347 results about "Generative systems" patented technology

A generative AI-supported system (100) for real-time demand forecasting and inventory optimization in supply chain management, which includes the following: a data collection and integration module configured to aggregate and preprocess structured and unstructured data from multiple sources, including historical sales records, IoT sensors, external market trends, and environmental factors; a generative AI-based forecasting module configured to process input data, recognize complex patterns, and generate highly accurate real-time demand forecasts using deep learning techniques; an inventory optimization module configured to dynamically calculate and adjust inventory levels based on demand forecasts, replenishment plans, lead times, and safety stock requirements; a decision support and analysis module configured to generate actionable insights, provide simulations of multiple scenarios, and recommend cost-effective strategies for supply chain operations; a real-time warning and notification module configured to monitor supply chain activities and inform stakeholders of potential disruptions, anomalies, or demand spikes; a user interface and integration module configured to provide outputs via a user-friendly dashboard and seamlessly integrate with existing enterprise resource planning (ERP), warehouse management systems (WMS), and logistics platforms.

The invention provides an agent-based large language model retrieval enhancement generation system and method, and the system comprises a planning layer which is used for receiving user query, carrying out the multi-round iterative decomposition of a complex task through a task planning agent, and generating an atomic query or a direct response; the execution layer is used for executing the atomic query generated by the planning layer in parallel, calling a search module to obtain external knowledge base data, and caching an intermediate result through a memory module; the answer detection module is used for performing multi-dimensional detection on the generated result, including preference, accuracy, integrity and logicality; the dynamic decision-making module is used for adaptively adjusting a subsequent retrieval strategy and a task planning process according to a detection result and user feedback; and a cross-layer interaction mechanism enables the planning layer and the execution layer to realize collaborative optimization through context sharing and iterative feedback.

Embodiments of the present invention provide an innovative Retrieval-Augmented Generation (RAG) system tailored for financial analysis, significantly enhancing the precision and contextual relevance of Large Language Models (LLMs). A part of the system is a query augmentation component that leverages a knowledge graph to semantically enrich user queries, ensuring comprehensive retrieval of pertinent financial documents. A noise filtering mechanism refines the search results, while a relevance ranking component prioritizes documents based on context (e.g., user and task). The system employs prompt engineering to guide the LLM in generating responses that meet the specific requirements of financial analysis. Additionally, the LLM is fine-tuned using a corpus of financial questions and answers, reinforced by human-in-the-loop feedback, to adapt the model to the financial domain's unique linguistic and structural nuances. This advanced RAG system offers financial professionals timely, reliable, and actionable insights, providing a competitive edge in a rapidly evolving financial landscape.

An unstructured data query-response pair generation system (generation system) populates a knowledge base of query-response pairs for queries of natural language content in unstructured data by prompting a first large language model (LLM) text extracted from the unstructured data. An unstructured data chatbot (chatbot) leverages the knowledge base by augmenting prompts to a second LLM responding to user queries for natural language content in the unstructured data with query-response pairs having queries that are semantically similar to the user queries. The knowledge base and LLMs are updated based on user feedback correcting responses, continually improving quality of the generation system and chatbot.

The invention discloses a multi-modal natural language understanding and generating system and method. The method comprises the following steps: constructing a cross-modal pre-training module, training a multi-modal encoder, and establishing a cross-modal association mapping space; mixing prompt fine tuning is carried out, and a complete blank filling template is constructed; according to the intention reasoning network, extracting multi-round dialogue intention representation of the user, and retrieving an external knowledge base for fine-grained reasoning; constructing a unified semantic representation framework, embedding the text, the image and the voice into a unified space, and generating a query vector of multi-modal intention perception; and the knowledge query module based on key value memory generates entity-level multi-modal replies and optimizes the semantic comprehension and generation capability of the dialogue model. According to the method, the multi-modal information understanding and generating capacity is improved, deep association and understanding of image and text information are achieved, downstream task adaptability is enhanced, task completion accuracy and efficiency are improved, unified semantic representation of the multi-modal information is achieved, and support is provided for information retrieval and utilization.

In some embodiments, generative systems are physical mechanics and hardware apparatus to generate display content and include an input receiver module, a graphics processor module, and an output module. In some embodiments, the input receive module converts or splits a video input signal to provide a video input stream for the graphical processing module, which includes one or more processing units. The graphics processor module extracts both the graphical input content and the metadata of the input stream, operates on said information, and outputs the result to the output module, which emits an output signal for display on a display system. In some embodiments, the graphics processor module uses a generative function or a computer vision function. In some embodiments generative system is a standalone device to mediate and infer visual content between a computational system and a display system.

The invention discloses a method and system for context semantic extraction and intention recognition in voice dialogue, and the method comprises the following steps: S1, obtaining a voice input signal, converting the voice input signal into text data, and carrying out the preprocessing; s2, constructing a dialogue context cache, and updating the cache content; s3, constructing a sparse hybrid expert Transform network, and carrying out semantic coding on the sparse hybrid expert Transform network; s4, calculating a semantic dependency relationship between dialogue statements by adopting an ordered graph neural network; s5, calculating intention probability distribution, and adjusting the weight of the sparse hybrid expert Transform network and the weight of the ordered graph neural network; and S6, updating a dialogue state, and generating a system response. According to the method, the sparse hybrid expert Transform network and the ordered graph neural network are combined, voice dialogue context semantic extraction and intention recognition are achieved, and the method has the advantages of being accurate in semantic understanding, high in calculation efficiency and high in context sensing capacity.

The invention discloses a SysML model automatic generation system based on mixed AI and domain knowledge, and the system comprises a preprocessing module which is used for carrying out the text preprocessing and structural enhancement of an engineering document of a PDF or Word version; the NLP extraction module is used for identifying six types of core entities by adopting aviation corpus fine tuning BERT, constructing a document-level relational graph by utilizing GNN, modeling a cross-paragraph dependency relationship, calling LLM for semantic fuzzy sentences to generate a thinking chain, extracting a reasoning path and solving ambiguity; the rule conversion engine module is used for mapping the entity relation graph into a SysML memory object tree; and the controllable generation module is used for carrying out limited decoding on the LLM by utilizing a Guidance framework. The invention further discloses an automatic SysML model generation method based on the mixed AI and domain knowledge. According to the method, the problems of low manual modeling efficiency and poor semantic consistency in traditional MBSE implementation are solved.

The invention discloses a report generation system and method based on a legal knowledge graph, and relates to the technical field of artificial intelligence and law, and the method comprises the steps: obtaining a legal appeal of a user through a non-standardized domain corpus collection engine, carrying out the multi-dimensional intention deconstruction through a semantic slicing model, and obtaining a judicial element topology network; inputting the judicial element topology network into a law deduction engine of dynamic value perception ability, calculating a decision vector weight through a dynamic value evaluation network, and generating a judicial decision path by using a time sequence feedback mechanism; based on a judicial decision path, through an association rule mining engine of a multi-modal knowledge graph, a dynamic weighting law retrieval confidence matrix is generated in combination with semantic coupling degree analysis and logic path influence evaluation. According to the method, qualitative change of legal consultation from information retrieval to intelligent decision is realized through collaborative innovation of a legal deduction engine with dynamic value perception capability and a legal semantic constraint decoder.

The invention relates to the technical field of intelligent generation, and particularly discloses a decoration scheme generation system and method based on generative artificial intelligence, which utilize an artificial intelligence technology based on deep learning to analyze the demand of a user, extract the semantic feature of the decoration demand of the user, and meanwhile, generate the decoration scheme according to the semantic feature of the decoration demand. And feature learning is performed on a large number of indoor decoration elements, and an indoor decoration element feature library is established, so that a room decoration effect picture is intelligently generated based on the semantic association interaction features of the indoor decoration element set and the decoration demand of the user. Therefore, the room decoration effect picture can be automatically and intelligently generated for the user to refer to and select according to the personalized requirements of the user, so that the efficiency and the quality of decoration design are improved, and better decoration experience is provided for the user.

The invention provides a legacy system-oriented large-model-driven API document automatic generation system, belongs to the crossing field of artificial intelligence and software development, and provides a multi-modal data fusion and closed-loop verification mechanism aiming at the defects of a traditional API document generation method in the aspects of semantic comprehension, dynamic context capture and multi-technology stack adaptation. A code static feature and a dynamic track during operation are analyzed through a multi-source data acquisition module, and an interface semantic feature is extracted in combination with a field self-adaptive large model of a semantic enhancement analysis module; deducing an implicit service rule by fusing static / dynamic characteristics through a graph neural network, and generating a standardized document conforming to an OpenAPI specification through a parameterized template generative adversarial network (PT-GAN); and finally, performing three-level verification and closed-loop optimization through a sandbox environment. The method supports a heterogeneous system of 16 programming languages such as Java / C + + / Python, interface version changes can be automatically recognized, document patches are generated, the problems of missing and outdated system documents and low maintenance efficiency are solved, and maintainability and integration efficiency of enterprise-level systems are remarkably improved.

The invention discloses an intelligent crawler generation method and system based on a large language model and an MCP protocol, belongs to the technical field of network data collection, and solves the problem that the capability of LLM in dynamic webpage analysis and anti-crawling strategy generation links cannot be fully exerted due to the fact that LLM and browser interaction protocols cannot be effectively integrated in the prior art. The method comprises the steps of analyzing an acquisition demand based on a large language model and generating a standardized demand description document, realizing interaction between the large language model and a browser based on an MCP protocol, analyzing a page complete DOM tree structure through a crawler script generation system, and performing quality verification and intelligent repair on a generated crawler script. According to the method, the complete DOM tree and the dynamic data rendered by the browser are obtained through the MCP, and the large language model can be called to automatically analyze the element positioning strategy, so that the collection script is adaptively generated, and high efficiency, intelligence and automation of webpage data collection are ensured.

The invention discloses an intelligent content generation system based on open source data analysis, and relates to the technical field of monitoring analysis, and the system comprises a data preprocessing module which is used for carrying out the multi-modal content collection of a heterogeneous open source data platform, and obtaining a structured semantic feature matrix; the graph construction and extraction module is used for constructing a domain knowledge graph based on the structured semantic feature matrix to obtain a domain evolution feature vector; the intelligent content generation module is used for inputting the domain evolution feature vector into a multi-modal generative adversarial network for semantic-visual cross-modal mapping to obtain style-enhanced intelligent generation content; and the optimization module is used for performing propagation effect simulation prediction on the style-enhanced intelligent generation content, generating a content propagation influence matrix, performing optimization iteration on the intelligent generation content according to the content propagation influence matrix, generating final optimization content and pushing the final optimization content to a target terminal.

The invention relates to the technical field of data processing, and particularly discloses a digital human voice generation system based on a large language model, which is used for solving the existing problems and comprises a semantic analysis module, a voice generation module, a multi-mode synchronization module, a data transmission module and a user feedback module, the semantic analysis module performs semantic analysis and sentiment analysis on voice and text data input by a user by using a large language model, extracts voice information and sentiment metadata for describing context, and dynamically constructs a semantic structure and a sentiment label adaptive to an education scene according to input content based on a metadata automatic derivation technology; through the dynamic time warping algorithm and the educational knowledge graph, semantic and emotion changes are accurately captured, the speech speed and tone are flexibly adjusted, and the naturalness of digital human speech generation and the adaptability of educational scenes are improved.

A system and method for generating question-answer pairs is disclosed. The system and method can receive a document. A sentence and / or a further sentence in the document may be identified. A syntactic map for the sentence and / or the further sentence may be generated. Noun phrases and prepositional phrases may be identified based on the syntactic map. Sentence level questions may be generated based on phrases identified using natural language processing (NLP) techniques. Document level questions can also be generated based on syntactic maps generated and NLP techniques.

The invention relates to an automatic assembly system and method for multi-scale digital twin components, and the system comprises a semantic relationship construction module which is used for carrying out explicit definition on the structural features, functional attributes, spatial layout requirements and logic dependency relationships of the multi-scale components, and constructing semantic relationships among the three types of components; the semantic reasoning and constraint engine module is used for carrying out logical reasoning through the semantic relationship constructed by the semantic relationship construction module and judging whether the component combination meets the assembly constraint or not; the assembly generation and configuration module is used for generating an assembly topological structure and a connection sequence of a system shelf according to the component candidate set output by the semantic reasoning and constraint engine module; and the man-machine interaction and visualization module is used for supporting a feedback closed loop between the engineer and the system and providing a visual display interface. The problems that an assembly method depends on artificial experience and is difficult to support high-frequency and multi-scene production line reconstruction are solved, and the method has higher semantic interpretation capacity, automatic combination capacity and context adaptive capacity.

The invention provides a multi-agent strategic scenario generation system based on a large language model, and the system comprises the following steps: carrying out the modeling of a scene and a system, and carrying out the system modeling of an attempt to record a case, deduce a situation, an event, a scenario, a strategic-battle-tactical-level red-blue party subsystem and a white party subsystem; the method comprises the following steps: initializing a red party attempt to record cases and a blue party attempt to record cases, when a user uses the system, prompting that relevant information of military deduction needs to be input, and after the information is input according to requirements, the system uses a global attempt record generation agent to receive the information input by the user and generate an attempt record in a JSON format. According to the multi-agent strategic scenario generation system based on the large language model provided by the invention, the strategy-battle-tactical multi-level agent vertical cooperation relationship-based game subsystem is designed, and the task granularity of scenarios designed by the large language model is refined through the multi-level game subsystem; and the scenario design quality is effectively improved.

The invention provides an industrial defect image generation system and method based on deep learning, and relates to the technical field of artificial intelligence. A defect form adaptive module, a physical attribute modulation module and a multi-mechanism fusion module are integrated in a defect image generation module; dynamically selecting a feature extraction unit according to the defect type label based on a pre-constructed generative network model, and generating a defect feature map; generating an affine transformation parameter based on the physical attribute vector, and performing channel-by-channel linear modulation on a defect feature map of a middle layer of the generative network model, so that a multi-scale defect feature map finally generated by the generative network model contains specified defect type features and physical attribute features; and fusing the multi-scale defect feature image and the defect-free background image to obtain a fused defect image. The problems that in the prior art, industrial defect image generation is insufficient in sense of reality, poor in controllability and poor in fusion effect are solved, the method can be used for data enhancement of industrial visual inspection, and downstream model performance is improved.

The invention discloses a construction method of an expert system based on an RAG technology. The construction method comprises the following steps: step 1, constructing a knowledge base; step 2, constructing a retrieval system; and step 3, generating system construction. In practical application, the retrieval system can take out corresponding knowledge fragments from the knowledge base according to problems of users; the generation system selects a correct answer most conforming to logic according to the knowledge fragment obtained through retrieval, namely a text block, and describes the correct answer by using a smooth language; the generation system includes a large language model. The application of the invention can simplify the maintenance of the knowledge base and provide more friendly and more logical answer presentation, thereby generating a better new generation expert system.

Machine learning based processing systems and techniques are described. In some examples, a machine learning based processing system analyzes text-based input to extract a plurality of natural language elements from the text-based input. The text-based input is associated with an object. The machine learning based processing system generates a prompt from at least a subset of the plurality of natural language elements. The machine learning based processing system analyzes the prompt by using a trained machine learning model to generate a response. The response is responsive to the prompt. The machine learning based processing system analyzes the response to extract a plurality of media content elements from the response. The plurality of media content elements corresponds to different aspects of the object.

The invention provides an intelligent cartoon line manuscript generation system capable of keeping human face features. The system comprises: 1, a backbone network, which is used for receiving cue word embedding representation input, controlling network embedding representation input, and generating a first draft of a cartoon line draft; the control network is used for receiving the human face, the human face key point embedded representation and the content image embedded representation, and generating a control network embedded representation; 3, four encoders of prompt words, human faces, human face key points and content images respectively generate corresponding embedded representations; and 4, a later-stage line draft adjustment module which performs local and overall style adjustment on the cartoon line draft generated by the backbone network by using a multi-layer generative adversarial network architecture to generate a final cartoon line draft finished product. According to the invention, the cartoon line manuscript with the specified face features can be efficiently generated, and the production efficiency of cartoon products is improved.

According to the intelligent imaging generation system and method, parameters of a spraying mechanical arm and imaging equipment are adjusted in a linkage mode in real time through a dynamic parameter adjusting module, and closed-loop control over the spraying quality is achieved in combination with a defect prediction and optimization decision module and a multi-modal process knowledge base. A high-resolution visual sensor, a capacitive thickness detector and a temperature and humidity sensor are integrated in the system, multi-source data are fused through a Kalman filtering algorithm, and the mechanical arm speed, the nozzle pressure and the imaging gain coefficient are dynamically adjusted. The defect prediction module simulates a coating defect form based on a physical constraint generative adversarial network and generates a process optimization instruction set through reinforcement learning. The multi-modal process knowledge base stores a correlation model of material characteristics, environmental data and defect modes, and supports incremental learning and historical data attenuation updating. And through dynamic parameter collaborative optimization, defect prediction preposition and multi-modal data fusion, the spraying quality detection efficiency and the coating consistency are improved, and the rework rate is reduced.

The invention relates to the technical field of intelligent abstract generation, in particular to an intelligent archive abstract generation system and method based on a natural language processing technology. The system comprises a multi-layer semantic generation core unit which performs semantic hierarchical segmentation analysis on an original file text to construct a multi-layer semantic graph, and constructs an abstract generation model to generate abstract content; the knowledge graph fusion engine unit associates and extracts terms in an original file text, constructs an entity mapping relation, and sets semantic graph node expression weights in an abstract generation model according to semantic association confidence scores; a paging index cache retrieval unit performs fragment division processing on each node in the multi-layer semantic graph, and constructs an abstract content fragment index structure containing a node set; and the version tracing transaction management unit carries out version recording on generation and modification operations of the abstract contents. The invention discloses an intelligent archive abstract generation system which is constructed by fusing a multi-layer semantic graph and is associated with a knowledge graph.

A method, computer program product, and computing system for processing a query provided to a generative AI model. A content portion retrieved by a Retrieval Augmented Generation system for the query is processed. User context information associated with a user providing the query is determined. Hyperparameters are generated for processing the prompt with the generative AI model by processing the query, the content portion, and the user context information using run-time surrogate model inversion optimization.

The invention provides a professional online collaborative editing method and system for hydropower project cost schemes. The professional online collaborative editing method comprises a standard management process, an intelligent collaborative editing process and a release state synchronous updating process. The standard management process comprises the steps of performing automatic analysis and data standardization conversion on an industrial standard file based on a deep learning model, generating a system rule set and storing the system rule set in a database; and based on the system rule set, automatically generating a standardized filling template of each specialty. According to the micro-service architecture-based intelligent online collaborative editing method and system for the hydropower engineering, a natural language processing NLP technology, a deep learning algorithm and a transaction management mechanism are combined, and the system has the functions of real-time grammar and spelling inspection of filled content, intelligent recommendation, semantic understanding, real-time difference detection, multi-user conflict processing and version control; the problems that communication cost is high, version control is difficult, data verification is not strict and multi-user cooperation is not smooth in water-power engineering cost scheme compiling are solved.

The invention relates to the field of digital content generation, and discloses an AI-based digital cultural creative content dynamic generation system, which comprises a user behavior data collection module used for acquiring interactive behavior data of a user on digital cultural creative content and generating a user feedback signal; the state modeling and user preference analysis module is used for constructing a user state according to the user feedback signal and analyzing user preference information; the style control vector generation module is used for generating a style control vector according to the user state, and the style control vector is used for regulating and controlling style features of the content to be generated; and the content generation module is used for receiving the style control vector and the user state information and generating the digital text creation content. According to the method and the system, through combination of the user behavior data and the emotion feedback signal, the system can dynamically adjust the style control vector, accurate personalized customization of the content style is realized, and the requirements of different users are better met.

The invention provides an intelligent contract examination and generation system based on an AI large model. The intelligent contract examination and generation system comprises the following steps: S1, data processing; s2, constructing a knowledge base; and S3, model training architecture. According to the intelligent contract examination and generation system based on the AI large model, the seriousness requirement of laws and regulations is met, the accuracy of the AI model in the law field can be greatly improved through close cooperation with a law expert team and fine adjustment of mass data, errors and illusion are reduced, the law compliance of the contract is concerned, and the law compliance of the contract is improved. More attention is paid to specific requirements and sites of users, customer interests are protected, contract terms better conforming to the user interests are generated by a tool by analyzing service backgrounds and requirements of the users, user interface design is simple and visual, the users can easily use the contract terms without professional legal knowledge, the users only need to describe the requirements through natural languages, and user experience is improved. Therefore, the contract can be examined and generated, the use threshold is greatly reduced, and the user experience is improved.

The invention relates to the technical field of artificial intelligence and software development, in particular to a DevOps assembly line intelligent generation system based on natural language description. According to the intelligent generation system for the DevOps assembly line based on the natural language description, an interaction layer is responsible for visually editing an interaction interface and outputting a generated executable script and a flow chart to a user; the intelligent processing layer takes Python and Go as development languages, fuses a knowledge graph technology, supports a dynamic code generation capability and realizes intelligent business process management and automatic execution; the infrastructure layer comprises three core modules, namely a metadatabase, a sandbox environment and an execution engine, and is responsible for providing basic support and resource guarantee for system operation. According to the intelligent generation system for the DevOps assembly line based on the natural language description, the configuration threshold of the DevOps assembly line is remarkably reduced, the cross-platform compatibility is ensured, the safety and reliability of the assembly line are greatly improved, and production accidents caused by manual configuration errors can be effectively avoided.

The invention relates to the technical field of financial risk assessment, in particular to an industry report automatic generation system based on a large language model, and the system comprises the steps: obtaining structured and unstructured data of each real estate enterprise through a dynamic multi-source data collection module, and constructing a first multi-source credibility coefficient; constructing a first comprehensive data set based on the coefficient and a data collection strategy; the risk assessment model construction module constructs an enterprise financial risk assessment model to identify the first comprehensive data set, finance risk anomaly features are obtained, and then a multi-source financial risk coefficient is generated; the financial risk assessment and report generation module generates a comprehensive financial risk score according to the multi-source financial risk coefficient, and outputs an automatic report according to the comprehensive financial risk score sequence of the real estate enterprise; according to the invention, by constructing the enterprise financial risk assessment model, intelligent analysis and report automatic generation of real estate enterprise financial risks can be effectively realized.

Systems and methods for using a generative artificial intelligence (AI) model to generate a suggested draft reply to a selected message. A message generation system and method are described that use guardrails that prevent unnecessary AI model processing and accidental sending of an AI model-generated draft. In some examples, draft reply-generation is limited to a subset of messages (e.g., focused, non-confidential) and triggering of the draft reply generation is performed only after user interaction criteria are satisfied. In some examples, a confirmation message is presented when the draft reply is attempted to be sent with no changes or quickly after the draft is generated. For instance, the guardrails limit the number of times the AI model is invoked to generate suggested replies and further prevents users from accidentally sending drafts generated from the AI model.