behavioral data, improving advertisement relevance and viewer experience.

The present disclosure provides a search engine optimization (SEO) system comprising a remote server. The remote server comprises a memory with a set of executable routines and a search engine database with multiple fields of applications, each associated with multiple URLs indexed with a user engagement matrix, written data, and a search engine ranking. A processor acquires a web link from a computing device, extracts textual content, analyzes relevancy, and retrieves relevant URLs. The processor evaluates a thematic score, a readability score, and an emotional tone data using NLP techniques, analyzes written data of each URL to determine a topic weight, a legibility weight, and a sentiment tone data, develops a machine learning model, applies the model to recommend content alterations, and renders the alterations at the computing device.

The invention relates to the technical field of video content analysis, and discloses a video scene content label determination method based on a knowledge graph. The method comprises the following steps: inputting a multi-modal feature sequence formed by visual, audio and text features extracted from a video stream into a knowledge graph inference engine comprising an entity relationship network and a semantic association rule base; performing node mapping through an engine to generate an initial scene entity set; performing hierarchical reasoning on the set based on a semantic association rule base to obtain a scene semantic topological structure; screening core entities according to entity weight distribution in the topological structure, and generating a candidate tag set; performing time sequence consistency verification on the candidate tag set, and correcting tag time sequence offset in combination with timestamp information; performing cross-modal disambiguation on the corrected label set by using an entity relationship network to eliminate semantic conflicts; and according to a disambiguation result, constructing a scene label knowledge sub-graph containing entity attributes and relation path constraints.

A multi-granularity semantic alignment method oriented to cross-modal image-text retrieval comprises the steps that multi-layer feature representation of an image and a text is established, and a global vector and a local mark sequence are obtained respectively; capturing fine-grained features with higher information amount by using a mark screening mechanism, and inhibiting interference of redundant and invalid features on representation; under a unified semantic alignment framework, a bidirectional mapping relationship among multi-granularity semantics is constructed, and coarse-granularity, fine-granularity and cross-granularity semantic similarity alignment is comprehensively realized so as to strengthen multilayer semantic consistency constraints; and adaptively integrating the granularity similarities through a weighted fusion module, generating single instance-level similarities, and carrying out descending sorting on candidates. According to the method, the multi-granularity characteristics of the image and the text are comprehensively considered, the image-text retrieval precision in an actual scene can be improved, and the method has good application value in the fields of image-text retrieval, multi-modal content analysis and the like.

Effective Retrieval-Augmented Generation (RAG) pipelines face significant challenges when processing domain-specific technical documents that have diverse content types like text, figures, equations, and tables. To address this challenge, a context-oriented RAG system can be implemented for various domain-specific applications. The RAG system can include a lightweight, two-stage architecture to facilitate contextual understanding: a content analysis and enrichment pipeline for structured metadata extraction and a query processing pipeline for context-aware retrieval. In some cases, tabular data is processed using a dual-stream approach: semantically via text and visually via screenshots. The embedding vectors and the metadata can be stored in a visual data management system. The RAG system, utilizing the visual data management system, can answer questions and precisely retrieve technical information in a way that can preserve structural relationships and semantic connections across different modalities.

The invention relates to the technical field of video content analysis, in particular to a mine surveillance video key frame extraction method, which comprises the following steps: extracting multi-dimensional features of each frame of a candidate frame set, obtaining a fusion value, and obtaining a candidate key frame set; based on an Euclidean distance method, calculating an Euclidean distance between continuous frames of the candidate key frame set, and determining a total clustering number according to a relationship between the Euclidean distance and a preset clustering threshold value; and clustering the frames in the candidate key frame set by using a preset mixed GWO-FCM clustering algorithm and the total clustering number to obtain a key frame set. According to the method, a hybrid clustering algorithm GWO-FCM combining grey wolf optimization and fuzzy C-means is adopted, global search and local optimization capabilities are considered, and the accuracy and representativeness of key frame clustering are ensured. The high-quality extraction of the key frames is realized, the number of redundant frames is obviously reduced, and the compactness and readability of the video abstract are improved.

The invention provides an intelligent video monitoring early warning method and system based on a multi-mode visual model, and the method comprises the steps: introducing a low-rank decomposition attention mechanism and an improved attention mechanism into an improved YOLOv8 algorithm, so as to obtain a detection model; detecting a specific target in the video based on the detection model; a ByteTrack algorithm is adopted to track a specific target, and an image of the specific target is cut out; performing short-time-sequence action recognition on the image by adopting a SlowFast algorithm; and a Qwen-VL multi-mode reasoning model is adopted to analyze the long-time-sequence content of the video, and the analyzed long-time-sequence content is combined with the RAG knowledge base to realize dynamic anomaly judgment. According to the method, the purpose of effective and continuous tracking can be achieved, short-time-sequence action recognition and long-time-sequence content analysis can be carried out, and dynamic anomaly judgment can be effectively achieved.

The application relates to the field of generative artificial intelligence, and discloses an ethical control and content authentication method of generative AI, which comprises the following steps: analyzing generative content characteristics and predicting attack modes, dynamically adjusting a watermark embedding strategy, and enhancing robustness by using redundant coding, optimizing watermark embedding by using game theory, guaranteeing watermark integrity, verifying watermark effectiveness by using multi-modal authentication analysis, and finally realizing automatic copyright protection by using blockchain and smart contract; the application also provides an ethical control and content authentication system of generative AI, which comprises a content analysis module, a watermark embedding module and the like. Through a game model between a protector and an attacker, optimal dynamic adjustment of the watermark embedding strategy is realized, the watermark can be adaptively adjusted to ensure its effectiveness under different attack modes, and the problem that the watermark is easily tampered with or removed under a confrontational attack is effectively avoided.

The invention relates to the technical field of network security intelligence, in particular to an information acquisition scheduling system based on dynamic weight optimization, which comprises a data monitoring and acquisition module, a scheduling queue generation module, a content analysis and feedback module, a crawler execution module and a content analysis and feedback module. Collecting user interaction data, content change rate and the like of a target intelligence source through a data monitoring and collecting module; the dynamic weight calculation module calculates an activeness score, a historical hit rate and a low-frequency protection gain according to the data and fuses the data to obtain a comprehensive final weight; a scheduling queue generation module builds a main queue and a low-frequency guarantee pool according to weight sorting, and distributes differential grabbing parameters; the crawler execution module performs distributed parallel grabbing; the content analysis and feedback module judges validity and feeds back adjustment parameters to form a closed loop; according to the method, the capture coverage rate and timeliness of the low-frequency high-value intelligence source can be improved, the utilization rate of system resources is optimized, and the requirements of network security intelligence for information comprehensiveness, timeliness and accuracy are met.

An object of a system according to an embodiment is to make it possible to easily check the content of a link destination without jumping to the link destination.SOLUTION: A system includes a link destination content analysis unit, a summary generation unit, and a display unit. The link destination content analysis unit analyzes the content of the link destination. The summary generation unit generates a summary based on the content analyzed by the link destination content analysis unit. The display unit displays the summary generated by the summary generation unit in a balloon format.SELECTED DRAWING: Figure 1

The invention discloses a cultural transmission knowledge graph construction method based on game video content analysis, and the method combines deep learning, natural language processing and multi-modal data fusion technologies, and is dedicated to deep mining and structured representation of cultural knowledge elements and transmission association thereof from mass and multi-modal game video data. And constructing a knowledge graph serving cultural transmission research and practice. The method not only provides an important theoretical tool for understanding a new mechanism of cultural transmission in the digital era, but also opens up an innovative technical path for actively, accurately and efficiently bearing and transmitting the cultural value by using game media, and has remarkable theoretical value and application potential.

The invention relates to the technical field of information processing, and discloses a creative content strategy intelligent analysis method and device based on artificial intelligence, and the method comprises the steps: displaying a content page of a creative content strategy, which is obtained by analyzing a plurality of pieces of video information of a target competitive product, the content page comprises a content analysis area of at least one analysis result of the creative content strategy, and each content analysis area is provided with a link component; in response to a trigger operation on any link component, determining an analysis result corresponding to the target content analysis area where the link component is located as a to-be-displayed target analysis result, and displaying an analysis page corresponding to the target analysis result; and displaying creative description information corresponding to the target analysis result and a video playing entrance thereof on an analysis page to trigger and display an associated creative video corresponding to the creative description information. Therefore, by implementing the method, the analysis accuracy and efficiency of the competitive product video strategy can be improved, and the accuracy and efficiency of acquiring the competitive product video strategy by the user can be improved.

A content analysis system includes processor and memory hardware storing data analyzed content items and instructions for execution by the processor hardware. The instructions include, in response to a first intermediate content item being analyzed to generate a first text description, receiving the first intermediate content item and analyzing the first text description to generate a first reduced text description. The instructions include identifying a first set of tags by applying a tag model to the first text description and generating a first analyzed content item. The instructions include adding the first analyzed content item to the analyzed content database and, in response to a displayed content item being associated with at least one tag of the first set of tags, displaying a first user-selectable link corresponding to the first analyzed content item on a portion of a user interface of a user device displaying the displayed content item.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

The invention relates to the technical field of video processing, particularly discloses a fine-grained multi-mode AI video semantic data processing method, and belongs to the technical field of video processing and content management. The method comprises the following steps: carrying out multi-modal content analysis on an original video, and respectively extracting an image frame, an audio Mel spectrogram and a text transcription with a timestamp; respectively extracting high-dimensional feature vectors of vision 768 dimension, audio 128 dimension and text 384 dimension by using a pre-trained deep learning model; unifying the multi-modal features to second-level time granularity through feature dimension standardization and time axis alignment, and splicing to generate a 1280-dimensional comprehensive feature matrix; constructing an AI model comprising a bidirectional LSTM time sequence feature extraction component and a fine-grained classifier, and training and classifying fusion features; and finally outputting a video file with structured classification information to realize second-level fine-grained semantic recognition. The method solves the problems of low multi-modal information fusion efficiency, inconsistent time sequence, high manual analysis cost and the like, and is suitable for safety supervision and quality control in industrial scenes such as building construction, chemical engineering and the like.

The invention relates to the technical field of electronic display, in particular to an intelligent control method for the refresh rate of an electronic screen. According to the method, the refresh rate switching opportunity and amplitude are accurately controlled in advance by fusing double engines of real-time content analysis and foresight interaction prediction, so that the average refresh rate is reduced to the minimum on the premise of ensuring that any sudden interaction can obtain smooth response, and the unification of energy conservation and smoothness is achieved.

A protection module operates to analyze threats, at the protocol level (e.g., at the HTML level), by intercepting all requests that a browser engine resident in a computing device sends and receives, and the protection agent completes the requests without the help of the browser engine. And then the protection module analyzes and / or modifies the completed data before the browser engine has access to it, to, for example, display it. After performing all of its processing, removing, and / or adding any code as needed, the protection module provides the HTML content to the browser engine, and the browser engine receives responses from the protection agent as if it was speaking to an actual web server, when in fact, browser engine is speaking to an analysis engine of the protection module.

Systems and methods initialize distributed client device membership for new client devices joining a group-based communication channel established for content exchange among a plurality of distributed client device members. The systems and methods comprise aspects for executing a content analysis over the content of the one or more group-based communication channels to identify characterizing terms associated with each group-based communication channel. After the content analysis is performed, systems receive search parameters from a potential new member client device for a group-based communication server. Various systems and methods provide selected search results generated based at least in part on a determination that characterizing terms match one or more of the search parameters to the client device. Those selected results comprise a target that is executable by the client device to join the associated group-based communication channel.

The system according to this embodiment aims to perform sentiment analysis based on the user's behavior history and speech content, and to provide positive feedback and a virtual reality environment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a provision unit, and a re-experience unit. The collection unit collects the user's behavior history and speech content. The analysis unit performs sentiment analysis on the data collected by the collection unit. The suggestion unit makes suggestions to praise the user based on the analysis results obtained by the analysis unit. The provision unit provides a virtual reality environment based on the content suggested by the suggestion unit. The re-experience unit records the user's success experiences and goal achievements in the environment provided by the provision unit and allows the user to re-experience them.

This application provides an interactive verification method, apparatus, electronic device, and medium. The method includes: in response to receiving a verification instruction for a verification object from a user terminal, continuously receiving a multimodal verification data stream, including a real-time video stream, submitted by the user terminal; performing real-time content analysis on the real-time video stream to determine whether the video content meets the integrity verification conditions; if not, issuing a targeted correction instruction to the user terminal to guide the user to adjust the captured image until the video content meets the integrity verification conditions; and in response to the integrity verification conditions being met, generating a verification data set based on multimodal verification data stream segments extracted from the multimodal verification data stream, performing multi-dimensional authenticity verification on the verification data set, and generating an authenticity verification conclusion for the verification object. This application can improve the quality of material collection and review efficiency based on intelligent interaction, and ensure the high reliability of the conclusion based on multi-dimensional authenticity verification.

The invention discloses a video platform intelligent content recommendation method based on deep learning, and relates to the technical field of intelligent recommendation, and the method comprises the following specific steps: data collection, collecting user historical watching data including video attribute data, user behavior data and scene feature data to form a basic data set; video information density calculation: based on the collected video attribute data, extracting visual, audio and text multi-dimensional features by using a multi-modal deep learning model, constructing a video information density evaluation system, and converting video contents into quantifiable information density values; according to the method, the user cognitive load tolerance model is constructed by analyzing the video information density and the user behavior data, the acceptance capability of the user to contents with different information densities can be accurately grasped, the current cognitive load state of the user is dynamically judged in combination with the real-time scene information of the user, and the recommended content is accurately matched with the cognitive ability of the user.

The system according to this embodiment aims to provide advice and suggestions for improving the progress of meetings and the quality of discussions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects the content of the meeting. The analysis unit analyzes the content of the meeting collected by the collection unit. The proposal unit provides advice and suggestions based on the content analyzed by the analysis unit.

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.

The system according to the embodiment aims to enable children to effectively practice skills necessary for daily life. [Solution] A system according to an embodiment includes a selection unit, an analysis unit, and a response unit. The selection unit selects a practice content. The analysis unit analyzes the child's utterances based on the practice content selected by the selection unit. The response unit responds based on the utterances analyzed by the analysis unit.

The system according to the embodiment aims to accurately summarize the content of inquiries from customers and store them as a history. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a storage unit. The reception unit inputs the content of an inquiry from a customer. The analysis unit analyzes the content of the inquiry input by the reception unit and extracts important keywords and phrases. The generation unit generates a summary based on the keywords and phrases extracted by the analysis unit. The storage unit saves the summary generated by the generation unit as a history.