3687 results about "Content generation" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a content generation and optimization method based on a knowledge graph, which comprises the following steps: constructing a multi-source knowledge database, extracting core concepts and knowledge contents, and constructing the knowledge graph. Performing semantic analysis to generate semantic vector representation and a keyword list; retrieving the associated text fragment based on the semantic vector and the keyword list, and inputting the associated text fragment into a generation model to generate initial answer content; and utilizing the knowledge graph to match the domain entity and the knowledge graph node, generating a logical reasoning path, optimizing the initial answer content, and generating the final answer content. According to the method, content generation of accurate retrieval, deep knowledge association and logical reasoning enhancement is realized by fusing a multi-source knowledge database, knowledge graph reasoning and generation optimization; semantic vector matching and keyword retrieval are combined, so that the accuracy of knowledge acquisition is improved; and through knowledge graph reasoning path construction, the answer logic is coherent.

The invention discloses a multi-element sales planning agent system and method, and aims to improve the intelligence and precision of sales planning. The system comprises a collection module, an analysis module, an optimization module, a creation module and a generation module. The collection module is used for receiving multi-modal data such as marketing targets and extracting key marketing elements. The analysis module is used for generating a target user portrait and extracting marketing strategy analysis data. And the optimization module is used for calculating a medium putting weight by utilizing reinforcement learning and generating a medium strategy scheme. And the creation module generates a propagation theme and marketing content by adopting a generative artificial intelligence technology. And the generation module predicts a delivery effect by using a machine learning model and dynamically optimizes a medium strategy and a content scheme. Through multi-modal data fusion, intelligent analysis and optimization, closed-loop processing from data acquisition to marketing execution is realized, the marketing decision-making efficiency is improved, and brand promotion accuracy and market adaptability are enhanced.

A system and method for AI based user experience curation across multiple scenarios and finite time horizons of interest. The present invention integrates connectionist and symbolic AI techniques to generate coherent, relevant, and personalized content across various domains. The invention bridges the gap between connectionist AI and symbolic AI, enabling more adaptive, immersive, and engaging user experiences while prioritizing security and traceability and contextualization considerations behind recommendations or content generation. The platform furthers dynamic and tailored user interactions with digital systems across individual interactions, preferences, sequences and ongoing engagements across sessions by leveraging the power of analytics, deep learning, and AI to create truly intelligent, dynamic, and responsive user experiences enhanced with optimization and planning faculties.

A system and method for creating complex, immersive, and interactive digital content is disclosed. The system integrates advanced artificial intelligence, multi-modal input processing, cloud-based shared environments, and immersive hardware to generate, optimize, and deliver rich interactive experiences. The platform supports content mashups, custom scenario generation, and adaptive AI behaviors, enabling the creation of unique and engaging digital environments across various media formats.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health, poster design and the like, and discloses a visual sense generation method and device based on semantic association modeling, equipment and a medium. Generating a demand text containing theme and style parameters; semantic features in the demand text are extracted, semantic association weights are constructed, and element layout coordinates are optimized in combination with spatial distribution constraints; and encoding the layout information into a control matrix, fusing the control matrix with the initial noise, adjusting a noise reduction process through an encoding and decoding network, and generating target visual content highly matched with the semantic meaning of the user instruction. According to the method, the layout optimization function is constructed, the diffusion model is guided to focus the semantic salient region in space, language model output and the visual generation process are closely combined, structured response and space mapping of user semantic requirements are achieved, and the expression consistency and personalized adaptation capacity of visual content generation are improved.

The invention relates to the technical field of AIGC content generation, discloses an AIGC content generation method and system based on multi-modal fusion, and aims to solve the problems of decentralization, low efficiency and insufficient originality of a traditional content generation tool. Multi-modal data such as texts, images, videos and audios are integrated, user intentions are analyzed in combination with intelligent retrieval and a domain knowledge base, automatic generation from multi-modal input to high-quality creative content is achieved, a cross-modal collaborative generation technology is adopted, semantic features are dynamically aligned, and logically coherent content is generated. The content emotional value is enhanced through an emotional analysis and dynamic optimization strategy, the homogenization bottleneck is broken through, meanwhile, an automatic quality evaluation and format adaptation mechanism is integrated, deep application of scenes such as text travel, advertisement, e-commerce and interactive network television service is supported, marketing copywriting, short videos and cross-platform distribution schemes can be efficiently generated, and the market competitiveness is improved. And the content production efficiency and the creativity transmission are obviously improved.

The invention discloses a dynamic visual content collaborative generation method based on a cognitive logic chain, and belongs to the technical field of visual content generation, and the method comprises the following steps: S1, user intention analysis and data input; s2, dynamically constructing a cognitive logic chain; s3, intelligent scheduling of the multi-modal generation module; s4, cross-modal content collaborative generation is carried out; s5, collaborative editing and real-time feedback are carried out; s6, iterative optimization of logic chain driving; s7, multi-dimensional quality evaluation: constructing an evaluation matrix containing semantic consistency, visual attraction and user participation degree, predicting a content propagation effect in combination with a deep learning model, and generating a quantitative improvement suggestion report; and S8, updating the self-adaptive knowledge reversely marking the cognitive logic chain according to the finally adopted content version, extracting a new association rule, and injecting the new association rule into the rule base. Through deep semantic analysis and dynamic logic chain construction, the system accurately captures a core creation target of a user and converts the core creation target into an executable visual strategy.

A three-dimensional preview of content can be generated and presented at an electronic device in a three-dimensional environment. The three-dimensional preview of content can be presented concurrently with a two-dimensional representation of the content in a content generation environment presented in the three-dimensional environment. While the three-dimensional preview of content is presented in the three-dimensional environment, one or more affordances can be provided for interacting with the one or more computer-generated virtual objects of the three-dimensional preview. The one or more affordances may be displayed with the three-dimensional preview of content in the three-dimensional environment. The three-dimensional preview of content may be presented on a three-dimensional tray and the one or more affordances may be presented in a control bar or other grouping of controls outside the perimeter of the tray and / or along the perimeter of the tray.

The invention discloses a robot anthropomorphic interaction method based on multi-modal emotion recognition and customized portrait generation. The method comprises the following steps: S1, dynamically fusing multi-modal emotions; the method comprises the following steps: S1, synchronously acquiring voice, visual and text signals through a multi-source heterogeneous sensor, capturing a user voice stream by a high-fidelity microphone array, and extracting acoustic characteristics such as intonation and speed, S2, performing cross-modal reasoning; s3, synchronously generating contents; step S4: style migration; step S5, anthropomorphic voice and expression generation; according to the method, man-machine interaction emotion is analyzed and generated by utilizing a large language model and multi-modal information fusion, the singleness of interaction emotion and the deficiency of emotional sharing ability are avoided, a strong emotion interaction characteristic is achieved, the image of the robot is obtained through a generative technology and can be migrated to any image, the limitation that a specific image is independently made is broken through, and the interaction effect of the robot is improved. The advantage that one robot can be suitable for different scenes is achieved.

The invention belongs to the field of natural language processing, particularly relates to a multi-modal document generation method and device based on multi-agent collaboration, and aims to solve the problems that an existing method is low in intention recognition accuracy, limited in retrieval range and not professional enough in content generation. The method comprises the following steps: generating a structured template; analyzing the text input by the user to identify a writing intention, and determining a target template; vectorizing each candidate resource feature to obtain a corresponding sparse vector, a dense vector and a knowledge vector, and performing semantic alignment; extracting context features of the input text, respectively performing multi-path retrieval recall, evaluating and sorting recall results, and screening out target features; and constructing a thinking chain in combination with the knowledge graph, and generating a multi-modal document according to the target template. According to the method, the outline structure can be extracted, the picture / table style can be recognized, the templates adaptive to different document types can be dynamically generated, and full-process automation from user input to document output is achieved.

A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.

An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.

The invention relates to the technical field of machine vision, and discloses a machine vision-oriented cross-modal image-text analysis method, which comprises the following steps of: partitioning an input image to generate an image block sequence; inputting the image block sequence into a visual converter for multi-scale feature extraction, and generating target visual features; encoding the input text to generate a target text feature; inputting the target visual features and the target text features into a deep reconstruction bottleneck network for compression alignment, and generating a cross-modal compression vector; and inputting the cross-modal compression vector into a large language model to generate cross-modal decoding information, so that cross-modal redundant information can be effectively filtered, compact shared semantic representation can be learned, the information integrity of the compression process is ensured through bidirectional reconstruction verification, cross-modal semantic alignment is realized, and the method has the advantages of high efficiency and high reliability. Omnibearing cross-modal content generation from the whole to details is achieved, and the requirements of different application scenes are met.

The invention discloses an intelligent auxiliary teacher lesson preparation system and method based on an education large model. The method comprises the following steps: S1, generating a standardized teacher lesson preparation data set; s2, constructing a teaching knowledge graph; s3, generating preliminary intelligent lesson preparation content; s4, presenting the preliminarily generated intelligent lesson preparation content, and providing an interactive interface combining a natural language and voice recognition; s5, generating the adjusted intelligent lesson preparation content; and S6, according to historical lesson preparation data of the teacher and the interactive feedback data, performing adaptive learning and optimization on the intelligent lesson preparation content, adjusting weights of key knowledge points in the teaching knowledge graph, updating a content generation strategy of the large education model, and generating a final intelligent lesson preparation scheme. According to the method, the response capability of the intelligently generated content to the teaching intention of a teacher is remarkably improved, the teaching content and the test question style are supported to dynamically cooperate with classroom feedback along with the teaching rhythm, and an intelligent decision support basis is provided for large-scale personalized teaching.

The invention relates to the technical field of natural language processing and intelligent content generation, and discloses an automatic generation method and system for a 3D scene interaction script based on a large language model.The method comprises the steps that input character description is obtained, role and event information is extracted through semantic analysis, and a multi-role interaction graph is constructed to obtain a preliminary script branch; calculating the matching degree of the node and the world view, and optimizing the path and the node attribute when the matching degree is low; logic verification points are extracted to verify the continuity of the plot; embedding role emotion to generate initial plot segments; and optimizing content connection and dialogue rhythm, and fusing plot promotion elements to output a final script. The method realizes automation and high quality of script generation, ensures plot coherence, logic compliance and natural emotion, and improves the immersion experience of the user.

The invention relates to the technical field of artificial intelligence and machine learning, in particular to a government affair industry intelligent information retrieval and pushing system and method based on a large model, and the system comprises an intelligent semantic understanding and query analysis module, a semantic-driven efficient retrieval module, a generation-enhanced intelligent content generation module, and a personalized pushing and feedback optimization module. The method has the beneficial effects that a natural language query request input by a user is received through the intelligent semantic understanding and query analysis module, semantic analysis and intention recognition are performed by utilizing a pre-trained large language model, key information is extracted, and query logic is optimized through a context sensing mechanism. Then, a semantic-driven efficient retrieval module quickly retrieves document fragments most relevant to user query from mass data of government affair cloud, precise matching is achieved through semantic vectorization and an efficient vector retrieval technology, and a retrieval strategy is dynamically optimized in combination with user feedback.

The invention relates to the technical field of agent construction, and discloses an agent construction method and system based on agentive workflow, and the method comprises the following steps: defining an agent core function module and distributing a unique identifier, building an inter-module communication protocol standard, and determining a message format and a priority rule; the method comprises the following steps: distributing computing resources for each module, setting an elastic capacity expansion and contraction strategy, generating a module dependency graph and an architecture metadata file, detecting the loss and delay of multi-modal input data, generating compensation characteristics based on historical context, calculating the quality confidence of each modal, and carrying out dynamic weighted fusion. According to the invention, a three-level alignment architecture is provided to realize cross-modal semantic consistency, and the generated content is ensured to accord with real scene logic; a double-layer decision-making mechanism is designed to drive workflow dynamic optimization, and the system agility under a complex task is remarkably enhanced; a privacy-efficiency balanced federated architecture is constructed, and differential content generation is supported while enterprise data sovereignty is protected.

Methods, systems, and computer programs are presented for generating personalized content. One method includes an operation for identifying audience parameter values that indicate which users are members of an audience. The method further includes operations for determining attribute values for attributes used to generate items based on the audience parameter values, and generating items for the audience based on the attribute values. Generating each item comprises creating a prompt based on a type of the item, the attribute values, and the audience parameter values; selecting a generative artificial intelligence (GAI) tool to generate the item based on the type of item; and providing the created prompt to the selected GAI tool. The method further includes causing presentation of the generated items in a user interface (UI), receiving on the UI a selection of one of the items; and transmitting the selected item to one or more members of the audience.

The invention discloses a new media-oriented interactive marketing method, which comprises the following steps of: 1, multi-source data acquisition: acquiring behavior data of a user in social media, a short video platform and an e-commerce scene in real time through an API (Application Program Interface), a burying point technology and cross-platform data crawling, comprising click tracks, staying duration, comment content, sharing behaviors and shopping cart operation; through the technical innovation of a dynamic content engine, a real-time feedback closed loop, edge calculation, reinforcement learning, a multi-modal material library, a GAN automatic synthesis technology, federated learning, a block chain and the like, the core benefits of improving the interactive conversion efficiency, data-driven real-time decision, reducing the content generation cost, balancing the safety and the effect, performing scene-based accurate touch and the like are realized; the user conversion rate, the click rate, the marketing response speed, the negative comment processing efficiency, the portrait accuracy and the target user coverage rate are remarkably improved.

The invention relates to the technical field of artificial intelligence and natural language processing, and particularly discloses a text content generation method based on artificial intelligence, and the method comprises the steps: obtaining natural language text input, and extracting a semantic recognition feature vector; acquiring context state data and encoding the context state data into a state recognition feature vector; generating a fusion feature vector containing a semantic and state association relationship through fusion analysis; constructing a causal discrimination model based on the fusion features, and outputting the matching confidence of semantics and states; dynamically adjusting a generation strategy according to the confidence coefficient, if the matching degree is high, generating a standard text, otherwise, triggering an error correction mechanism to output a corrected text; and finally, performing logic consistency verification on the generated text to ensure that physical constraints, technological procedures and safety standards in the industrial field are met. According to the method, by introducing multi-level feature fusion, causal reasoning, intelligent error correction and rule verification mechanisms, context perception and safety controllability in the text generation process are achieved.

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.

An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a search criteria from the interaction content, retrieving a subset of vectors from a plurality of vectors stored in a vector database based on the search criteria of the interaction content, wherein the subset of vectors includes previous interaction content with the service provider, generating a response for the communication session based on execution of a large language model (LLM) on the subset of vectors, and outputting the response to at least one of the source device and the service provider device during the communication session.

The invention discloses a private domain data-based user portrait generation method and a private domain data-based content generation and touch method, and relates to the technical field of big data processing and pushing. Analyzing user behavior characteristics and interest preferences based on the collected data; marking static tags for the corresponding users based on the basic attributes; according to the behavior characteristics and interest preferences, constructing a primary dynamic tag; through user behavior data collected in real time, a time decay weighting algorithm is adopted to improve recent behavior weight and filter abnormal click data, a time sequence cross attention network is adopted to model a behavior feature sequence, interest preference and probability are output, and a dynamic threshold value is set to trigger dynamic label updating; and if the key behavior data of the user is collected, recalculating the dynamic tag based on the key behavior data and performing full-amount refreshing. According to the invention, a data-content-touch real-time linkage closed-loop system is constructed.

The invention discloses an accurate international communication digital human real-time dialogue method fused with a multi-modal technology. The method comprises the following steps: S1, constructing a digital human image and tone; s2, propagation content generation and problem guidance; s3, semantic analysis and intention clarification based on the real-time voice dialogue; s4, geographic preference modeling and path planning; s5, cross-context propagation content generated based on retrieval enhancement is generated; s6, visually displaying the propagation content; and S7, carrying out digital human-driven multi-language propagation content real-time output and feedback closed-loop optimization. Through accurate utterance expression analysis, accurate international propagation problem recommendation is provided, cross-context propagation content generation based on semantic understanding is realized, digital people with voice features and visual images are constructed, real-time dialogue interaction of users is realized, and user experience is improved. The system can carry out geographic modeling according to the region where the accurate problem is located, language preference and propagation object culture characteristics, and differential propagation path planning is achieved.

The invention relates to the technical field of multi-modal large language models, in particular to a government affair service content navigation method and system based on a large language model, and the method comprises the following steps: receiving multi-modal information input by a user through texts, voices or pictures; the voice is converted into a text, and character information in the picture is analyzed by using an OCR (Optical Character Recognition) technology; fusing multi-modal data, and inputting the fused multi-modal data into a large language model for semantic understanding and context association analysis; matching items are retrieved in combination with a local government affair knowledge base, and an initial recommendation list is generated; the recommendation result is displayed through the intelligent assistant, and interaction optimization options are provided; the method has the beneficial effects that the current government affair service content navigation mode is optimized through the multi-modal capability, the retrieval enhancement capability and the content generation capability of the large language model, so that the navigation is more modal and more intelligent, and meanwhile, the privacy and authority of data reply are ensured.

The invention discloses a patent disclosure auxiliary writing method and system based on multi-agent collaboration. The method comprises the steps that a multi-agent system composed of a collaboration agent, an agent agent and an expert agent is constructed, a knowledge base and a decision module of each agent are configured, and a collaboration architecture of a task scheduler, an arbiter and a shared information space is established; the method comprises the following steps: receiving an original technical scheme of a user, and constructing a structured intermediate representation through multi-agent parallel semantic analysis and feature extraction; based on the representation, a writing task sequence is generated through dynamic task decomposition, intelligent agent division is coordinated through a contract network protocol, and content generation and verification are completed based on a multi-round debate mechanism; performing multi-dimensional consistency detection on the generated draft, and eliminating logic conflicts and expressing defects through iterative revision; and finally, an interest portrait is generated based on the user technical scheme and the optimized draft, interest retrieval is performed, a technical content report is output, and the efficiency and quality of disclosure book writing are improved.

The invention discloses a product package planning-oriented AI-driven full marketing content design method, and relates to the technical field of marketing content design, and the method comprises the following steps: obtaining a product package design draft, extracting color, composition, font and semantic features based on a deep convolutional neural network, and carrying out the fusion to form a brand tonality mother vector of brand style tonality; calling a multi-modal analysis module, extracting visual and language features consistent with brand tonality vector dimensions from multi-modal marketing materials published by a brand recently frame by frame or sentence by sentence, and generating a marketing material feature vector set; according to the method, the brand tonality mother vector is constructed as the style anchor point, and the multi-modal material analysis, the time-sensitive style evaluation mechanism and the self-adaptive style jump strategy are combined, so that the content style consistency is monitored in real time, the capability of dynamically regulating and controlling the style according to the content fatigue risk is realized, and the real-time monitoring of the content style consistency is realized. And the content generation system is ensured to intelligently sense the change of the propagation environment and make a strategic response.

The invention discloses a large model generation content traceability technology based on model copyright ID watermark embedding, which comprises the following steps that: firstly, a large model content generator compiles a dynamic watermark embedding process into an arithmetic circuit, and generates a watermark text and a proof by using a zero-knowledge proof algorithm; then the large model content generator publishes the text with the watermark and the proof together, and hides the copyright ID and the secret key; a large model content user verifies the proof and the watermarked text through a smart contract by adopting a zero-knowledge verification algorithm; after verification is passed, an adversarial sample corresponding to the text with the watermark is generated, an adversarial training method is used for optimizing the text with the watermark, and parameters of the Viterbi balance algorithm are dynamically adjusted and improved. According to the method, the concealment and the generation quality are balanced, high-capacity watermark embedding is realized, the tamper resistance and the accurate traceability of multi-model copyright disputes are improved, and the privacy protection intensity is improved.

Predicting the salience of one or more data entities to a particular (target) data entity from among a plurality of data entities may comprise generating a graph of the plurality of data entities and a machine-learned model architecture that predicts the salience of the one or more data entities output by the machine-learned model architecture using the graph. For example, the machine-learned model architecture may comprise a first machine-learned model for generating an embedding using the content of the target data entity, a second machine-learned model for generating a vector using the data type indicated by the target data entity, and a third machine-learned model (e.g., a graph neural network or other feed-forward neural network) for generating a contextual representation of the target data entity to which other contextual representations associated with the plurality of data entities may be compared (e.g., using Euclidean distance, cosine similarity, dot product).