5463 results about "Content generation" patented technology

A system and method for AI-driven advertising and content generation that integrates connectionist and symbolic AI techniques to deliver personalized, contextually relevant user experiences. The invention leverages specialized agent networks, knowledge graphs, and retrieval-augmented generation to create dynamic, adaptive content with seamlessly integrated advertisements. It employs a sophisticated ad integration layer and experience broker to ensure relevance and engagement. The system prioritizes security, traceability, and user preferences while optimizing ad placement and content delivery. By bridging connectionist and symbolic AI, the platform enables immersive, tailored interactions across multiple scenarios and time horizons, enhancing user engagement and advertiser value in an AI-driven digital landscape.

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.

The present disclosure is directed to systems and methods to enhance the process of creating an artificial intelligence (AI) generated content or content items, such as images, text, video, sounds, etc., using a text or other suitable prompt, such as via voice input. The systems and methods disclosed provide streamlined content generation with, e.g., reduced processing power and computing time. In an embodiment the systems and methods receive a prompt for generating a first content item using a generative artificial intelligence (AI) model and retrieve, based on the prompt, a collection of matching content items. The systems and methods may then receive input selecting one of the content items from the collection and identify a prompt used to generate the selected content item. The systems and methods may then merge using a trained natural language processing model, the received prompt with the prompt of the selected content item to create a third prompt. In an embodiment the systems and methods may modify the third prompt based on additional input and, based on the modified third prompt, generate a second content item.

Approaches presented herein provide for the ability to process, store, index, and search geospatial information such as maps with flexible granularity. A set of observations, such as may include sensor data captured for a region of an environment, can be fed as input to a language model. The language model can generate a tokenized description of the region, as may include a text string of tokens encapsulating semantics, topology, geometry, and / or other aspects of the region. A feature vector or embeddings for the region can be generated based on the tokenized description, and a similarity search performed against a vector database, for example, to identify similar feature vectors corresponding to similar regions or domains. Labels or other information associated with these similar feature vectors can be automatically applied to the example region. Clustering of feature vectors or other embeddings can also be performed based in part on the similarity.

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 discloses a multi-layer semantic understanding large model agent construction and application method. The method comprises the following steps: S1, preprocessing input data; s2, analyzing the preprocessed data by using a large language model and dialogue history, identifying user intentions and extracting deep semantic features; s3, key information is extracted from data intending to be recognized, structured representation is carried out, and model configuration is adjusted through dynamic hyper-parameter optimization; s4, querying the knowledge graph, associating and organizing the extracted key information with the existing data, and generating a structured knowledge representation; s5, vectorizing the structured knowledge representation, and storing the vectorized structured knowledge representation in a vector database to support rapid similarity search; and S6, in combination with the generated unified semantic representation and the large model, performing deep analysis on user input, generating coherent and logical personalized answers, and returning the personalized answers to the user. The method can support various application scenes such as intelligent question and answer, content generation, sentiment analysis and multi-modal search.

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 present disclosure describes a method comprising receiving a plurality of training images; executing a feature extraction machine learning model to generate a plurality of training embeddings for a plurality of training images each in an embedding space; training a content scoring machine learning model using the plurality of training embeddings to generate performance scores for content items based on embeddings in the embedding space; receiving a set of text; executing the feature extraction machine learning model using the set of text to generate a text embedding in the same embedding space as the training embeddings for the plurality of training images; generating, using the content scoring machine learning model, a text performance score for the set of text using the text embedding in the embedding space; and generating a record identifying the text performance score for the set of text.

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.

Provided in the embodiments of the present disclosure are a dialogue content generation method, a model training method, a prompt text generation method, a device and a storage medium. The dialogue content generation method comprises: receiving an input question; determining whether the question needs to be answered in combination with external knowledge; if the question needs to be answered in combination with external knowledge, acquiring target knowledge from a preset knowledge base on the basis of the question, generating prompt text corresponding to the target knowledge, inputting into a dialogue content generation model the question, the target knowledge and the prompt text corresponding to the target knowledge, and generating dialogue content by means of the dialogue content generation model, wherein the prompt text is used for prompting the dialogue content generation model to answer the question with reference to the target knowledge; and if the question does not need to be answered in combination with external knowledge, inputting the question into the dialogue content generation model, and generating dialogue content by means of the dialogue content generation model, wherein the dialogue content generation model is obtained by means of performing fine tuning on a pre-trained language model.

In various examples, systems and methods are disclosed relating to systems and methods for multi-modal creative content generation using neural networks. The systems and methods can use one or more neural networks to generate outputs representative of creative and / or artistic characteristics of features indicated by input prompts. The one or more neural networks can include at least one text extension model to increase an amount of information of the input prompts. The one or more neural networks can be configured to generate high resolution outputs. The one or more neural networks can be used to implement end-to-end conversational interfaces for receiving input prompts and presenting creative and / or artistic outputs.

The invention relates to the field of natural language understanding in an artificial intelligence technology man-machine dialogue system, in particular to a psychological accompanying dialogue method based on multi-agent collaboration and a storage medium. The psychological accompanying dialogue with individuation, high naturalness and accurate emotion recognition is provided, the user experience is effectively improved, and the psychological support requirement of the user is met. According to the main scheme, the method comprises the steps of performing multi-mode emotion evaluation on user input to obtain user emotion state description; retrieving related dialogue states and external knowledge from a shared memory module according to user questions and user emotional state description; according to the retrieved dialogue state and external knowledge, the strategy planning module generates a reply strategy to obtain a strategy group; the dialogue content generation module calls a corresponding vertical domain agent according to the generated reply strategy to generate specific reply content; and storing the user questions and model replies in the current round into the short-term memory of the shared memory module, and updating the long-term memory. The method is used for AI dialogues.

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.

Techniques and systems are disclosed that perform content generation with integrated automated formatting using word processing applications that deploy large language models (LLMs). The techniques include receiving, a natural language (NL) query for a synthetic content for a document, identifying formatting rules of the document, and generating an augmented query that includes a representation of at least a portion of the NL query and a representation of the one or more formatting rules of the document. The techniques further include providing the augmented query to an LLM and updating the document with the synthetic content generated by the LLM in response to the augmented query.

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.

The invention discloses an AIGC multi-mode audio-visual content creation method and system, and belongs to the technical field of multi-mode audio-visual content generation and automatic creation, the AIGC multi-mode audio-visual content creation method comprises the following steps: an input module for inputting text description and materials required by creation by a user; the AIGC generation module is used for converting the text description and the material input by the user into a video sequence and a music clip; the multi-modal fusion and alignment module fuses and aligns the generated video sequence and the music clip to obtain an audio / video; the video intelligent editing module is used for splicing the segments of the audios and the videos and keeping visual coherence and smooth connection between the two audios and the videos; and the output module is used for outputting the generated audio and video as the media format required by the user. The method solves the problems that in the prior art, multi-modal audio-visual content creation lacks the ability of cross-modal cooperative work, the content generated during multi-modal information fusion and alignment is inconsistent in rhythm, semantics and emotion, the support of a later editing process is lacked, the content connection is not natural enough, and the creation speed is low.

Methods and systems are described for generating memes and enhanced content in electronic communication. The communication is received, analyzed, and assessed for determining whether the enhanced content should be included in the communication. A content, context, intent, and / or mood of the communication is determined. For meme generation implementations, images and captions are generated based on the communication. Some candidate memes incorporate an image of one of the participants in the communication. Enhanced content candidates are generated and presented to the user for inclusion in the communication. Artificial intelligence systems, including neural networks, and models are utilized to improve the enhanced content generation. Related apparatuses, devices, techniques, and articles are also described.

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.

A system and method for automatically generating content for a user based on a context includes conducting a search of data associated with the user to generate search results associated with the user, applying first models to the first search results to infer characteristics and patterns associated with the user and to generate a first set of content based on the inferred characteristics and patterns, and generating a first pseudo-document that includes a first set of pattern content, based on the first set of content generated by the one or more first models. Additionally, second models are applied to data associated with the context to infer characteristics and patterns associated with the context and to generate a second set of content based on the inferred characteristics and patterns of the context. A second pseudo-document is generated based on the second set of content. A prompt is created by integrating the first pseudo-document and the second pseudo-document and transmitted to a language model to cause the language model to generate content for the user that is customized to both the user and the context.

The technology disclosed relates to a system and methods for artificial intelligence-based workspace content generation using sources of digital assets in a multi-user search and collaboration environment. The disclosed methods can include sending a portion of a spatial event map that locates events in a virtual workspace; sending data to allow the client node to display a digital asset identified by events in the spatial event map; receiving an input for a trained machine learning model wherein the input comprises the identification of a digital asset selected by a user or desired features in an artificial intelligence (AI)-based digital asset; sending the input received to the trained machine learning model; receiving the AI-based digital asset as output by the trained machine learning model; and sending the AI-based digital asset to a plurality of client nodes, allowing the client nodes to display the AI-based digital asset in respective digital displays.

An apparatus and method are provided, which provide automated analysis and generation of marketing or advertising content, e.g., using large language models (LLMs). The apparatus extracts specific insights from input advertisements, such as needs served, brand personas, products advertised, target audiences, tone, and topical categories. These insights are summarized in the formats commonly used in digital marketing, including brand evaluations, comparative analyses of campaigns, possible future advertising content examples, and examples of user personas together with the imaginary persona stories supporting them. The apparatus may leverage multi-modal prompt engineering to have LLM identify key features of advertisements, generalize analyses, present examples, and generate customer personas, stories, marketing content examples. Sample outputs include brand values and goals identification, persona analysis with examples, campaign differentiators comparison, and Artificial Intelligence (AI)-enhanced customer persona generation. The automation and scalability of such formerly manual marketing tasks provide actionable insights to facilitate rapid and informed decision-making.

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.