2521 results about "Current user" patented technology

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a dynamic question and answer matching method and system based on context awareness. The method comprises the following steps: acquiring history of multiple rounds of dialogues and user background information, and generating a semantic understanding model of a current dialogue; based on the semantic understanding model and the current user input, matching the most relevant answer from the question and answer library, and dynamically adjusting the matching strategy; acquiring real-time feedback data of the user, updating a personalized recommendation algorithm based on the interest change of the user, and generating a personalized answer recommendation list; dynamically updating the content of the question and answer library according to the personalized recommendation list and the user behavior data; and inputting the user feedback and the updated question and answer library data into a personalized recommendation system to generate final personalized answer recommendation. Through context sensing and real-time dynamic adjustment, the matching precision of the question answering system is remarkably improved, the interaction experience of the user is improved, and the adaptability and flexibility of the system are enhanced.

The invention provides a plasticizing product recommendation method and system based on user demands, and relates to the technical field of plasticizing product recommendation, and the method comprises the steps: S1, constructing a plasticizing industry knowledge graph; s2, acquiring user historical data and current user requirements, including historical search data, historical browsing data, historical interaction data and historical transaction data; s3, preprocessing the historical data of the user, analyzing the preprocessed historical data of the user, and extracting historical demand features; s4, performing keyword extraction on the current user demand by utilizing the semantic understanding model, and generating an optimized query condition in combination with the chemical and plastic industry knowledge graph; s5, searching in a plasticizing product database by utilizing the optimized query condition to obtain a first list; and S6, carrying out personalized sorting on the first list by utilizing the chemical and plastic industry knowledge graph, generating a recommendation list from the first k lists, and presenting the recommendation list to the user. According to the invention, the relevance and accuracy of recommendation can be improved, so that the user experience is improved.

Techniques for generating a prompt for a language model to determine an action responsive to a user input, are described. In some embodiments, the system receives a user input, determines one or more application programming interfaces (APIs) configured to perform actions that are relevant to the user input and exemplars representing examples of using the APIs with respect to user inputs similar to the current user input. The system further determines device states of devices that are determined to be related to the user input and also determines other contextual information (e.g., weather information, time of day, geographic location, etc.). The system generates a prompt including the user input, the APIs, the exemplars, the device states, and the other contextual information. A language model processes the prompt to determine an action responsive to the user input and the system causes performance of the action.

The invention provides a user behavior data mining method and system applied to digital enterprise management, and the method comprises the steps: collecting the multi-dimensional behavior data of a target user in a business operation interface, carrying out the multi-modal data analysis of the multi-dimensional behavior data, generating a behavior track feature set with time sequence relevance, and carrying out the mining of the behavior track feature set; training an adaptive time sequence analysis model based on the behavior trajectory feature set, capturing a long and short term dependency relationship in a user behavior mode by the time sequence analysis model through a dynamic window division strategy, generating a potential loss risk prediction index, and constructing an interaction process parameter matrix according to the potential loss risk prediction index; and calling the optimized interaction process parameter matrix to drive a service operation interface to reconstruct, generating an interaction interface adaptive to the current user behavior mode, and iteratively updating the time sequence analysis model through an incremental feedback mechanism in a preset verification period. According to the invention, the comprehensiveness and accuracy of user behavior pattern mining can be improved.

The invention discloses a distributed trusted virtual human identity authentication system and method based on a block chain, and the system comprises a distributed digital identity system which is used for obtaining user entity identity information registration data, verifying the identity of a user entity, and carrying out the digital identity creation of a virtual human corresponding to a current user entity, generating a distributed digital identity of the target virtual person, and supporting query verification and authorization management of the digital identity of the virtual person; the virtual human digital identity registration system is used for registering a distributed digital identity of a target virtual human corresponding to the current user according to target digital identity registration information obtained when the virtual human digital identity is created; and the digital certificate authentication center is used for responding to the digital identity certificate request of the target virtual person and respectively sending a target digital identity certificate corresponding to the identity of the target virtual person to the distributed digital identity system and the virtual person digital identity registration system, so that the problems of virtual person identity determination and right confirmation are effectively solved.

The invention discloses a database management method and management system, and relates to the technical field of general database management, and the system comprises a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis prediction module, a multi-terminal integration module and a safety guarantee module. In the invention, a role-based access control permission model is constructed in a permission control layer and a module definition, and a field-level data control mechanism is implemented for three roles of a platform administrator, a customer enterprise and an employee, for example, the employee can only check a specific public field in a salary list, and sensitive fields such as a salary calculation formula are dynamically shielded; through a permission inheritance mechanism, the system automatically adds a filtering condition that employee ID = current user ID for a query request of an employee role, data isolation is ensured, a multi-dimensional data relation graph of employee-post-customer-contract is constructed, and a dynamic association path between entities is mapped in real time through a visual view.

Methods, systems, and computer programs are described for using a using an LLM-based conversation engine to treat a mental health disorder. The method includes obtaining data corresponding to a communication from a user device, determining a current user context based on the obtained data, determining based on the determined user context, whether to invoke the LLM, and based on a determination to invoke the LLM: determining a prompt that is to be provided to the LLM based on the determined user context, providing input data to the LLM, the input data comprising (i) the obtained data and (ii) the determined prompt, obtaining output data, generated by the LLM based on the LLM processing the provided input data, indicating a classification of the provided input data, and using the obtained output data to determine a therapeutic treatment for the user.

Various embodiments include systems and methods for generating a privacy-aware multi-modal autoreply to an incoming communication. A processing system of a computing device may collect multi-modal information, determine a current user circumstance based on the collected information, determine a user privacy preference for autoreply responses, and generate a prompt that is input to a generative large language model (LLM) to generate optional autoreply responses, receive a list of personalized response suggestions from the generative LLM, and perform an autoreply action based on a selected personalized response suggestion.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intention analysis and strategy generation method, device, equipment and medium, and the method comprises the steps: obtaining interaction data, recognizing an interaction type according to metadata features, selecting a corresponding analysis model to analyze and process data, and generating a text corpus; current user information is extracted from the text corpus, user intention score analysis is executed based on an intention analysis strategy corresponding to the interaction type, and a current user intention score is generated; and generating a demand list according to the user information, the intention score and the interaction type, and generating and outputting an interaction strategy based on the user information and the demand list. Through interaction data processing and automatic intention scoring analysis, the demand list and the interaction strategy of the customer can be quickly and accurately generated, the customer information processing efficiency and accuracy are improved, the workload of manual input and analysis is reduced, and the customer service quality and the response speed are improved.

The invention provides an intention recognition method, system and equipment, and the method comprises the steps: carrying out the vector representation of a current user query, carrying out the retrieval in a pre-constructed retrieval library through the vector representation of the user query, obtaining similar queries, and selecting at least one similar query and a corresponding intention label as a reference sample; a prompt text is spliced through an intention label description text, the user query and the reference sample, and the intention label description text defines intention labels in all scenes; and inputting the prompt text into a pre-trained large language model to obtain an intention recognition result. The method can improve the accuracy of large model intention recognition.

The invention discloses a virtual historical character dialogue method and system with role knowledge and context awareness, and relates to the technical field of man-machine interaction, and the method comprises the steps: constructing a multi-level role depth model; when a question of a current user is received, identifying information of a virtual scene where the current user is located, analyzing micro-expressions of the face of the user and voice rhythm characteristics of speech of the user, analyzing an emotional state and an interaction intention of the user based on a multi-modal fusion algorithm, and generating a user state vector; executing a dynamic Prompt construction program, extracting related information from the multi-level role depth model and the user state vector, and generating a structured Prompt; and inputting the structured Prompt into a large language model, generating a reply text conforming to role features based on questions of the current user, and driving a virtual character model. The method solves the problem that in the prior art, virtual historical figures cannot provide real immersion and credible interactive experience with emotional connection.

The invention discloses a user analysis method and device based on a large model, equipment and a medium, and the method comprises the steps: obtaining user data from a plurality of data sources, and carrying out the preprocessing of the user data, so as to form a structured data set; inputting the structured data set into a pre-trained large model based on deep learning, and outputting a current user portrait after performing feature extraction on the user data through the large model; according to the user portrait, analyzing the user by adopting clustering analysis, association rule mining or a classification algorithm, and outputting a user classification result; and according to the user group characteristics corresponding to the user classification result, in combination with the user portrait, performing user behavior prediction so as to push personalized recommendation content to the user. According to the method, the users are analyzed and classified through the user portraits, and the pushing requirements of personalized contents can be accurately and quickly met.

The invention discloses an AI-based intelligent marketing advertisement putting method and system, and the method comprises the steps: collecting and fusing heterogeneous user behavior data streams from a plurality of independent platforms in real time, and generating a user behavior feature map with a unified space-time mark; on the basis of the user behavior characteristic spectrum, a deep auto-encoder and a space-time diagram convolutional network are used for joint modeling, and fine-grained scene-decoupled user interest preference vectors are generated; inputting the user interest preference vector into a pre-trained generative adversarial network, and dynamically generating a personalized advertisement material highly matched with the current user interest preference and scene; and taking the personalized advertisement material as a candidate arm, and dynamically generating an advertisement putting strategy including an advertisement display form, a display opportunity and a display channel in combination with a current user state and historical putting feedback data. By utilizing the embodiment of the invention, the accuracy and the real-time performance of advertisement putting can be improved, and the advertisement conversion rate and the user experience are improved.

TrustGPT secures a device by ensuring that only an authorized user is able to use the device. TrustGPT utilizes information received from one or more sensors of the device and generative artificial intelligence to determine that the current user is the authorized user. Without TrustGPT, user devices are susceptible to being stolen or hacked and used for nefarious purposes.

The invention relates to the technical field of Web front-end development, and discloses a front-end intelligent menu caching and dynamic authority control method. The method comprises the following steps: firstly, checking whether menu cache data in a local storage of a browser exists and is valid, if so, directly using the cache data, otherwise, obtaining latest menu data from a server and caching according to a preset strategy; the menu data change state of the server side is monitored in a timed polling or WebSocket mode, and the local cache is updated in time when the data change; acquiring role permission information of the current user, matching the role permission information with the menu data, and dynamically generating an accessible menu tree; when routing jump is carried out, the type of a target address is intelligently judged, permission verification is carried out on an internal routing address, and it is ensured that a user can only access pages within the authorization range of the user. According to the application, the server request frequency is reduced, the response speed of the front-end application is improved, and meanwhile, the real-time performance of the menu data and the access security are ensured.

Techniques for determining relevant and personalized supplemental content for a user are described. A system may prompt a language model to generate queries based on an entity (or entities) representing a user's affinity. In some examples, the language model may be prompted to generate an initial query corresponding to the entity and then generate one or more enriched queries based on the initial query. The one or more enriched queries may be used to search one or more content providers for supplemental content results. The supplemental content results may be aggregated, may be validated to ensure correspondence to the enriched query, and may be stored for future output. A system component may request supplemental content for output, at which time the system may select one or more supplemental content from storage, where the selection may be made to optimize relevancy for current user and situational context.

The invention discloses a voice control implementation method and system based on a large model, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of insufficient recognition precision, low response speed and limited complex context processing capability in voice control. Comprising the following steps: analyzing a user voice instruction into a text instruction through a voice recognition model; performing semantic feature extraction on an input text instruction through a pre-trained text embedding model, and outputting a text embedding vector as a to-be-recognized text embedding vector; searching whether a text embedding vector similar to the to-be-identified text embedding vector exists in a pre-constructed vector database, if not, executing text analysis, and if yes, returning corresponding control information from the vector database; and taking the text instruction and the question statement as input, performing semantic analysis through a pre-trained large language model to obtain control information, and storing a text embedding vector corresponding to the current user voice instruction and the control information into a vector database.

The invention provides a context compression method based on multi-round dialogue intention graph construction, which comprises the following steps: S1, dialogue data acquisition and preprocessing: carrying out natural language processing on each dialogue unit; s2, intention atlas construction is achieved through node design and edge design, and each node comprises original text content and structured semantic information; s3, carrying out context compression and graph structure cutting, and only retaining sub-graphs forming a core semantic link; s4, dynamic context management and topic jump processing: in a multi-topic dialogue, when a user jumps or switches to a new topic, a system records a sub-graph of a current active topic, and contextual nodes of an inactive topic are frozen; s5, context sequence generation and model input: linearizing node contents in the cut sub-graph according to a dependent link sequence to generate a compressed context sequence, and transmitting the context sequence and current user input to a large language model for reasoning; and S6, continuous updating and feedback optimization are carried out.

The invention discloses a network security defense system based on collaborative intrusion detection, which relates to the technical field of network security and comprises a behavior data acquisition module, an anomaly analysis module, a behavior evaluation module, a joint risk evaluation module and a security defense module. The behavior data acquisition module is used for acquiring related access behavior information of a user; the anomaly analysis module is used for analyzing the user login time and the deviation degree of the fingerprint of the current login equipment; the behavior evaluation module is used for analyzing the deviation degree of the user access behavior to judge whether the current user access behavior is abnormal or not, and sending an access risk analysis instruction; the joint risk assessment module is used for analyzing the risk degree of the abnormal internal access behavior of the current user; and the security defense module is used for judging whether the abnormal internal access behavior of the current user has a risk or not so as to obtain and execute a security defense instruction of a corresponding level.

The embodiment of the invention provides an interface interaction method and device, equipment and a storage medium. The method comprises the following steps: providing a widget associated with a target user, wherein the widget at least presents a virtual object of joint interaction between a current user and the target user; and in response to the received first operation associated with the widget, presenting a session interface of a target session, the target session being at least associated with the current user and the target user. In this way, according to the embodiment of the invention, the virtual object of joint interaction between the current user and other users can be presented by utilizing widget, so that the interaction efficiency between the users is improved.

The invention provides a user dialogue generation method and system based on memory fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a user memory knowledge base based on a preset knowledge graph tool; based on the user memory knowledge base, reading current text data input by the current dialogue user and historical text data of the current dialogue user; obtaining short-term memory data, long-term memory data and scene memory data based on the user memory knowledge base, a preset large language model, a preset cue word engineering algorithm, the current text data and the historical text data; and generating a user dialogue based on the preset large language model, the preset cue word engineering algorithm, the short-term memory data, the long-term memory data and the scene memory data. According to the invention, the memory range of the user from recent interaction to long-term important information is fully covered, and the reliability of responding to the user is improved.

The invention discloses a resource mining method based on big data, and the method comprises the steps: receiving a retrieval picture keyword of a current user, updating the historical behavior data and picture resource data of the user in real time, carrying out the clustering of the retrieval keyword of the user, and carrying out the classification of the retrieval keyword. The method comprises the following steps: acquiring a retrieval keyword, adding a corresponding retrieval spatio-temporal index for the retrieval keyword, capturing the dynamic change of the retrieval intention of a user, analyzing the feature preference of the user and the current retrieval intention of the user based on historical behavior data and the retrieval picture keyword, and retrieving a related picture from a picture resource library. The method comprises the following steps: obtaining a picture, carrying out feature extraction and classification on the picture, carrying out intention scoring analysis, carrying out descending order arrangement display on screened picture resources based on an intention scoring result, feeding back a retrieval result based on a user, and optimizing a retrieval model. According to the method, the problems of low user intention matching accuracy and picture resource waste are solved.

The invention relates to the technical field of augmented reality application, and discloses an augmented reality system and method for immersive experience. The method comprises the following steps: collecting dynamic spatial data of a physical environment in real time, and synchronously obtaining an interactive behavior data stream of a user terminal; performing environment semantic modeling on the dynamic spatial data to generate multi-level feature description of a physical environment, and determining a rendering priority sequence of virtual-real superposition content according to the multi-level feature description; extracting behavior pattern characteristics in historical interaction data of the user, performing immersion correlation analysis on the behavior pattern characteristics to obtain environment cognitive preference parameters of the current user, and generating a virtual-real interaction event sequence in combination with the environment cognitive preference parameters and the interaction behavior data flow; and dynamically compensating the virtual-real synchronization error in the immersive experience process according to the rendering priority sequence and the virtual-real interaction event sequence. According to the method, the accuracy and naturalness of virtual-real fusion can be improved, the personalized requirements of the user are met, and the overall immersive experience effect is enhanced.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving a search query from a user during a current user session; generating a query embedding for the search query; determining one or more entity embeddings with similarity scores that satisfy a threshold, the one or more entity embeddings including keywords associated with entities; filtering the one or more entity embeddings based on filtering criteria; generating a mapping for the search query based on the one or more entity embeddings, the mapping including an association between keywords in the search query and an entity; and modifying a graphical user interface to display items associated with the entity in an upper portion of the graphical user interface during the current user session. Other embodiments are disclosed herein.

The invention discloses an intelligent dialogue implementation method, device and equipment for generating SQL based on DSL, and the method comprises the steps: responding to an intelligent dialogue instruction of a user terminal, obtaining current user dialogue data corresponding to the intelligent dialogue instruction, and carrying out the intention recognition to obtain a current intention recognition result; correspondingly generating a current DSL script based on the current intention recognition result and a specific domain language script generation strategy; correspondingly generating a current SQL script based on the current DSL script and the script conversion strategy; and obtaining a current query result in a local database based on the current SQL script, generating a current display result corresponding to the current query result, and sending the current display result to the user terminal. According to the embodiment of the invention, the dialogue data of the current user can be firstly subjected to intention recognition and converted into the DSL script and then converted into the SQL script, so that the SQL script obtains the corresponding data from the database, and the support capability of complex query and the accuracy rate of data query are improved.

The embodiment of the invention relates to a live broadcast interaction method and device, equipment and a storage medium. The method provided by the invention comprises the following steps: presenting a preview interface, wherein the preview interface presents live broadcast preview content of a first live broadcast party; presenting a target element in the preview interface, wherein the target element indicates a live broadcast state of a second live broadcast party associated with the current user; and in response to a preset operation for the target element, presenting an access entry associated with the second live broadcast party. In this way, the live broadcast watching efficiency of the user can be improved.

The invention relates to the technical field of artificial intelligence, in particular to a method and device for selecting a knowledge base and a storage medium. Comprising the steps of obtaining a current query intention and a current user portrait of a user; obtaining a positive sample data set in the sample data set; selecting a positive sample data set of the large model for the knowledge base to generate a task scene cue word; constructing a knowledge base selection large model based on the task scene cues; inputting the current query intention and the current user portrait into a knowledge base selection large model to obtain a knowledge base selection score of each currently available knowledge base; according to the preset selection rule and the knowledge base selection score of each currently available knowledge base, the target knowledge base aiming at the current query intention of the user is selected from each currently available knowledge base, the personalized demand of the user is considered, the target knowledge base required by the user can be accurately determined, and the user experience is improved. And the efficiency and accuracy of subsequently answering user questions and answers are improved.

The present application relates to the field of artificial intelligence. Disclosed are a large model inference method and apparatus, and a device and a storage medium. The method comprises: performing permission verification on login information; acquiring an inference request, symmetrically encrypting data to be inferred that is in the inference request, and encrypting a symmetric encryption key and then storing same in a remote authentication service; performing permission verification on the current user by means of an online inference service system, and issuing the inference request to an online inference engine, wherein the online inference engine and the remote authentication service run in a TDX trusted execution environment; and performing legitimacy verification on the inference request by means of the online inference engine, applying for the symmetric encryption key from the remote authentication service, using said data to perform inference, and encrypting an obtained initial inference result. The protection of a model itself is realized on the basis of a TDX technique, such that the model runs in a trusted execution environment, thereby greatly reducing risks such as model stealing or attack, and realizing protection of user-side data.

The invention relates to the technical field of voice semantics, and discloses an information interaction method and device based on multi-agent collaboration, equipment and a medium, and the method comprises the steps: generating a target workflow through a large language model and a current user demand; determining a target AP I according to the target workflow, and generating initial interaction information; and generating current reply evaluation information based on the initial interaction information and the criticizing agent, and determining the initial interaction information as target interaction information under the condition that the current matching degree is greater than or equal to a matching degree threshold value. Through the above mode, the user demand is split through the routing agent and the built-in large language model, the target workflow is generated, and the method adapts to complex and changeable task scenes. According to the method, the target AP I is determined through the induction agent, and the initial interaction information is generated, so that the efficiency and accuracy of calling the AP I are improved, and the performance of an automatic business process system for processing complex interaction information in the fields of financial science and technology, medical treatment, health, old-age care and the like is improved.

The invention discloses a multi-modal news recommendation method and system in combination with clock interests, and relates to the technical field of data mining and recommendation methods.The method comprises the steps that a user behavior sequence, a candidate news set and a related timestamp set are extracted; obtaining the multi-modal coding representation of the historical interaction news through the time perception multi-modal feature coding; performing clock interest modeling based on multi-modal coding representation of historical interactive news, and fusing long and short-term interests through Gaussian weighted aggregation and long-term interest enhancement operation to obtain a user interest vector; and calculating the matching degree of the candidate news and the current user interest vector, applying time period sensitive suppression through time sequence gating, performing click probability prediction on the output of the time sequence gating by adopting a linear network, and performing a dynamic recommendation decision. According to the method, by means of the feature fusion technology of time context perception, the interest continuity of the hour granularity is captured, and the recommended content is more accurately matched with the requirements of the user in different time periods.