122 results about "User knowledge" patented technology

The invention relates to the technical field of knowledge graph question answering, and discloses a knowledge graph retrieval reasoning method and system based on big language model enhancement, and the method comprises the steps: carrying out the entity extraction of a user question through a big language model, and generating a candidate entity list; retrieving entity nodes corresponding to the candidate entity list in a knowledge graph database through a mixed retrieval method of fuzzy matching and vector retrieval; re-screening the entity nodes by using a large language model to generate a starting node list; performing width-first traversal on each node in the starting node list in an edge-in direction and an edge-out direction, and retrieving a reasoning path in the knowledge graph; constructing the reasoning path into a prompt template, and inputting the prompt template into a large language model to generate an answer to the user question; according to the method, the big language model is utilized to analyze questions and query the knowledge graph in the knowledge graph question-answering scene, question-answering is performed based on the knowledge graph path, and the effectiveness and accuracy of user knowledge graph question-answering are improved.

The invention provides a cue word generation method and device, equipment, a medium and a program product, and relates to the technical field of computers, in particular to the technical field of artificial intelligence, natural language processing and deep learning. According to the specific implementation scheme, user input content is obtained; based on constraints of a user knowledge base and / or a user intention rule base, adopting an intention recognition model to perform cue word intention recognition on user input content, and outputting cue words reflecting user intentions; and inputting the cue word into the cloud model to obtain an output result. According to the embodiment of the invention, the generation accuracy of the cue word can be improved.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention provides a control system and method applied to a smart home terminal and a terminal, is applied to the technical field of smart home Internet of Things, and has the advantages that a user behavior prediction model driven by machine learning and a dynamic rule generation engine are introduced, so that the system can break through a traditional three-level linkage mechanical execution framework; an intelligent control closed loop with environment perception, intention reasoning and autonomous decision-making capabilities is constructed, multi-dimensional data fusion analysis from equipment state parameters and user historical operation tracks to real-time environment variables is realized, and a composite control strategy including equipment linkage sequence optimization, scene parameter dynamic calibration and abnormal operation self-correction is automatically generated. And in combination with a federated learning architecture, cross-user knowledge migration and group behavior pattern mining are realized, and the generalization ability and scene adaptability of a control strategy are continuously improved.

Techniques for creating and updating natural language summaries representing personalized user knowledge (e.g., user interests, user affinities, user preferences, family structure, routines, and other insights) based on conversational interactions with and other natural language content available to an AI system are described. In some embodiments, to provide a more personalized service, a system can use a generative model to summarize learnings about a user and determine helpful nuanced insights about the user such as “the user is learning how to play guitar.” This “user knowledge” can be updated based on further (later) conversations with the user, where updating can involve negating or deleting stored information, adding to or modifying stored information, etc.

The invention discloses an intelligent personalized topic recommendation method based on a knowledge graph, and relates to the technical field of information retrieval, and the method comprises the steps: constructing a multi-dimensional subject knowledge graph which defines knowledge point entities and topic entities through the knowledge graph, and establishes structural relationships and capability dimension attributes between the entities; forming a structured knowledge basis for recommendation; collecting learning behavior data of the user, and generating a user knowledge state model for dynamically evaluating the knowledge state of the user in combination with the multi-dimensional subject knowledge graph; selecting a corresponding recommendation strategy according to an output result of the user knowledge state model, and performing multi-dimensional question matching based on capability dimension matching based on the multi-dimensional subject knowledge graph to generate a personalized question recommendation scheme; and dynamically optimizing the multi-dimensional subject knowledge graph, the recommendation strategy and the matching rule based on the feedback of the user to the recommendation scheme. The problems that a traditional recommendation system is shallow in knowledge association, rough in diagnosis and rigid in strategy are solved.

The invention discloses a big data-based civil and commercial law learning recommendation method and system, and the method comprises the following steps: obtaining user learning behavior data and civil and commercial law knowledge base data, and constructing a multi-dimensional learning feature data set; performing standardization processing on the multi-dimensional learning feature data set to generate a structured feature matrix; constructing a user knowledge mastery degree portrait based on the structured feature matrix, and generating a user-knowledge point association graph; constructing a mixed recommendation model according to the user-knowledge point association graph, and generating a personalized learning path; and receiving user feedback data in real time, updating recommendation model parameters, and dynamically adjusting a learning path. According to the method, data refined modeling is realized through a multi-dimensional learning feature data set and an improved TF-IDF algorithm; optimizing learning path adaptation based on a dynamic weight fusion recommendation model and a real-time feedback mechanism; high concurrent processing and content security are guaranteed by means of a modular architecture and a compliance verification unit, and finally an efficient, self-adaptive and compliance intelligent recommendation system is constructed.

The invention discloses an e-commerce platform commodity evaluation analysis method based on multi-source data mining. The method comprises the following steps: S1, collecting data of each platform; s2, performing data preprocessing on the text data of the platform; s3, carrying out data analysis on the preprocessed text data, splicing LDA features and BERT features, carrying out standardization, and providing suggestions for commodities through results of sentiment analysis, evaluation clustering analysis and user behavior analysis; and S4, continuously monitoring and optimizing the commodity, and establishing a user feedback mechanism to collect feedback output by the user to the model to guide the optimization direction. According to the method, deeper-level relations and laws among data are mined, and comprehensiveness and diversity of the data are ensured; potential negative evaluation and product problems are accurately found and processed, and targeted improvement is carried out; demands and problems of the user are responded in time, and customer service experience is improved; data analysis and learning are continuously carried out, and precious market and user knowledge is accumulated.

The invention discloses a personalized semantic evaluation system combined with a knowledge graph, and relates to the technical field of educational evaluation, and the system comprises the steps: obtaining a pre-constructed mathematical knowledge graph and a preset mathematical knowledge point set, and determining a target knowledge point frame; obtaining a test paper sequence and a test paper standard answer sequence of the target user in historical time, and performing matching analysis to obtain a test question score set sequence; performing test question semantic analysis, and determining a test knowledge point set sequence; respectively mapping and inputting the test knowledge point set sequence and the test question score set into a target knowledge point framework to obtain a test knowledge point score framework sequence; and performing iterative association interaction analysis, determining a target interaction test knowledge point score framework, performing analysis, and determining a target mastery degree evaluation result. The technical problem that the knowledge point mastering degree of a user cannot be accurately evaluated in the prior art is solved, and the technical effect of providing an accurate basis for learning condition evaluation is achieved.

The invention discloses a task processing method and device, equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a to-be-processed user intention; based on the user intention and a pre-constructed user knowledge graph, performing task decomposition processing on the user intention to obtain a target task process corresponding to the user intention; determining a tool of the target task flow; based on a tool of the target task process, tool context information corresponding to the target task process is acquired from the pre-constructed user knowledge graph, and the tool context information comprises a tool calling condition; and based on the tool and the tool context information of the target task process, performing execution processing on the target task process to obtain a processing result of the user intention.

The invention relates to the technical field of intelligent recommendation, in particular to a teaching content recommendation method and system driven by online education platform data. The invention discloses a teaching content recommendation system driven by online education platform data. The teaching content recommendation system comprises a knowledge point mastering feature data construction module, a mastering degree analysis module, a gap knowledge point determination module and an education content recommendation module. The knowledge point mastering degree of the user is determined by analyzing the evaluation data of the user, the education content knowledge graph is analyzed through the mastering degree analysis model, the dependency relationship between the knowledge points is researched, the deeper knowledge point mastering degree is analyzed, and then more accurate knowledge point mastering degree judgment is obtained. And then determining notched knowledge points with low knowledge point mastering degree of the user based on the knowledge point deep mastering feature data, and determining an educational content learning path needing to be recommended through the notched knowledge points, so that educational content recommendation for checking and repairing the missing of the user can be realized.

A mastery level determining method to assess user mastery of educational skills by centralizing and processing data from diverse educational platforms is disclosed. The method involves collecting user educational data from various sources such as online learning platforms, external assessments, and internal quizzes. This data is then normalized to adapt to the educational standards and ingested into a structured mastery framework. The recent and reliable data is evaluated to determine skill mastery states, prioritizing newer and more reliable information. Subsequently, reliability and recency scores are calculated and assigned to each educational data. The method further categorizes user mastery skills into distinct states, providing an overview of the user's knowledge. The simplified mastery states and associated tags are accessible to the user, facilitating personalized learning experiences and assessments within the online learning platforms.

The invention provides a method for joint answering of a user knowledge base and a service system under an AI platform, and belongs to the technical field of AI platform models.According to the method, hierarchical semantic matching is achieved by constructing a three-level knowledge vector cache and a three-level question intention vector cache and adopting vector representation of different dimensions; after user query is received, a semantic similarity algorithm is used for performing parallel retrieval to generate an initial matching degree, multi-dimensional similarity information is fused through a joint matching degree calculation function, and the precision is improved by using an intention recognition optimization model; calculating an optimal sequence length through an adaptive parameter adjustment function according to the long and short memory matching length to obtain real-time data of the service system, fusing the static answer of the knowledge base and the real-time data of the service system to generate a final answer, and updating a vector cache and optimizing algorithm parameters according to user feedback. And high-precision joint answering of the knowledge base and the business system is completed.

The present invention provides a multi-domain recommendation method based on adaptive knowledge enhancement of a large language model, including: obtaining sample data of a target user; constructing domain-shared prompt templates based on text information corresponding to multiple recommendation domains; constructing a unique domain prompt template based on the text information corresponding to the target user in the target domain and a user knowledge base; calling a large language model to perform feature learning based on the domain-shared prompt template and the unique domain prompt template to obtain domain-invariant features and unique domain preference features of the target user; constructing a multi-task model based on the domain-invariant features and unique domain preference features of the target user, optimizing and training the multi-task model to generate a multi-domain recommendation model, and completing multi-domain recommendations for the target user through the multi-domain recommendation model. By designing two types of prompt templates to train the large language model, the domain-invariant features and unique domain preference features of the target user are learned, and the prediction performance of the multi-domain recommendation model is effectively improved.

The invention discloses a learning assistance method, device and equipment based on a big language model and a storage medium, and the method comprises the steps: collecting historical interaction data of a user and the big language model, after desensitization processing and structured storage, extracting keywords and knowledge points through a natural language processing module, clustering similar problems and knowledge points, and carrying out the clustering of the similar problems and knowledge points; constructing a user knowledge graph containing knowledge point relevance and mastery score; generating a learning priority list based on the map score, and matching a preset knowledge base to generate a personalized learning plan; pushing the plan to a user side, and obtaining a learning and testing result to determine the knowledge mastery degree; and comparing the mastering degree with a preset threshold value, if the mastering degree is lower than the threshold value, regenerating the targeted plan, and if the mastering degree is not lower than the threshold value, marking the knowledge points as mastered and updating the graph. According to the scheme, knowledge blind spots in dialogues are dynamically tracked, conversion from passive question answering to active learning is achieved, and the knowledge mastering ability of a user is systematically improved.

The present invention relates to a learning assistance device, a program, a method, and a system. A learning assistance device 16 comprises: an output instruction unit 50 that instructs an output device (24) to present to a user, at a presentation timing for target content, learning presentation information (110, 120, 130) which relates to learning the target content; a data acquisition unit 40 that acquires result data (D2) which is obtained as hand-written input by the user with respect to the learning presentation information (110, 120, 130) presented using the output device (24); an retention degree calculation unit (82, 92) that performs an analysis process with respect to the result data (D2) and calculates the degree of retention of user knowledge relating to the target content; and a timing setting unit (84) that sets the next presentation timing for the target content in accordance with the calculated degree of retention.

The invention provides an intelligent customer service dialogue method and system for intellectual property legal consultation, and the method comprises the steps: firstly receiving an intellectual property legal consultation request of a user, and obtaining a consultation text sequence which comprises a plurality of legal question expressions and logic association information; performing intention analysis on the consultation text sequence, and extracting surface and deep appeal elements to form an appeal element set; calling an AI legal reasoning model to perform scene element matching based on the appeal element set, and generating a legal reasoning scene anchoring result; constructing a multi-round legal reasoning chain with the appeal element set as a starting node and the legal conclusion derivation as a termination node, and generating a multi-round reasoning process record; and the content of the reasoning node is dynamically associated with the provisions of the preset intellectual property legal basis library to generate a consultation response sequence containing the reasoning logic and the legal basis, and the consultation response sequence is fed back to the user, so that the efficiency, the accuracy and the comprehensiveness of intellectual property legal consultation are improved.

Provided is a method (100) and system (200) for recent user-activity image based authentication. The system collects (202) recent user-activity and location data, generates (214) user-knowledge events from the recent user-activity according to location data, generates (421) random image questions concerning the user-knowledge events based on location. The random image questions test the user's knowledge of their recent user-activity according to location. A generative Applications Programming interface (API) (140) parses (220) the answered image questions, queries (222) the database for an expected response to the answered image questions, and compares (224) the expected response to the answers received to authenticate the user. In some embodiments a DALL-E (161) component and a GPT-4 V (162) component are used together in conjunction for such purposes. Other embodiments are disclosed.

The invention relates to a method for realizing semantic chain retrieval enhanced generation processing for knowledge questions and answers of long documents. The method mainly comprises two parts of knowledge base construction and user knowledge questions and answers. User knowledge questions and answers are constructed based on a knowledge base, and knowledge base construction relates to long document preprocessing, semantic chain type storage, information compression and knowledge extraction and storage. When a user performs knowledge questions and answers, a retrieval vector is formed through fine-grained semantic coding, and then related document vectors and knowledge maps are matched in a knowledge base through vector similarity calculation. If the length of the document exceeds the context scale of the model, document information after static information compression is indexed through information compression, whether dynamic compression is carried out or not is evaluated, and document fragments related to tasks are returned through dynamic compression. Otherwise, directly returning the related original document as the background knowledge of the generator. And the model generator generates an answer based on the related document information / information after information compression, the knowledge graph information, the user question and a model prompt.

Provided is a method and system for recent user-activity image based authentication. The system collects recent user-activity and location data, generates user-knowledge events from the recent user-activity according to location data, generates random image questions concerning the user-knowledge events based on location. The random image questions test the user's knowledge of their recent user-activity according to location. A generative Applications Programming Interface (API) parses the answered image questions, queries the database for an expected response to the answered image questions, and compares the expected response to the answers received to authenticate the user. In some embodiments a DALL-E component and a GPT-4 V component are used together in conjunction for such purposes. Other embodiments are disclosed.

The invention relates to the technical field of artificial intelligence, in particular to an online state grid potential lost user identification method based on a knowledge graph. The identification method comprises the following steps: constructing a user knowledge graph; based on the user knowledge graph, constructing a relational graph convolutional neural network to deeply mine and analyze graph structure data, learning low-dimensional vector representation of entities and relationships in the knowledge graph, and capturing complex relationships between the entities and deep features of the graph structure data; two classifiers are constructed based on the extracted user features by reference to a multi-task learning network, an independent output layer is designed for each classification task, one is used for identifying whether the user has a loss risk, and the other is used for predicting the level of the user loss risk. According to the invention, the problem of how to improve the identification accuracy of the state grid user loss risk in the big data environment is solved; and the power consumer groups with potential loss risks are effectively identified, risk level subdivision of the users is realized, and then differentiated recall strategies are formulated.

The invention discloses a digital media art interactive experience system, which relates to the technical field of digital interaction and comprises a multi-mode sensing module, a dynamic content generation module, an interactive experience module and an intelligent recommendation module. The multi-mode sensing module is used for carrying out real-time positioning and micro-motion capture on a user; the dynamic content generation module is used for constructing an artistic work parameterized model library, deconstructing color, form and motion track elements into adjustable parameters, and training an interaction strategy network through reinforcement learning, so that the system autonomously optimizes the content according to an audience behavior mode; the interactive experience module is used for superposing and projecting the digital media art content, the generated dynamic content and the corresponding projection substrate to a physical space; and the intelligent recommendation module is used for constructing a user knowledge graph, dynamically adjusting art symbols and guide prompts, and recommending interested digital media art for the user. According to the application, the user participation degree can be improved, diversified dynamic interaction can be realized, and the cultural adaptability is relatively high.

The present disclosure relates to a system capable of increasing the accuracy of a prediction result when predicting a user drop-out rate or predicting a correct answer probability of a user in an online learning environment, and further relates to an operation method of the system. A system according to the present disclosure may include a plurality of encoder neural networks and a plurality of decoders, and may input question information to a kth encoder neural network and input response information to kth decoder neural network to learn the system, thereby predicting the user drop-out rate information and the users correct answer probability information with higher accuracy on the basis of the learned system.

The present application is applicable to the field of data processing technology, and provides a method and device for generating a recommendation list, including: extracting candidate documents associated with the user information from a document database based on the user information of a target user; determining the legal entities contained in each of the candidate documents based on a preset legal knowledge graph, and generating text feature vectors of each candidate document according to all the legal entities; obtaining the user knowledge graph of the target user, and generating browsing knowledge paths corresponding to each browsing record of the target user through the user knowledge graph; outputting content feature vectors of the browsing content according to the browsing content corresponding to each browsing record; generating a user feature vector of the target user based on the content feature vectors and browsing knowledge paths of all browsing records; determining recommended documents based on the user feature vectors and text feature vectors, and generating a recommendation list. The present application can improve the accuracy of recommended document selection and reduce the time spent by users searching for documents of interest.

The present disclosure provides a knowledge processing method, system, storage medium and program product, and relates to the field of artificial intelligence. The method comprises: constructing a user knowledge graph according to historical operations of each user on knowledge in a public knowledge base; processing the user knowledge graph to obtain a user feature vector and a knowledge feature vector; obtaining an embedding vector matrix of a plurality of candidate knowledge based on the user feature vector and the knowledge feature vector; and performing knowledge recommendation for a target user based on historical operations of the target user on knowledge and the embedding vector matrix of the plurality of candidate knowledge.

A system is capable of reducing effects of how much knowledge / experience a user has and making it easy for the user to decide a plan for work by a crane. It is possible to cause a user who has specified first carriage points through a terminal input interface to grasp an arrangement aspect and specifications of a crane for carrying materials to the first carriage points (or from the first carriage points) by the crane while avoiding interference of the crane and the materials with a building, through a secondary work situation image output to a terminal output interface of an information terminal.

This invention discloses an intelligent knowledge extraction system based on natural language processing, comprising: a text data acquisition module that acquires unstructured text and forms raw data; a text data preprocessing module that removes noise and standardizes the format of the raw data, outputting standardized text data; a text feature encoding module that converts the standardized text data into feature vectors that satisfy the input of an improved W2NER model; a knowledge extraction module that processes the feature vectors based on the improved W2NER model to obtain entity extraction results; an entity normalization module that performs ontology mapping and normalization on the entity extraction results; a knowledge fusion module that performs deduplication and fusion on the entity knowledge set; a knowledge storage module that stores the fused knowledge data in a structured manner; and a knowledge application service module that provides user knowledge query and reasoning services. This invention improves the accuracy of knowledge extraction and the efficiency of data application.

A first acquisition unit acquires user information of a target user who is the target of an interaction from a first database in which user information including at least information on a genre of interest and information on knowledge is registered for each user. A second acquisition unit acquires a list of episode data associated with the genre of interest of the target user from a second database in which episode data associated with at least one of a plurality of genres is registered. A generation unit uses the information on the knowledge of the target user acquired from the first database and the list of episode data acquired from the second database to generate a query instructing a language model to select episode data assumed to be of interest to the target user and about which the target user is assumed to have no knowledge.

To facilitate the formation of a shared understanding among stakeholders with different areas of expertise, and to enable work efficiency and cost optimization through optimal evaluation tailored to the characteristics of each component. [Solution] The user interface provider unit 51 provides the user terminal 2 with a user interface having a function to select an operation mode according to multiple user knowledge levels, a function to acquire target building information, and a function to present output information. The classification unit 52 classifies the components of the target building according to their location and arrangement based on the target building information. The evaluation unit 53 compares the evaluations of multiple evaluation methods for each classified component and selects an evaluation result using an appropriate evaluation method based on the comparison results. The output control unit 55 generates output information indicating the selected evaluation result for each classified component and executes control to present it from the user interface.

An auto-login system and process enable maintaining user accounts on a server without a user having to register or create a user name, password, or other authentication method. An account may be created without user knowledge. The server may transmit a content item to a target user, along with a link. A server identifies the target user from use of the auto-login link and collects interaction or “engagement” data while the user is logged in, to assess user interest in products, for example, a mutual fund investment product, which may be characterized by tags and / or categories. The system may quantify a product salience metric for a given product relative to a target user's interest profile to focus marketing efforts and support engagement with interested target users, especially securities funds and financial advisors.