123 results about "Specific knowledge" patented technology

The invention provides a large model cue word design method, system and device in an industrial scene and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a private cue word template library, and forming a domain special knowledge unit set through multi-source industrial data collection, intelligent semantic analysis and classified storage; customizing a four-layer structured template, sequentially establishing a task overview layer, a step disassembly layer, an instruction refining layer and an output specification layer, and constructing a layered mapping model from task definition to output execution; template library management and iterative optimization are carried out, and dynamic updating and performance improvement of a cue word system are realized through centralized system management, multi-source feedback acquisition and data-driven optimization. According to the method, a cue word design and optimization system based on a hierarchical structure is constructed for complex task requirements in an industrial scene, and the task processing performance of a large model in scenes of industrial production, quality detection, equipment operation and maintenance and the like is improved.

Systems, methods, and computer-readable media provide a context-specific prompt to answer a user query. The systems, methods, and computer-readable media determine a context based on content of a natural language request and / or determine a role of a user who submitted the natural language request. Additionally or alternatively, templates or RAG sources that will be used for prompt generation may include financials domain-specific knowledge or other domain-specific knowledge or insights. Inclusion of this additional information in the prompt enhances the context to promote more accurate results from a large language model. In one embodiment, the prompt templates are created from various RAG sources, such as payables, general ledger, receivables, and asset management, containing structured data and information specific to the financial domain, enterprise, or other domain, which helps craft accurate prompts. A prompt is generated that identifies a subset of available fields and other selected information based on the role or other context. The prompt template may contain domain-specific knowledge uses a relevant domain or enterprise information to drive relevant results, and an executable query is generated by a large language model based on the prompt. The executable query causes data to be retrieved from a database to generate a result, and information is displayed based at least in part on the result.

Automated computer systems and methods to determine a sentiment of information in digital information or content are disclosed. One aspect includes deriving, by a processor, the digital information from a source; generating, by the processor, a domain-specific machine learning sentiment score, based on the digital information, by one model of at least two machine learning models; autonomously mapping, by the processor, a non-domain specific knowledge graph of associations between elements in a set of digital contextual information; receiving, by the processor, sentiment graphs, each sentiment graph defining a sentiment; generating, by the processor, a graph sentiment score based on the non-domain specific knowledge graph and the sentiment graphs; generating, by the processor, a final sentiment score based on the graph sentiment score and the domain-specific machine learning sentiment score; and determining the sentiment of the information in the digital information or content via the final sentiment score.

The invention provides a complex scene-oriented end-to-end semantic extraction system, belongs to the technical field of artificial intelligence and natural language processing, and realizes cross-modal information association through a multi-source heterogeneous data fusion module to construct a dynamic semantic network model. A hierarchical attention mechanism is adopted to carry out context-aware coding on unstructured input, and unsupervised pre-training and a weak supervised fine tuning strategy are combined to optimize a feature representation space. And designing an adaptive inference engine, automatically switching semantic analysis paths based on scene complexity, and generating a structured output result. According to the method, the dependency on specific knowledge in the field is reduced, the semantic understanding generalization ability in a complex scene is remarkably improved, high-precision analysis performance can still be kept in a low-resource environment, meanwhile, calculation resource consumption is reduced, and the method is suitable for practical application scenes with multi-language mixing, serious noise interference and high real-time performance requirements.

The invention relates to the field of artificial intelligence, the technical scheme is applied to the financial field and the medical health field, and discloses a federated learning-based large model optimization method and device, computer equipment and a storage medium, and the method comprises the steps of receiving a global model from a server; performing local training on the global model through local data, keeping weight freezing of the basic model during local training, and updating the first low-rank matrix and the second low-rank matrix; sending the updated first low-rank matrix to a server, so that the server aggregates the updated first low-rank matrix of each client to obtain a global low-rank matrix; and receiving the global low-rank matrix from the server, generating a weight update quantity to update the weight of the local adapter of the global model, and obtaining an optimized global model. According to the scheme, the communication efficiency can jump; the second low-rank matrix solidifies client-side specific knowledge to prevent global knowledge pollution; and a weight merging mechanism in the reasoning stage ensures zero delay spread.

Techniques for constraining the results of a generative language model to valid information using knowledge-grounded documentation. A generative language model may generate invalid results, including compound entities and incorrect entity relations. The techniques include, for a given user inquiry, determining a set of documented information, from a particular knowledge base, that corresponds to the user inquiry. The techniques further include determining a subgraph from a knowledge graph representing the knowledge base, as well as determining a trie data structure representation of the set of documented information. The user inquiry and subgraph are provided as input to a trained generative language model for generating a response to the user inquiry. The techniques include using the trie data structure to validate that the generated response corresponds to real information from the set of documented information.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a security detection method and system based on a large language model and retrieval enhancement, and the system comprises an IDE plug-in module and a security language server SLS module; the IDE plug-in module is used as a front end for interaction between a user and a system, is directly integrated in a development environment IDE, and communicates with a security language server SLS module at a rear end by using an API provided by the IDE to realize real-time capture of code context and visual presentation of an analysis result; the method has the advantages that the strong code understanding ability of the LLM and the specific knowledge of the field provided by the RAG are combined, the knowledge limitation of the universal LLM in the professional security field is effectively relieved, and the detection accuracy and the relevance of suggestions are improved. The risk can be detected, detailed explanation and specific repair suggestions can be provided, and the understanding and repair cost of developers is reduced.

The invention relates to the technical field of code generation, in particular to a knowledge-adaptive code retrieval model, method and system. The knowledge adaptive code retrieval model provided by the invention comprises a code language selection module, a code knowledge module, a general knowledge module and a feature fusion module, the code language selection module and the feature fusion module are respectively connected with the code knowledge module and the general knowledge module; the code language selection module selects appropriate code languages for different user questions, the various code knowledge modules learn specific knowledge of each code language, and the universal knowledge module captures user intentions or universal knowledge of various code languages, so that under the interaction of the universal knowledge module and the various code knowledge modules, the user questions can be learned through the code language selection module. And the learned feature embedding is enabled to be richer and more professional, so that the accuracy of a code retrieval task is improved. And the code language selection module adaptively selects a proper code language for the user problem, and considers the applicability and the applicability degree of each code language, so that the code retrieval accuracy can be improved.

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

The invention discloses a multi-modal rumor detection system based on multi-field perception, relates to the technical field of rumor detection, and is used for solving the technical problem of low multi-field rumor detection precision in the prior art. The rumor detection system comprises a multi-view feature extraction module, a multi-domain perception module, a multi-level knowledge fusion module and a final classifier. Wherein the multi-view feature extraction module is used for respectively extracting corresponding single-mode features from a text mode and an image mode, and acquiring aligned image and text features as multi-mode view features; the multi-domain sensing module is used for learning cross-domain shared knowledge and domain specific knowledge at a text view angle, an image view angle and a multi-mode view angle; the multi-level knowledge fusion module is used for fusing cross-domain shared knowledge and domain specific knowledge of a text view angle, an image view angle and a multi-mode view angle; and the final classifier is used for obtaining a prediction result according to the cross-domain shared knowledge and the domain specific knowledge obtained by the multi-level knowledge fusion module.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an enterprise special agent rapid construction method based on large model fine tuning. The method comprises the following steps: S1, performing agent basic configuration; s2, outputting an enterprise special agent based on the agent basic configuration scheme and the general large model; s3, setting rule parameters of an agent response strategy, and generating an agent interaction strategy; s4, performing full-life-cycle management on enterprise knowledge entries to ensure the accuracy and traceability of agent answering; and S5, verifying the accuracy of the agent knowledge and continuously optimizing the agent knowledge. According to the invention, through low-code configuration, an enterprise can construct an agent without a technical team, and autonomous controllability is realized; a large model adaptation technology enables the intelligent agent to accurately understand proprietary knowledge, solves the problem of term misunderstanding, and achieves high-precision adaptation; the knowledge full-life-cycle management and rapid updating mechanism ensures the timeliness of the agent knowledge; customizable interaction rules enable the intelligent agent to adapt to multi-scene requirements, and the user experience is improved.

Context-aware content filtering adapted for a knowledge domain is provided. In certain examples, a classification confidence score by a classifier indicates a level of confidence that a prompt from a user is associated with the knowledge domain. The classification confidence score is compared with a threshold. When the score is below a threshold, a violation notice is provided to the user without submitting the prompt to a generative artificial intelligence (GAI) model. When the classification confidence score is above the threshold, the prompt is further processed to determine, according to rules, whether the prompt should be submitted to the GAI model. In various examples, the rules are applied to contextual information, safety score information, and intent information derived from the prompt.

Collecting task-specific knowledge for a classification task by AI in the industrial environment requires a high degree of expert knowledge about the respective area. For the purpose of formalisation and embedment in the machine learning model used, ML experts are however required. Close cooperation between data scientists and domain experts for each specific application is a prerequisite. This process is very costly and prone to errors in the event of incorrect communication, and as a result, these approaches are practical only for a limited number of applications where the benefit of solving the problem with an ML approach with a high degree of domain knowledge integration is sufficiently high to cover this cost. The proposed method overcomes the above-mentioned hurdles by providing a way of using knowledge contained in LLMs in small application-specific models. This is done by distilling expert knowledge, which is combined into rules for the direct classification and / or extraction of relevant data features in LLMs using domain-specific input requests and is then used in small task-specific models during inference or training.

The present application relates to a translation system for natural medicinal material domain-specific knowledge. The system comprises: a front-end program, a first learning model, a search engine and a natural medicinal material domain-specific knowledge base. On the basis of performing coreference primary term annotation, and coreference annotation of standard translations in multiple languages on natural medicinal material terms in the natural medicinal material domain-specific knowledge base, the search engine is used to search in the natural medicinal material domain-specific knowledge base for a natural medicinal material term extracted by the first learning model from text to be translated, the first learning model generates a translated document in an original format on the basis of a search result, and finally, the front-end program displays a translation result in a target language in a mode matching a display mode of a user interaction interface. By means of the translation system in the present application, a user can conveniently and intelligently perform accurate and standardized translation on related terms and knowledge in the domain of natural medicinal materials on the basis of requirements of the user.

The invention provides a heterogeneous knowledge migration method and system based on redundancy suppression, and the method comprises the steps: respectively sending an input image into a heterogeneous teacher network and a heterogeneous student network, extracting the feature vectors of the networks, and obtaining a teacher feature vector and a first student feature vector; the first student feature vector is sent to a decoupling module, a second student feature vector consistent with the teacher feature vector in dimension is obtained, the first student feature vector is decoupled from a redundancy suppression optimization target, and specific knowledge of a student network is reserved; calculating redundancy suppression distillation loss based on the teacher feature vector and the second student feature vector; calculating cross entropy loss based on the first student feature vector and a truth value label of the input image; and summing the redundancy suppression distillation loss and the cross entropy loss as total loss, and training a student network and a decoupling module. According to the invention, the student network can avoid learning specific incompatible information of a teacher network architecture and only focuses on knowledge beneficial to the student network, so that the heterogeneous distillation effect is improved.

The invention discloses a robot autonomous assembly method and system based on imitation learning, a storage medium and computer equipment, and the method comprises the steps: constructing a universal expert strategy suitable for polygonal shaft hole assembly through analyzing and summarizing the polygonal shaft hole assembly experience of an expert; according to the strategy, task type images and human assembly demonstration data including assembly force / torque and assembly actions are collected; modeling a human assembly skill into a universal assembly skill model based on shared knowledge and specific knowledge; using a loss function guidance algorithm to train and optimize the general assembly model; and deploying the optimized general assembly model into a control system of the robot so as to drive the robot to execute humanization-like assembly. According to the method, the problems of poor generalization performance, slow convergence of an end-to-end learning mode and high cost of an imitation learning method can be solved, rapid learning of human skills can be realized, and humanoid robot autonomous assembly is realized.

The present disclosure relates to a technique for addressing an issue to be resolved associated with an electronic document. The method discloses accessing an actionable portion associated with a particular knowledge domain of the electronic document and associated context. Further, retrieve data from data sources to provide additional information related to the particular knowledge domain and the associated context. Then structuring the retrieved data to produce a subset of organized data and determine the issue to be resolved related to the electronic document. Further, generate data elements associated with the issue to be resolved and map dependency relationships between data elements. Also, determine test goals associated with the issue to be resolved based on the dependency relationships. Thereafter, determine corresponding test cases associated with resolution and determines actionable test steps related to the issue to be resolved based on the corresponding test cases associated with the electronic document.

Systems and methods for implementing domain-specific agent networks including configuring specialized agents optimized for retrieving information from a respective specific knowledge domain, receiving a user query, analyzing the user query to identify relevant knowledge domains, activating a subset of the specialized agents corresponding to the relevant knowledge domains, retrieving information by the subset of specialized agents, aggregating the retrieved information including information from a plurality of knowledge domains, providing the aggregated information to one or more h-LLMs, receiving a plurality of responses from the one or more h-LLMs, and generating a comprehensive response from the plurality of responses, the comprehensive response incorporating information from the plurality of knowledge domains.

An AI-based response generation chatbot system that acts as a digital replica of a person or expert, rather than being the expert itself, interacts with a user while being entirely guided by the information provided to it, without revealing its AI nature and the source of information. The AI-based response generation chatbot system includes a knowledge database initialized with knowledge documents containing expert knowledge with a specific viewpoint. The knowledge documents are compiled into a vector database through chunking and embedding techniques and are converted into unique topic-specific knowledge chunks in a machine-readable format. The compiled vector database further incorporates the Retrieval Augmented Generation (RAG) framework, enabling the retrieval of relevant information from the vector database and then using the retrieved information to frame accurate and contextually relevant responses aligned with user queries.

The present application relates to a system for acquiring natural medicinal material domain-specific knowledge. The system comprises a dialogue application program, a first learning model, a search engine, and a natural medicinal material domain-specific knowledge base. The dialogue application program provides a user interaction interface for a user, receives a user question of the user intending to acquire natural medicinal material domain-specific knowledge, and presents to the user an answer generated by the first learning model. The first learning model generates an answer to the latest user question on the basis of a dialogue history of the user, or uses the search engine to perform information retrieval on the natural medicinal material domain-specific knowledge base by employing at least one of coreference-based graph search, vector search and full-text search, to acquire background knowledge associated with the user question, and generates an answer to the user question on the basis of the dialogue history embedded with the background knowledge. According to the system of the present application, the user can intelligently and accurately acquire required authoritative, accurate, standardized and comprehensive natural medicinal material domain-specific knowledge in a friendly dialogue mode.

The invention discloses a personalized learning portrait construction method fusing multi-source data and state updating, and belongs to the field of student portrait construction. The method comprises the steps of firstly collecting learning behavior data of a student, calculating a mastering probability value of the student for each knowledge point in a knowledge graph based on a preset evaluation model, and mapping the mastering probability value into an explicit mastering state; active interaction behaviors of the students are monitored in real time, the self-evaluation mastering state is deduced, and whether correction of the explicit mastering state of the specific knowledge points is triggered or not is judged according to the state difference degree; and taking the corrected mastering state of the knowledge point as an updating trigger point, calculating an expected influence quantity of the mastering state change of the corrected knowledge point on the associated knowledge point based on the topological structure of the knowledge graph, and updating the mastering state of the associated knowledge point according to the expected influence quantity, thereby generating an updated personalized learning portrait. According to the method, the mastering state of the associated knowledge points is dynamically and intelligently updated, and a solid technical support is provided for self-adaptive learning path recommendation.

The application provides a kind of instruction driving machine task planning method and system based on knowledge model, and the specific knowledge of field is structuredly represented by knowledge model, and the object of class level, instance level of field knowledge and its logical relationship are represented;Further, the semantics of instruction is understood, and then the task planning problem represented by instruction is normalized, specifically including task planning object and its mutual constraint relationship;On this basis, further consider the space-time constraint between task objects and the measurement and evaluation of task efficiency.Finally, through atlas and visual graph display, give the pareto optimal scheme under multi-dimension, support intelligent or man-machine interactive decision, realize man-machine interaction in task planning, and output field-specific reliable scheme based on natural language.

Disclosed herein are systems and method for using machine learning to generate / provide supplemental information material for a presentation. In an aspect, presentation material associated with the presentation is analyzed using a knowledge model to identify one or more elements in the presentation material, the one or more elements being associated with one or more concepts related to the presentation and / or related to a specific knowledge area of the knowledge model. For each element of the one or more elements, supplemental information material related to a concept associated with that element is created. A table that links each element of the one or more elements with corresponding supplemental information material is generated. The table links each element in the one or more elements with a position of that element in the presentation material.

The invention discloses domain adaptation for automatic speech recognition systems using retrieval enhancement generation. The method presented herein provides for the generation of a textual transcript of speech represented in audio data. In particular, an Automatic Speech Recognition (ASR) model may be used with a Retrieval Enhanced Generation (RAG) pipeline to provide improvements in transcripts including terms related to or specific to a particular knowledge domain. A knowledge base of a given domain may include a number of a plurality of different formats of files or documents (e.g., documents, images, and web pages) that do not need to be cleaned, classified, or curated. When the ASR generates a transcript for which the confidence level of at least one word is below a confidence threshold, the transcript may be passed to a language model of the RAG pipeline that may use the retrieved domain-specific data to attempt to identify an appropriate word or term for replacing a word tagged as having a low confidence.

This invention provides a method, apparatus, and electronic device for multi-turn conversations. The method includes: generating a knowledge matrix comprising multiple items, where each element represents a knowledge point corresponding to a specific knowledge type within that item; extracting the target knowledge point from the current round of information; generating a response for the current round when the target item in the knowledge matrix cannot be located based on the target knowledge points from all current rounds, until the target item is located based on the target knowledge points from all current rounds; and outputting the content result corresponding to the target item. The multi-turn conversation method, apparatus, and electronic device provided by this invention can generate a knowledge matrix with a simple structure; it does not require accurate entity identification, nor does it require attention to the relationships between entities, making the generation process simple. Even in complex application scenarios involving a large number of items, multi-turn dialogues can still be effectively implemented based on this knowledge matrix.

An improved knowledge graph for augmenting queries in a retrieval augmented generation system for generative artificial intelligence is constructed using entity data comprising information regarding a first entity of a first entity type and a second entity of a second entity type, the first and second entity types being defined by a domain-specific ontology for a knowledge domain. Relationship data for the knowledge graph comprises information regarding relationships between the first and second entities using relationship definitions from the domain-specific ontology. The knowledge graph is constructed by adding nodes corresponding to the entities and edges corresponding to the relationships. In some examples, the graph is used to identify cybersecurity threats applicable to a specific context.

The application provides an enhanced structured federated graph learning method, and belongs to the technical field of deep learning.The method comprises customer contribution evaluation, parameter adjustment and knowledge forgetting;the customer contribution evaluation evaluates customer contribution by using a reputation theory;the parameter adjustment adjusts key parameters by using an attention mechanism and an entropy weight method, reduces aggregation errors and optimizes global model performance;the knowledge forgetting processes data forgetting requests by combining soft confusion and hard confusion loss, and ensures that specific knowledge is forgotten without affecting overall performance.The application solves the data isolation problem in large-scale graph data training, and solves the problem of non-independent and identically distributed data and diversified local model features;even if the reliability of participants is low, the accuracy can still be guaranteed;and specific knowledge is removed from the client and propagated to the global model, which well responds to the forgetting request proposed by the user.The application significantly improves the accuracy of the global model, and better maintains the model precision after meeting the forgetting request.

The embodiment of the invention relates to a context-aware and stateless deep learning auto-tuning framework. Systems and methods are provided for improving auto-tuning procedures using stateless processing with a remote key value store. For example, the system may implement task launchers, schedulers, and agents to launch, schedule, and execute decomposed auto-tuning phases, respectively. Scheduling policies implemented by the scheduler may perform operations beyond simple scheduling policies (e.g., FIFO-based scheduling policies), which may result in high queuing latency. By utilizing auto-tuning domain-specific knowledge, queuing latency is reduced and resource utilization is improved compared to conventional systems.

The invention relates to a bilingual network threat intelligence relation extraction-oriented large language model optimization method and system, and the method comprises the steps: generating an enhanced data set according to an example set, and combining the example set and the enhanced data set to form a training set; the performance of a plurality of LLMs models is tested under various conditions, task performance and instruction following ability are extracted based on the relation of the models on a Chinese and English data set, and a basic model is screened out. And carrying out fine tuning on the screened model by adopting LoRA, and correcting the output by the LoRA through two trainable weight matrixes and by introducing an increment updating item containing task specific knowledge. Generating a dynamic instruction with a cluster exclusive feature for each test sample based on cluster affiliation, retaining a part of candidate examples with the highest correlation, screening out a part of examples with the highest correlation from the training set, constructing a sample subset under redundancy constraints, and performing cluster classification; and finally, fusing the cluster exclusive instruction with the demonstration examples for enhancing diversity in the sample subset by the cue word.

The present invention provides a system and method for identifying, analyzing, and determining major accident hazards based on a large AI model, wherein the system includes: an information acquisition subsystem for acquiring information to be identified in a detection area; a model training subsystem for training an AI large model for identifying accident hazards; a knowledge graph construction subsystem for constructing an industry-specific knowledge graph; and a hidden danger identification subsystem for performing identification, analysis, and determination of major accident hazards based on the AI ​​large model for identifying accident hazards and the industry-specific knowledge graph and the information to be identified. The system and method for identifying, analyzing, and determining major accident hazards based on a large AI model of the present invention trains an AI large model for identifying accident hazards and constructs an industry-specific knowledge graph; after acquiring the information to be identified, the model automatically extracts effective trigger words through natural language processing technology combined with the industry-specific knowledge graph, optimizes hidden danger inference rules, and automatically performs hidden danger inference, thereby improving the accuracy of hidden danger identification.