2789 results about "Content generation" patented technology

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.

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 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.

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.

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 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 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.

The invention discloses a self-adaptive NL2SQL method and system based on a large language model and retrieval enhancement. The method comprises four key steps of mode linking, example enhancement and SQL generation, multi-path thinking chain fusion reasoning and multi-round self-correction. The system comprises the following modules: a knowledge base management module, an input analysis and preprocessing module, a mode link module, an example enhancement module, an SQL (Structured Query Language) generation module, an SQL fusion and optimization module, an SQL execution and feedback module, a multi-round self-correction module, a Prompt construction and context management module, a system interface module and a content generation module. The method has the advantages that multiple large models are supported, a data source is quickly accessed, a user-defined prompt structure is configured, good engineering maintainability and scene adaptability are achieved, and SQL generation accuracy, performability and universality are improved.

The invention discloses a multi-knowledge-base scheduling routing method based on intention recognition and feedback optimization, and the method comprises the steps: S1, receiving multi-modal input from a user, carrying out the deep semantic analysis of the input content through natural language understanding and multi-modal analysis, and outputting a standardized intention label; s2, constructing a capability portrait for each knowledge base, and then generating a route matching score matrix according to a similarity relationship between the user intention tag and the capability portrait of each knowledge base; s3, screening out one or more most relevant candidate knowledge bases according to the scoring matrix and a set matching threshold value, and selecting a retrieval combination strategy to realize information recall; s4, information retrieval is executed based on the selected knowledge base, recall results and original user input are submitted to a large language model for content generation, and answers or suggestions are formed and output; and S5, user feedback collection and strategy optimization are carried out. According to the method, a more accurate, efficient and self-evolutionary knowledge base scheduling mechanism can be realized.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for content generation are provided. One of the methods includes receiving one or more user input identifying information associated with one or more media elements and one or more characteristics of the video to be generated; and generating video content based on the received one or more user inputs, the generating comprising: identifying assets to include in the video, the assets including an avatar, generating a script for the video, and assembling a video layout.

The invention relates to the technical field of communication, and discloses a communication state monitoring management method and system. The method comprises the following steps: acquiring a transmission data packet in a communication network in real time, extracting protocol header information and load content of the data packet, and generating an original monitoring data sequence containing a protocol type, a timestamp and load characteristics; and performing multi-dimensional correlation analysis on the original monitoring data sequence, identifying a logical relationship between protocol header information and load content, detecting an abnormal correlation mode, and generating an abnormal event mark set with a confidence score. And constructing an adaptive behavior model based on the original monitoring data sequence and the abnormal event mark set, and capturing normal behavior characteristics of the communication network by dynamically updating model parameters. Transmitting data packets collected in real time are input into the self-adaptive behavior model for deviation degree calculation, abnormal data packets exceeding a deviation threshold value are screened out, and historical abnormal event mark sets are associated to generate abnormal context records.

The invention discloses a marketing content generation method and system based on big data, and relates to the technical field of big data processing and artificial intelligence, and the system comprises a multi-source data collection and fusion module which is used for receiving original multi-source data from different behavior sources, content sources and business scene sources, and performing structured processing, time relationship coordination and feature fusion on the original multi-source data to generate a fused portrait data set for describing the relationship between the user and the product. In the invention, through the multi-source data acquisition and fusion module, deep integration of heterogeneous multi-source data and unified representation of multi-modal features are realized, a high-quality data basis is provided for subsequent knowledge graph construction and content generation, and the problems of isolated island and insufficient feature utilization of traditional marketing data are solved; through a ternary knowledge graph construction module, information such as users, products, scenes and the like is constructed into a structured knowledge graph, and the semantic understanding ability and relevance of content generation are greatly enhanced.

The invention relates to the field of document processing, and particularly provides a document format processing method, system, terminal and medium based on a large language model.The method comprises the steps that a generation instruction containing a target document type and a source material are received, and original text content is generated through the large language model integrating domain knowledge; obtaining a matched structured format template, and analyzing the style rule into a format instruction set; identifying logic elements and hierarchical relationships thereof in the original text based on a natural language processing technology; performing association mapping on the format instruction and the logic element, and performing automatic style rendering by calling a document object model interface to generate an intermediate document with a standard format; and finally, outputting after quality verification. According to the method, an automatic process of content generation and intelligent formatting is constructed, and the document writing efficiency and normalization are improved.

The invention discloses a creation content generation system based on image recognition and large language model fusion, and particularly relates to the technical field of creation content generation, and the system firstly completes the fact extraction and brand anchor point construction of an input image in a unified coordinate and scale system, and forms a structured fact package in one-to-one correspondence with an original image; then, performing protagonality scoring and ambiguity gating on the figure instance, and outputting an explainable and calibratable protagonality judgment result; on this basis, the condition controlled generation and template selection module converts the fact constraint into a controlled text packet and a format instruction packet, and keeps explicit mapping with a fact packet; the system further executes cross-modal consistency and compliance verification based on image facts, and machine-readable verification and minimum cost correction are carried out on text and graph entities, geometrical relationships and brand elements; and finally, solidifying the key intermediate quantity, the parameters and the judgment basis into an evidence chain through a chain type index, and introducing online adaptive learning in a compliance boundary to realize mild updating and rollback release.

The invention discloses a document generation method based on agent collaboration, which comprises the following steps: acquiring initial document data through a content collection agent, and standardizing the initial document data to obtain input content; the input content is distributed to different intelligent agents; performing text generation on the standardized document data through a content generation agent and a multi-task parallel processing mechanism to obtain preliminary document content; performing structure planning coordination on the preliminary document content according to framework requirements through a structure planning agent to obtain a structured document draft; performing language retouching optimization on the structured document draft through a language retouching agent according to a retouching requirement to obtain retouched document content; and optimizing the different intelligent agents, integrating output results of the optimized intelligent agents to obtain final document content, and storing the final document content to obtain final document output.

The invention provides a vertical field data construction method based on a large model, which belongs to the technical field of data processing and artificial intelligence, and comprises the following steps: converting a vertical field source document into an intermediate format text, and segmenting the intermediate format text into a plurality of text blocks; inputting the text blocks into a pre-trained generative language model, guiding the pre-trained generative language model according to pre-designed cue words to generate a plurality of candidate questions according to the content of the text blocks, and performing preliminary screening and fine screening on each candidate question to obtain a question set; pre-defining a mode of a knowledge graph according to field characteristics of the vertical field, processing all text blocks based on an information extraction model, and constructing a field knowledge graph; and performing local context retrieval on each final question in the question set based on the text block of the question source, performing global knowledge retrieval based on the domain knowledge graph, and generating a final answer and a final thinking chain. The method is suitable for different vertical fields, the data quality can be effectively improved, and the problem generation accuracy is guaranteed.

Certain aspects of the disclosure provide artificial intelligence (AI) methods and systems for generating personalized social media content with trend integration. A method generally includes retrieving data from data sources that includes customer interactions with a business, and inventory data of the business, determining trending-product pairs that increase engagement of the customers with products recorded in the inventory data of the business based on the retrieved data. A generative artificial intelligence (AI) model is used to generate one or more of a caption, a hashtag, and a promotional image that are personalized to each of the customers in response to receiving prompts that contain information about the customers, information about trending-product pairs, and social media platforms of the customers. The method sends one or more of the captions, the hashtags, and the promotional images that are personalized to the customers to social media platforms of the customers.

The invention discloses a multi-source data-based selling point generation method for AI intelligent marketing, and relates to the technical field of big data analysis and application, and the method comprises the following steps: obtaining a unified time scale matrix in a multi-source data access stage, building an extreme value inhibition prior based on the unified time scale matrix, and building an acquisition synchronization baseline; full-link time alignment is realized by collecting a synchronous base line; and acquiring an extreme value diagnosis fingerprint under the constraint of full-link time alignment, generating a threshold track based on a quantile residual spectrum and kurtosis skewness, and capturing and calibrating a burst extreme value by taking the threshold track as a dynamic reference. According to the method, a time sequence consistency basis is constructed, a data extreme value is accurately identified and corrected, the content is ensured to be true and credible in combination with causal correction and semantic screening, dynamic closed-loop optimization of content generation and delivery is realized through closed-loop feedback and phase regulation and control, and the authenticity, stability and compliance of AI marketing content are effectively improved.

The invention discloses a personalized teaching content generation method and system based on a digital portrait, and the method comprises the steps: collecting the multi-dimensional learning data of a student in real time, analyzing the data keyword of the student, constructing the portrait of the student, generating a teaching target based on the portrait of the student, and enabling the teaching target to comprise a teaching theme, exercises related to the theme, and knowledge points related to the theme. Converting the teaching target into an executable instruction of a large language model through a structured prompt engineering technology; the large language model generates initial teaching content according to the teaching instruction; verifying and optimizing the generated initial teaching content to ensure that the generated teaching content conforms to a teaching target and can adapt to the current cognitive level and learning requirements of students; and outputting the verified and optimized teaching content to the students and automatically adapting to the content presentation form according to the learning style preference of the students.

The invention provides a video generation method and device, electronic equipment, a storage medium and a program product, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, and can be applied to scenes of digital human, content generation based on artificial intelligence and the like. The method comprises the following steps: acquiring text features of a description text, image features of a virtual image in a reference image and audio features of audio, wherein the description text indicates action description information for driving the virtual image based on the audio; the role feature is bound with the audio feature to obtain a target audio feature, the role feature is associated with a corresponding virtual image in the reference image, and the role feature is used for indicating an association relationship between the target audio feature and the image feature; and generating a target video based on the text feature, the image feature and the target audio feature, wherein the target video comprises a plurality of video frames for making a sound according to the action description information based on the audio driving virtual image.

The invention relates to the technical field of large language models, in particular to an official document content generation and compliance verification system based on a large language model. Comprising an official document extraction module used for analyzing a model essay by using a large language model, extracting a writing style and a fixed sentence pattern, constructing a knowledge graph, storing format specifications, common expressions and logic structures, mining and constructing a core intention library, and storing typical intention prototype vectors; the content generation module is used for generating paragraphs through context sensing, expanding and writing keywords, analyzing behavior data, predicting writing intention vectors and performing condition control; the compliance verification module is used for warning improper words in real time, retrieving policy verification logic and verification formats, and performing pre-verification based on intention vectors; and the writing auxiliary module is used for providing real-time modification suggestions and forward-looking prompts based on writing intention vectors. According to the technical scheme, document writing efficiency and quality can be improved.

A processing system including at least one processor may obtain a text message content from a sender to a recipient, identify additional contextual content based upon a context associated with at least one of: the sender and the recipient, generate a revised text message content based upon the text message content in accordance with the additional contextual content, and transmitting a message to the recipient comprising the revised text message content.

The invention discloses an intelligent customer service dialogue generation method based on multi-modal interaction and dynamic knowledge enhancement, and the method comprises the specific steps: S1, intention recognition: recognizing the intention of a user according to the content inputted by the user; a dynamic query vector is generated based on the user intention and historical interaction records, weighted retrieval is carried out in a double-source knowledge base combining a knowledge graph and document paragraphs, and the weight of knowledge can be dynamically updated according to user feedback; s3, reply generation: inputting the user intention and the knowledge fragments related to the intention into the large language model to obtain a plurality of candidate replies; s4, performing compliance verification; and S5, outputting response. Through multi-modal fusion understanding, dynamic knowledge enhancement and controllable content generation, the interaction accuracy, knowledge timeliness and service individuation level of intelligent customer service are remarkably improved.

The invention provides an automatic workflow code generation method driven by a large model, which comprises the following steps: sequentially converting an original demand into a subtask set, a node relation graph and structured workflow description data through multi-stage agent cooperation, and forcibly checking a data closed loop between nodes by the last-stage agent; tool nodes and content generation nodes are defined in the workflow description data, and decoupling of content generation and tool calling is achieved; analyzing the structured workflow description data, dynamically generating a strong type state class mapped with the description data, and automatically filling a node calling logic and a parallel routing rule based on a preset code template to generate a workflow code; when the code runs wrongly and the number of retry times exceeds a preset threshold value, an execution track is generated through the error positioning agent, candidate schemes are generated in parallel through the multiple repairing agents, and after self-adaptive scoring is conducted through the scoring agent based on the error type, the repairing schemes are verified and executed in sequence.

The invention discloses a one-stop intelligent recruitment method, device and equipment and a storage medium, and the method comprises the steps: analyzing an electronic resume uploaded by a user, and extracting personal basic information; pushing an online evaluation questionnaire to the user, obtaining evaluation result data of the user, and performing dimension alignment on the personal basic information and the evaluation result data to generate a user comprehensive information file; acquiring post demand data corresponding to a preset job fair, analyzing the post demand data, and generating post feature vectors; constructing a matching model corresponding to the user and the post based on a deep matching neural network, inputting the user comprehensive information file and the post feature vector into the matching model, and outputting a candidate post list containing a matching degree score; receiving a target post selected by the user from the candidate post list, constructing an AI virtual interview scene, and analyzing the answer content of the user in the virtual interview to generate an interview evaluation report; and analyzing the interview evaluation report to assist the user in participating in the preset recruitment fair.

The invention discloses an intelligent document generation system and method for bidding documents, and the system comprises a preprocessing module which is used for obtaining bidding document files in various formats, and converting the obtained bidding document files in various formats into structured bidding document files; the analysis module is used for analyzing the converted bidding document file based on the large model cue word to obtain a structured bidding document file analysis result; the directory generation module is used for generating a directory of the bidding document file according to the analysis result; the retrieval module is used for generating a corresponding knowledge content set according to the bidding document file directory; and the content generation module is used for generating the text content of the bidding document file based on the knowledge content set and the structure information of the bidding document file directory. And the content assembly module is used for performing multi-dimensional feature extraction and classification on the generated bidding document file text content, and completing content assembly according to a preset document structure tree to obtain a standard document meeting bidding document requirements.

The invention discloses a digital animation role dynamic generation method based on a generative model. The method comprises the following steps: inputting a role original image and scene background information, extracting skeleton key points and scene features, analyzing an action sequence, predicting a motion track, calculating an optimal position of a role in a picture, and carrying out dynamic adjustment. And according to the role position, obtaining morphological feature data, evaluating the quality grade, and optimizing the coordination of the role image. And finally, comprehensively scoring by adopting a multi-dimensional quality evaluation system, and determining the visual presentation quality of the role. According to the method, deep fusion of role actions, positions and scenes is realized, the dynamic expressive force and visual coordination of animation pictures are improved, and an efficient solution is provided for generating high-quality animation contents.