2312 results about "Generative model" patented technology

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

An advanced search system leverages a pre-trained large language model to enhance user query responses. The system, equipped with hardware processors, a search query via an interface and accesses a pre-trained large language model designed to respond to the search query. The system fine-tunes the model to generate a task-specific generative model. The system employs the task-specific generative model to generate a search result to the search query and analyzes the search result based on a performance metric associated with the task-specific generative model. The system refines the task-specific generative model based on the analyzing of the search result.

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.

The invention provides an intelligent customer risk assessment system and method based on a large language model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining multi-modal data of a customer, carrying out the preprocessing, and extracting structured and unstructured features; constructing a hierarchical risk knowledge system, and realizing adaptive evolution of the knowledge system through a graph neural network and a generative model; constructing an initial negative sample library, and constructing a negative sample database in combination with a non-risk mode labeled by an expert and derivative layer analysis; optimizing the large language model by adopting a strong supervision, weak supervision and reinforcement learning cooperative training mechanism under each classification according to the customer type; mining risk features in a text by using the optimized large language model, processing multi-modal data through a multi-level attention network, and generating a positioning report including contradiction type coding, service influence dimension evaluation and risk level quantification; the accuracy, efficiency and flexibility of customer risk assessment are improved, and the risk management strategy is optimized.

A neural end-to-end system is provided for the face and voice preserving translation of videos. The system is a pipeline of multiple models that produces a video of the original speaker speaking in the target language with modified lip movement to match the target speech, while preserving emphases and prosody of the original speech, and voice characteristics of the original speaker. The pipeline starts with automatic speech recognition including emphasis detection, followed by the translation model. The translated text is then synthesized by a Text-to-Speech model that recreates the original emphases in the target sentence. The resulting synthetic speech is then converted back to the original speakers' voice using a voice conversion model. Finally, to synchronize the lips of the speaker with the translated audio, a generative model generates frames of adapted lip movements which are combined with the audio to produce the final output. The disclosure further describes several use-cases and configurations that apply these techniques to video conferencing, dubbing, low-bandwidth transmission, speech enhancement and assistive technology for the hearing impaired.

This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.

Embodiments of the present disclosure relate to rendering and inverse rendering using one or more generative models. “Rendering” refers to the process of generating a final visual image, video frame, or animation from a 2D or 3D model. “Inverse rendering” is a process that involves deducing or estimating the properties (e.g., material maps or other properties such as geometry, lighting, and textures) of a scene from observed images or visual data. Essentially, it aims to reverse the traditional rendering process. Various aspects of the present disclosure introduce editable light and material controls into generative models to allow for artistic creation. Various embodiments integrate generative models as a renderer for classic rendering pipelines to upcycle and enhance the style of rendered content.

The invention provides a complex task full-automatic processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as large language models, generative models, agents and task intelligent scheduling. The method comprises the following steps: interacting with a target user who puts forward an original task demand to obtain a complete task demand; the complete task demand is split into a plurality of sub-tasks at least comprising a complex sub-task, the complex sub-task refers to a sub-task needing at least two sub-agents to process according to a cooperation process, and a single sub-agent is used for processing a simple sub-task; issuing the complex sub-tasks to corresponding target execution intelligent units, and issuing the simple sub-tasks to corresponding target sub-agents; and summarizing sub-task execution results returned by each target execution intelligent unit and each target sub-agent. According to the method, the execution intelligent unit specially used for processing the complex sub-task is introduced, and the complex sub-task is processed more intensively according to the cooperation process through the at least two sub-agents contained in the execution intelligent unit, so that the task disassembling difficulty is reduced; and a better sub-task processing result can be obtained through the execution intelligent unit integrated by the multiple sub-agents.

The invention relates to the technical field of artificial intelligence and robots. According to the multi-modal perception and interaction method and device in the body environment, the method comprises the steps that environment entropy estimation processing is carried out through a dynamic weighting multi-modal feature fusion algorithm, and an environment entropy value representing the disorder degree of the environment is generated; performing cross-modal alignment processing to generate a fusion environment understanding map; performing dynamic decision processing through the task adaptive reinforcement learning model to generate an interaction instruction; driving an execution mechanism to execute the interaction action to obtain an execution result of the interaction action; carrying out dynamic adjustment processing on the weight of the environment entropy value to generate an updated environment entropy weight; and performing local knowledge node incremental updating processing on the meta-knowledge base to generate an optimized meta-knowledge base so as to solve the problems of insufficient consistency of cross-modal data and poor environmental understanding robustness caused by large distribution deviation between virtual features generated by a generation model in a noise or data missing scene and a real environment in related technologies.

A key-value cache management system processes inference requests of a generative model. An inference request requests a starting virtual memory address and a number of layers in the generative model. In response to receiving an inference request, contiguous virtual memory space is reserved for the key-value cache in accordance with the inference request by assigning the starting virtual memory address in the key-value cache and calculating memory pointers for each layer in the model and each block so that the one or more blocks are written sequentially. The generated memory pointers are outputted to the generative model so that each self-attention layer writes computed key-value pairs at physical addresses in the key-value cache specified by the memory pointers.

Generative models are disclosed to generate audio and visual outputs to a user when the user struggles with a particular aspect of a video game. The generative outputs can demonstrate what success at that aspect of the game looks like, doing so using the same playstyle, ability, and tactics as the user themselves to provide relevant and feasible assistance to the user.

The invention provides a personalized teaching method and system based on generative artificial intelligence. The method comprises the following steps: constructing a knowledge graph and a teaching strategy template library of a target course; constructing a feature enhancement matrix; calculating node activation intensity, generating a plurality of candidate learning paths through a beam search algorithm, and screening an optimal learning path in combination with a preset teaching strategy template; using the generative model to generate standardized teaching content adaptive to the teaching strategy; dynamically optimizing a teaching scheme through a neuroplasticity model; and carrying out teaching adjustment based on the optimized teaching scheme. Standardized teaching content is generated by utilizing the generative model, the teaching content quality is improved, the teaching content is closely matched with a learning path and a teaching strategy of a learner, meanwhile, a teaching scheme is optimized through the neuroplasticity model, teaching parameters and cognitive resource allocation are adjusted in time according to real-time feedback and cognitive states of the learner, and the teaching efficiency is improved. Limited cognitive resources are reasonably distributed to key parts of teaching contents, and the teaching effect is improved.

The invention relates to the technical field of industrial defect detection, and discloses an overflowing surface defect detection and identification method and system of a multi-modal feature and prompt mechanism, and the method comprises the steps: collecting image data and point cloud data of the surface of a water turbine runner overflowing surface, and converting the point cloud data into a surface defect two-dimensional image; obtaining a high-resolution defect image based on an image super-resolution reconstruction method of a deep recursive network; inputting a defect image into the generative adversarial network, judging a data source, generating a high-quality defect image through an objective function optimization generative model, and expanding a data set for a defect identification detection model to use; and inputting the point cloud data into the PDE-Net to construct a dynamic adjacency graph, and mapping the point cloud data to a specific defect category after graph convolution and multi-layer feature extraction. The method has the beneficial effects that a mixed attention mechanism is introduced in a multi-modal feature extraction stage, global information and local details in a defect image are fully extracted, and the detection capability of complex defects is improved.

A method and system is disclosed which enable sensitive data to be protected during interactions with a generative model.

Generative question answering and explanation includes receiving a question. It further includes identifying a type of the question. It further includes constructing a prompt to provide to a generative model based on the identified type of the question. It further includes providing output based on a generative response provided by the generative model. The output further comprises a citation. The citation is based at least in part on one or more contextual passages.

Implementations relate to providing a user feedback mechanism that enables a user to provide feedback towards one or more specific portions of a response. The response can be generated based on processing of a user query using a generative model such as a large language model (LLM). The one or more specific portions can be a textual portion that includes textual content, and / or a media content portion that include media content such as one or more images, one or more videos, one or more audio pieces, etc. The feedback towards one or more of the specific portions of the response can be utilized in training or fine-tuning the generative model (or an additional generative model) via approaches such as supervised training or reinforced learning.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a generation strategy optimization method, device and equipment based on a dynamic environment and a medium. Correcting the generated action vector by combining a domain constraint strategy to obtain a compliant action vector, constructing a multi-dimensional reward vector according to feedback after execution, scaling the reward vector into a reward signal, and finally updating the pre-training generative model by adopting a self-adaptive strategy optimization module based on the reward signal and an interaction track to obtain a reward result. And collaborative evolution of strategy generation and environmental response is realized. According to the method, by introducing dynamic environment information and domain constraints, compliance correction and optimization updating of the generative strategy are realized, and the stability and practicability of the model in a complex environment are improved.

A data processing system implements receiving content and a call requesting a generative model to generate a video summary of the content; constructing a prompt including the content and instructions to the model to identify semantic context of the content, to identify a text data item, an audio data item, and / or a video data item embedded in the content to generate a text transcript of the audio data item and / or the video data item, or a textual description of the video data item, to summarize the text data item, the text transcripts, and / or the textual description as a summary of the content based on the semantic context, and to generate the video summary based on the summary and a portion of the text data item, the audio data item, and / or the video data item; providing the first prompt to the generative model; providing the video summary to a client device for presentation.

The invention discloses a knowledge base technical method and system based on RAG retrieval enhancement, and relates to the technical field of knowledge bases. The method comprises the steps that a source document is analyzed into structured text fragments, and semantic vectors are generated; splitting the text segments into minimum knowledge units, extracting concepts and behavior trigger words to construct a concept association graph, and storing session abstracts in a fixed-length annular structure; the method comprises the following steps: receiving user query, generating a query vector, retrieving a most relevant text fragment, constructing and de-duplicating candidate knowledge units by combining hierarchical diffusion of a concept association graph and an annular abstract matching result, deeply splicing original text paragraphs according to a graph path, generating a dynamic prompt box, calling a generative model to generate a preliminary answer, and executing verification. And the verification state is marked on the final answer. By constructing a concept association map, the activeness weight and the time sequence fingerprint of a map edge are updated in real time, and accurate capture of deep semantics and logical relationships of query intentions is realized.

A data processing system implements receiving a first prompt including a style visual content item and a topic content item and requesting generating an output visual content item; constructing a second prompt as an input to a first generative model, by appending the style visual content item and the topic content item to a first instruction string that comprises instructions to the first generative model to generate a textual description combining a topic in the topic content item with a style in the style visual content item as a third prompt; inputting the third prompt into a second generative model to generate the output visual content item by including the topic in the output visual content item and replacing visual element(s) of the style visual content item based on the topic while preserving the style; and providing the output visual content item to be presented on a user interface.

The invention discloses a joint denoising method for robot visual motion prediction, and the method comprises the steps: constructing a unified generative model through fusing an image and a depth map collected by a depth camera, motion data collected by CAN line communication of a Piper mechanical arm, and a tactile image collected by a Gelsight Mini tactile sensor; the method comprises two steps of data acquisition and input coding, and joint denoising and generation: firstly, multi-modal data are coded into low-dimensional potential representation, and then future images, depth maps, tactile data and robot actions are cooperatively predicted through a joint denoising framework based on Transform. A mask self-attention mechanism is innovatively introduced, information interaction between modes is dynamically adjusted, action generation is guided through tactile feedback, and the force control precision is improved. The model adopts a de-noising diffusion probability loss function to jointly optimize multi-modal prediction, so that the output consistency is ensured. According to the method, the robustness and the accuracy of flexible operation of the robot are remarkably improved.

The invention discloses a semantic analysis and generation method and system of an operation order, a medium and equipment, and belongs to the field of power system automation. The method comprises the steps that a task text of power grid operation is acquired, semantic analysis is conducted on the task text according to a generative model, and an analysis result is obtained; performing logical reasoning on the analysis result according to a preset knowledge graph to obtain a corresponding operation sequence; wherein the knowledge graph comprises nodes and edges between the nodes, each node comprises power grid equipment and an operation state, a running state and an operation rule of the power grid equipment, and each edge comprises an operation relation and a regulation dependency constraint between the nodes; processing the operation sequence according to a preset formatting rule to obtain a corresponding first operation ticket; and based on a preset operation rule set, performing logic verification on the first operation ticket to obtain a final operation ticket. Therefore, by implementing the method and the device, the problem that logical reasoning and semantic understanding capabilities are insufficient when the operation ticket is generated in a dynamic scene in the prior art can be solved.

Systems and methods for domain-specific model-generated content item generation, evaluation, and selection can include generating a plurality of candidate model-generated content items that can then be evaluated based on one or more signals, which can then be leveraged for candidate model-generated content item selection. The plurality of candidate model-generated content items can be generated with a generative model that was tuned for domain-specific content item generation. The selected model-generated content item can be processed to generate an outline that may then be provided to a user for user interaction to generate an augmented outline. The augmented outline may then be processed to generate an updated model-generated content item.

Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

The invention relates to the technical field of electrical digital data processing, and discloses a system conflict point intelligent review system based on a knowledge graph and a large language model, which comprises the following steps: constructing a dual-mode storage space containing an unstructured index and a structured logic graph, analyzing target text extraction features and triggering graph-based generation logic; converting the topological structure of the associated sub-atlas into a natural language instruction sequence to construct a forced logic constraint template, filling the template with a text, and inputting a pre-training language model to generate a verification result; according to the method, the discrete atlas topology is mapped into the linear logic constraint, random divergence of the generative model is restrained on the calculation principle, and precise decoupling and dynamic evolution of unstructured semantics and structured logic are achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a generation optimization method and device based on dynamic verification feedback, equipment and a medium, and the method comprises the steps: pre-training a verification network used for analyzing and generating content quality; constructing a dynamic reward space containing orthogonal reward components; integrating the dynamic reward space into a generative model to form a real-time verification loop, and injecting verification signals into a plurality of processing layers; optimizing an exploration strategy of the generative model according to the verification signal; executing double-loop feedback control based on the real-time verification loop and the optimized exploration strategy, dynamically adjusting training parameters, and generating a target model; and outputting a reasoning result based on the target model. By constructing a real-time verification loop and an optimization strategy, a double-loop feedback control mechanism is realized, a verification signal is introduced into a training process, a training error and a strategy deviation are dynamically responded, and the training efficiency of the generative model is improved by combining rapid and slow adjustment paths.

The present invention provides a system and method for generating proposals for infrastructure modalities, such as electrical substations, using advanced artificial intelligence. It includes an input interface for data collection, a lightweight generative or rendering pipeline for creating preliminary 2D designs, and a generative model selected from diffusion, transformer-based, GAN or other architectures for refining these into detailed 3D models and generating preliminary designs. The system evaluates designs against predefined criteria to ensure compliance and feasibility. Supported by a cloud-based infrastructure for robust data processing and integration with third-party services, this system enhances the efficiency, accuracy, and compliance of modality planning and proposal generation.

An apparatus for generating obfuscated data within a computing environment, comprising a processor and a memory containing instructions configuring the processor to access a database containing a plurality of private data elements belonging to at least a private record, generate a set of obfuscated data elements, representative of the at least a private record, as a function of the plurality of private data elements using an generative model, determine a first distance measure between at least an obfuscated data element within the set of obfuscated data elements and at least a private data element of the plurality of private data elements within the database, and verify the first distance measure is within a distance range, wherein a minimum threshold of the distance range is determined as a function of a deidentification parameter and a maximum threshold of the distance range is determined as a function of an obfuscation parameter.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.