528 results about "Content analytics" patented technology

The present invention relates to a computer-implemented method and system for generating a video editing project using artificial intelligence (AI) and machine learning (ML) techniques. The method includes processing a collection of video clips to generate text-based metadata, receiving selection criteria to identify relevant video clips, and generating a natural language prompt based on the selection criteria. The prompt, comprising instructions and context, is provided to a large language model (LLM), which processes the input and outputs data for constructing a video project data model. The project data model includes timing data for salient snippets within the selected video clips. A dynamic and interactive web-based user interface is rendered to visually represent the project data model, offering a timeline view and editing tools for refining the video project. This system streamlines the video editing process by integrating AI-driven content analysis with user-directed editing, resulting in a tailored video project that aligns with user-defined thematic elements.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

HyperText Markup Language (HTML) content analysis (HCA) using machine learning is described. A feature vector schema may be generated based on domain names corresponding to HTML webpages and corresponding indications of a status of the HTML webpage. The schema may map each position in a feature vector of a given HTML webpage to a resource identifier. Information may be processed using the schema to generate respective feature vectors. The feature vectors may be used to train a model to generate risk indicators for HTML webpages. A potentially parked domain webpage or a potentially malicious domain webpage may be received. A feature vector for the webpage may be generated and inputted to the model. The model may generate a risk indicator for the webpage. The risk indicator may be output and may cause responsive actions. The model may be updated based on a determination indicating whether the webpage was a parked domain webpage or a malicious domain webpage.

The invention discloses a dialogue type enterprise knowledge question answering method and device, a storage medium and electronic equipment, and relates to the technical field of data analysis, and the method comprises the steps: obtaining user request information; obtaining a corresponding channel; reconstructing the question content to obtain the reconstructed question content; performing vectorization processing on the reconstructed question content to obtain vectorization information of the reconstructed question content; obtaining a plurality of first FAQ knowledge and second FAQ knowledge; obtaining a plurality of pieces of first document block knowledge and second document block knowledge, and obtaining documents corresponding to a plurality of document blocks; obtaining a corresponding prompt word template based on the dialogue intention and the channel; and constructing a large language model, and sorting to obtain corresponding answer information. Based on the question content of the user, the dialogue intention is obtained through analysis, the knowledge base is matched, the answer information is obtained through arrangement, the user question is automatically answered, and the method has the advantages of being simple, efficient and high in real-time performance.

The invention discloses a multi-agent automatic image retouching system based on content analysis, and the method comprises the steps: 1, receiving the input of an original image, calling a finely-adjusted multi-mode large model to carry out the combined semantic-visual analysis of the image, extracting key semantic elements in the image, and carrying out the recognition of the key semantic elements; comprising but not limited to background coordination degree, illumination condition, figure hair style definition, weather quality and composition layout rationality content information. Based on these information, the system performs comprehensive image quality scoring on the image, which combines image style, aesthetic level, definition and subjective quality multi-dimensional evaluation criteria. Meanwhile, based on a multi-dimensional image quality scoring result and understanding of image semantic content, the system automatically generates a repair and optimization strategy set for specific defects so as to guide a subsequent image quality enhancement process. The invention relates to the field of multi-modal model and multi-agent cooperation, and can meet the increasing requirements of rapid optimization and high-quality propagation of image contents.

The invention discloses a public information publishing platform and method, and relates to the technical field of information public facilities, and the method comprises the following steps: obtaining to-be-evaluated information and publisher data, and analyzing to generate a credibility score PA of an information source; performing sentiment analysis and keyword inspection on the to-be-evaluated information, identifying sentiment deviation and semantic contradiction in the to-be-evaluated information, and analyzing a credibility score PB of the generated content; and acquiring an external authoritative data source, comparing the external authoritative data source with a time consistency check algorithm by using a text similarity algorithm, judging the consistency of the to-be-evaluated information and the external authoritative data source, and outputting a consistency reliability index Pc. According to the method, the problems of low false information identification efficiency and poor accuracy in the prior art are effectively solved by combining multi-dimensional data processing such as information source credibility evaluation, content analysis, external authoritative data source comparison, propagation path analysis and the like.

The invention relates to the technical field of display, and particularly discloses an intelligent self-adaptive eye protection display system which sequentially comprises a content analysis module, an ambient light detection module, an eye protection parameter calculation module, a display parameter adjustment module and a user feedback module. The system identifies the screen content type and the dynamic degree in real time, synchronously detects the ambient light intensity and the color temperature, obtains the optimal combination of the blue light proportion, the brightness, the contrast ratio, the color temperature and the sharpness through a multi-target optimization model, and smoothly adjusts the display through driving; a user can score, finely adjust and mark a scene, and feedback data enters a self-learning engine to iteratively update a parameter weight so as to form a content-environment-user closed loop. The system can significantly reduce blue light harm and improve visual comfort without external hardware.

The invention discloses an AI-based classroom content analysis method, system and device, and a storage medium. The method comprises the following steps: acquiring classroom video and voice data in real time; identifying and judging a current classroom scene by using the first multi-modal fusion and scene identification model; according to a preset classroom scene-interaction object mapping rule, determining an interaction object correspondingly mapped by the current classroom scene; utilizing a second multi-mode fusion and effective interaction identification model to extract behavior characteristics of the student, and judging whether the current student behavior is effective interaction; counting the ratio of the effective interaction duration to the total detection duration, and calculating the classroom concentration of the students; based on the interaction quality evaluation model, performing weighted calculation to obtain an interaction quality index of the student; and automatically generating an analysis report about the class concentration level and the interaction quality level according to a preset level evaluation standard. According to the method, accurate quantitative analysis of the concentration degree and the interaction quality of the students in the classroom can be realized, and the classroom analysis report is automatically generated.

The invention relates to the technical field of content recognition, in particular to an Al-based video content analysis method and system, and the method comprises the following steps: carrying out the image segmentation learning based on a video sequence through employing a U-Net convolutional neural network, analyzing scenes and elements in a video frame, recognizing and isolating key visual elements in a video through network learning, and carrying out the recognition of the key visual elements in the video. Comprising objects and figures. According to the method, image segmentation learning is carried out by adopting the U-Net convolutional neural network, key visual elements in the video can be identified and isolated more accurately, a clearer basis is provided for follow-up scene change and key event tracking, video content analysis is carried out by applying the graph neural network, the identification capability of dynamic scenes and events is enhanced, and the identification efficiency is improved. Deeper structured understanding is provided for video content indexing, the video quality is analyzed through a structural similarity index evaluation method, and the reason of visual quality reduction can be accurately recognized and improved.

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.

A system (100) for AI-driven real-time optimization of advertising placement in dynamic media streams, comprising: (a) a media stream ingestion module configured to receive and pre-process live or continuously streamed content; (b) a content analysis and context understanding module operatively coupled to the recording module and configured to analyse the media content in real time using natural language processing, computer vision and audio signal analysis; (c) an audience profiling and behaviour tracking module configured to generate dynamic viewer profiles based on demographic, behavioural and contextual data; (d) an AI-based ad matching and ranking engine configured to select and prioritize ads based on content relevance, audience profile match, and predicted engagement; (e) a real-time decision and placement engine configured to insert selected advertisements at contextually appropriate points in the media stream without interrupting the viewer experience; and f) a feedback and analytics module configured to track ad performance and update AI models for continuous optimization of future ad placements.

A social networking system is provided. The system encompasses a robust processing subsystem hosted on a server, comprises various modules including a registration module linking social media accounts, a blockchain-backed bio page module storing audience metrics, a payment gateway with a reward system, and a CRM module offering dashboards for influencers, brands, agencies, and followers. An analysis module integrates natural language processing to assess content tone, engagement, and sentiment. A button module generates emoticon categorization and auto-suggestions based on metadata, leveraging machine learning for predictions. A compliance analysis module ensures adherence to regulations in paid social media content, and a royalty distribution module calculates and allocates royalties based on engagement, location, compliance, and fan type via smart contracts, This comprehensive subsystem integrates functionalities to elevate user interaction, content analysis, and monetization opportunities within the social media platforms.

The invention relates to an image steganography method. The method comprises the following steps: carrying out content analysis on an original image serving as a carrier; generating a self-adaptive embedding strategy according to an analysis result, and encoding the embedding strategy into a compact binary format; embedding to-be-embedded data into different areas and channels of the original image in a layered manner according to an embedding strategy to obtain an embedded image; the embedded data is extracted from the embedded image by decoding the embedding policy and mapping reconstruction. According to the method, a region self-adaptive embedding strategy is formulated, the embedding capacity is maximized on the premise of keeping the visual quality of the original image, and the hidden data detection risk is reduced.

The invention discloses a video generation method, system and device based on a large language model and a medium, and the method comprises the steps: obtaining product information inputted by a user, the product information comprising the name, description and selling point of a product; preprocessing the product information, and carrying out semantic information split processing on the preprocessed product information through a large language model to obtain split description information corresponding to the product information; the method comprises the following steps: acquiring an original video, and performing video clip segmentation on the original video to generate a plurality of video clips and picture description information corresponding to the video clips; semantic matching processing is carried out on the sub-mirror description information and the picture description information, and a video clip with the highest similarity with each piece of sub-mirror description information is obtained through matching; and splicing the video clips according to the sequence of the sub-mirror description information to generate a complete video. Compared with the prior art, the product video can be automatically and efficiently generated by integrating natural language processing, video content analysis and an intelligent matching algorithm.

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.

The invention discloses a phishing mail detection method and system based on a large model, and relates to the technical field of network security, the system comprises a real-time detection system and an offline analysis system, and a mail content analysis module receives real-time mail data to form structured data; the detection rule center matches the structured data with a detection rule, directly intercepts a mail hitting a high-confidence blacklist rule, and transmits the structured data of a mail hitting a low-confidence blacklist rule into a phishing mail detection agent; the phishing mail detection agent sequentially performs mail header suspicious feature analysis, mail body semantic analysis, mail body structure analysis and attachment content analysis on the structured data to obtain a mail intention, a link and an attachment file; and performing corresponding analysis by combining a detection tool, and judging whether the mail is a phishing mail or not according to an analysis result. A large-model-driven phishing mail detection intelligent agent is utilized, and a detection path is dynamically planned to cope with endless phishing mail attack means.

The invention discloses a video content analysis and post-production optimization method and system based on cloud computing, and relates to the technical field of video processing, and the method comprises the following steps: in a video content analysis stage, obtaining a video through a data interface, dividing the video into frames, analyzing the character action change, forming an action event stream, and storing the action event stream in a database; extracting video content dimension features at the same time; constructing a video content comprehensive feature scoring model, and obtaining a comprehensive feature score through weighted summation in combination with the dimension features of the video content; in the later production and adjustment stage, the performance of the video in each dimension is evaluated based on the comprehensive scoring model, and the video content is adjusted according to the evaluation result and the video clip; according to the method, the frame number meeting the set matching degree threshold condition in the video frame is calculated, the proportion of the frame number higher than the threshold value and the frame number lower than the threshold value is analyzed, when the proportion meets the preset condition, whether the video content needs to be further adjusted in the post-production stage or not is marked, and video content analysis and post-production optimization are achieved.

Aspects of the subject disclosure may include, for example, obtaining current video content of a player repeatedly performing a physical skill, analyzing the current video content based on previous video content, the previous video content comprises other video content of the player repeatedly performing the physical skill, determining biomechanic metrics of the player performing the physical skill based on the analysis, and determining each biomechanic metric of a portion of the biomechanic metrics does not satisfy a respective biomechanic metric success rate. Further embodiments include generating a first image of the player performing the physical skill from the current video content, generating a second image of the player performing the physical skill from the previous video content, and presenting the first image and the second image simultaneously and indicating the portion of the biomechanics that did not satisfy the respective biomechanic metric success rate. Other embodiments are disclosed.

The invention discloses a text travel content analysis method and system based on semantic recognition, and relates to the technical field of text travel data intelligent analysis, and the method comprises the steps: obtaining a multi-source UGC text travel material set, and constructing a grid time-space domain module set according to a target region grid set and a continuous historical time window; each multi-source UGC text travel material is distributed to a corresponding grid time-space domain module; extracting a grid text semantic center vector of the grid time-space domain module, and calculating a semantic drift amount of the target area grids so as to screen at least one candidate hot spot area grid from each target area grid; and calculating a corresponding grid visual semantic center vector, and calculating a cross-modal semantic similarity in combination with the second grid text semantic center vector of the candidate hot spot region grid to screen a target hot spot region grid, and generating a travel destination recommendation list. Therefore, the method can quickly focus on geographic areas with active recent contents and increased discussion popularity, and is beneficial to exploring micro-destinations of newly-emerging tourism hotspots.

The invention discloses an outdoor intelligent art display system based on multimedia interaction, and relates to the technical field of digital art display and intelligent control, and the system comprises an audience behavior collection module which is used for collecting audience staying time and behavior response in real time, obtaining a content attraction initial evaluation result, calculating an attraction index of each content unit, and outputting the attraction index of each content unit; the content analysis and screening module is used for extracting alternative multimedia content units from a preset content library, judging the theme relevance between alternative contents and the currently displayed contents, and obtaining a candidate content set meeting replacement conditions; according to the outdoor intelligent art display system based on multimedia interaction, intelligent replacement and updating of multimedia content are realized on the premise of not interrupting display, content combination is automatically optimized through audience feedback data, content with low attraction is eliminated, and welcome elements are enhanced.

The invention provides an academic viewpoint extraction method and system applied to academic literatures, and aims to solve the problem that viewpoint information in the academic literatures is difficult to automatically and accurately extract in the prior art. Firstly, an original data set of the academic literatures to be analyzed is obtained, and structure standardization preprocessing is carried out; then, a pre-trained viewpoint feature extraction model is called to perform semantic feature extraction on the preprocessed literature; then, through viewpoint detection processing, generating a viewpoint detection result of each document, including a text position mark of the academic viewpoint to be extracted and a viewpoint content summary; and finally, generating an academic viewpoint extraction result containing a viewpoint content summary and a corresponding text position mark, and outputting the academic viewpoint extraction result to a target analysis terminal to support academic content analysis, thereby realizing automatic and accurate extraction of academic viewpoints, and being beneficial to improving the efficiency and quality of academic research.

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 provides a value-oriented content analysis and intervention method in ideological and political education, and belongs to the technical field of education and teaching. A pre-trained multi-modal value understanding model is combined with a recurrent neural network to extract dominant value features and capture context dependence, implicit value expressions are recognized through an attention mechanism and comparative learning, and a tendency index is generated based on the similarity between feature vector calculation and core value. A reinforcement learning training content intervention agent is utilized to generate an education intervention strategy, and finally anti-fact reasoning is adopted to evaluate different intervention strategy effects and optimize an intervention scheme, so that a complete technical closed loop from content analysis to intervention implementation to effect evaluation is formed. The technical problem that the implicit value in the ideological and political education content is difficult to quantify to accurately identify and implement effective intervention is solved.

The invention discloses a short video traffic anomaly analysis system based on big data analysis, and relates to the technical field of big data analysis. The system comprises a data acquisition and preliminary screening module, a user behavior analysis module, a comment content analysis module, a social relation analysis module and an abnormal user identification and auditing module. The data acquisition and preliminary screening module acquires flow data and user behavior data of a to-be-analyzed short video, and screens out a target user and behavior data thereof according to a preset rule; the user behavior analysis module analyzes a behavior mode of a target user in a selected time period and performs singleness scoring; the comment content analysis module extracts comment information of a target user, and evaluates the conformity of the comment information and the short video content through keyword extraction and semantic analysis; the social relation analysis module constructs a target user social relation graph and analyzes the social complexity of the target user; and the abnormal user identification and auditing module identifies and audits the abnormal user.

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.

The invention provides an intelligent question-answering system and an intelligent question-answering method based on a large model. In the system, an intention recognition module is used for receiving a natural language question input by a user, and semantic recognition is performed on the natural language question by a large language model to obtain an intention recognition result; the SQL conversion module is used for obtaining a structured SQL query statement corresponding to the intention recognition result; the data retrieval module is used for performing retrieval in a database by utilizing the SQL query statement obtained by the SQL conversion module to obtain a retrieval result; the content analysis module is used for analyzing the retrieval result obtained by the data retrieval module by using a deep learning technology so as to obtain an analysis conclusion, and the analysis conclusion comprises key information and a mode corresponding to the key information; and the result display module is used for displaying the retrieval result and the analysis conclusion to the user in a visual form. According to the method, the user can retrieve the information in the database by using the intelligent question and answer system without needing to have SQL (Structured Query Language) knowledge.