243 results about "Response method" patented technology

The invention provides an AI intelligent customer service response method and system based on a remote digital service, and the method comprises the steps: firstly receiving a session request data flow inputted by a user through a remote session terminal, and extracting a dynamic semantic feature sequence containing a grammatical structure, an emotional tendency and a context dependence feature; inputting the data into an intention recognition model trained based on a historical session data multi-dimensional semantic relationship, and outputting an intention category label and confidence; matching a target dialogue stream node from a preset dialogue process library according to the label and the confidence coefficient; dynamic response content is generated based on the node response logic rule, and a response data stream is returned; and finally, collecting user feedback behavior data, updating the association weight of the intention recognition model according to an interaction validity index, optimizing the semantic mapping precision of the intention recognition model to similar session requests, and realizing more accurate, intelligent and adaptive customer service response.

The invention relates to the field of network security, and provides a security event tracing and response method and system based on an LLM intelligent agent, and the method comprises the steps: collecting log information from network equipment, and extracting a security event with a high threat degree in the log information; identifying an attack mode of the security event based on LLM, and generating a response strategy including atomization operation steps according to the identified attack mode; the natural language instruction in the response strategy is converted into a tool calling instruction through the intelligent agent, and related tools are called to execute specific operation. By constructing an LLM-driven intelligent decision-making body, full-process automation from event analysis to response execution is achieved, the LLM deep reasoning ability and a safety tool chain are innovatively and deeply fused, an unstructured log is converted into an executable disposal scheme through the semantic understanding ability of the LLM deep reasoning ability, and the processing efficiency is improved. And designing a multi-stage reasoning framework of attack mode analysis-target decomposition-tool matching, and supporting dynamic generation of a customized response scheme conforming to an enterprise strategy.

Disclosed in the present application are a task-type dialogue response method and apparatus. The method comprises: acquiring basic dialogue information of a task-type dialogue, wherein the basic dialogue information at least comprises: intent information of the current round of request, and historical dialogue information; using a dialogue state large language model to analyze the basic dialogue information, so as to obtain a target dialogue policy request based on a domain-specific language; using a dialogue policy loader to load a target dialogue policy sample corresponding to the target dialogue policy request; using a dialogue policy large language model to analyze the target dialogue policy sample, so as to obtain a target dialogue generation policy; and using an engineered-dialogue generation module to execute the target dialogue generation policy, so as to obtain request response information, and outputting the request response information. The present application solves the technical problems in the related art of the capability of processing a multi-round task-type dialogue by means of a dialogue system being relatively poor and the user experience being poor.

Methods, computer systems, and computer-storage media are provided for generating and using context briefs to identify relevant chat responses. In embodiments, a context template associated with an intent is obtained. The context template includes a data reference referencing dynamic data and content providing context for the dynamic data. Thereafter, a context brief associated with the intent is generated by obtaining the dynamic data and incorporating the dynamic data with the content. Upon obtaining an input data request indicating a user intent, the context brief is identified as corresponding with the user intent of the input data request based on the intent associated with the context brief. A prompt to be input into a large language model is generated. The prompt includes the input data request and the context brief. A response relevant to the input data request is obtained as output from the large language model.

The invention relates to the technical field of distributed response, in particular to a distributed message dynamic response method based on websocket. The method comprises the following steps: constructing a distributed message service cluster to configure a websocket communication module, message middleware and a client for each service node, and providing a unified service access point to the outside through an Nginx load balancer, so as to automatically register and subscribe a global control theme to the message middleware and form a hierarchical theme subscription structure; when the client initiates a connection request, the connection is distributed and a secure channel is established; the client sends a service request message, packages the service request message into a standardized message format, and associates session information of the client at the same time; and receiving the session message, performing intelligent filtering, generating a response result, and pushing the response result to the client in real time. According to the invention, efficient processing and real-time response of messages in a distributed environment can be realized.

The embodiment of the invention relates to the field of large language models and multi-agents, and provides a multi-agent collaborative query response method based on a state dependency graph and a related device, and the method comprises the following steps: receiving a to-be-queried text input by a user; performing query semantic analysis on the to-be-queried text to obtain the analyzed to-be-queried text and query complexity; instructing the intention recognition agent to generate a domain index; calling a preference matching agent to generate preference information; calling a resource retrieval agent to query to obtain query use resource information corresponding to the to-be-queried text according to the query complexity; the query response information is generated at least according to the historical context of the to-be-queried text, the to-be-queried text, the analyzed to-be-queried text, the field structure information, the field index, the preference information and the query use resource information, and the personalized service corresponding to the to-be-queried text currently input by the user can be automatically adapted according to the actual interaction scene; and response type and high-efficiency intelligent assistance is realized.

The embodiment of the invention provides a reply method and device based on a large language model, electronic equipment and a storage medium, relates to the technical field of intelligent questions and answers, and is suitable for the fields of financial science and technology, digital medical treatment and the like. The method comprises the steps of obtaining candidate question and answer texts of a target knowledge base based on a preset knowledge category, and obtaining question extraction indication parameters; performing question extraction on the candidate question and answer text according to the question extraction indication parameter to obtain candidate question information; obtaining target problem information; performing problem analysis on the target problem information according to a preset problem analysis indication parameter to obtain problem analysis information; performing question splitting on the target question information according to the question analysis information to obtain reference sub-question information; and performing answer extraction on the candidate question and answer text according to the reference sub-question information and the candidate question information to obtain target answer information. According to the embodiment of the invention, the reply accuracy can be improved.

The invention relates to the technical field of information interaction, in particular to a retail customer service response method and a retail customer service system. The response method comprises the following steps: based on retail data, constructing a retail large language model by using a Transform model architecture, and carrying out training and fine tuning, so that the retail large language model has a question and answer ability in the retail field; performing feature extraction based on the multi-modal data input by the user to generate question and answer text data; based on the question and answer text data, topK information with most matched semantics is retrieved from a preset vector knowledge base; and based on the text data and the topK information, calling the retail large language model to respond. According to the response method of the retail customer service, questions of the user can be deeply understood, intelligent response is carried out, and the shopping experience of the user is improved.

The invention relates to the technical field of semantic recognition, can be applied to business scenes such as financial science and technology and medical health, and discloses a business response method, device and equipment based on communication theme recognition and a medium. And calculating the distribution density of the words in the plurality of text units, generating word weights in combination with word frequencies and the distribution density, identifying concerned topics, generating user feature tags or content classification identifiers, and associating with a service information system to trigger service operation. According to the method, the communication record is converted into the text data, the word frequency and the word distribution density are calculated, and the word weight with the discrimination degree is generated, so that a user concerned theme is automatically identified, and a label or a classification identifier is further constructed; automation of user behavior understanding, standardization of label generation and linkage of an operation process and a business system are realized, and personalized service response capability and operation efficiency are improved.

The invention relates to an intelligent customer service response method and device, equipment and a medium, and the method comprises the steps: responding to a user question event, and obtaining a latest short-time chat record, the user question event comprising a latest question statement; according to the short-time chat records, determining historical behavior data and long-time chat records matched with the semantics of the user; combining the short-time chat record, the long-time chat record and the historical behavior data into question reference data, and determining an intention commodity and intention intensity thereof according to the question reference data; and the question reference data, the intention commodity and the intention intensity of the intention commodity are embedded into a response prompt text, response information is generated according to response service logic in the response prompt text, and the response information comprises own commodities determined from the online shop according to the intention commodity. According to the method, the accuracy and the individuation level of the response information are improved, the flexibility and the effect of commodity recommendation are enhanced, and remarkable commercial value and user satisfaction improvement are brought to online shops of an e-commerce platform.

The invention provides a response method, device and equipment for a customer service robot, and relates to the technical field of customer service robots, and the method comprises the steps: obtaining multi-mode request data inputted into a current session with the customer service robot by a user, and a conversation state of the current session; according to the request data of different modals and the modal features of the request data of the corresponding modals, constructing a multi-modal heterogeneous graph; fusing the different modal features based on the request data of the different modalities, the corresponding modal features, the dialogue states and the multi-modal heterogeneous graph to obtain multi-modal fusion features; inputting the multi-modal fusion feature into a pre-constructed user intention response model based on a causal relationship to obtain a response strategy of the customer service robot to the multi-modal request data; wherein the user intention response model is used for representing the causal relationship between the multi-modal interaction data input by the user and the response strategy of the customer service robot. The response accuracy of the customer service robot can be improved, and the user interaction experience is improved.

The invention discloses a collaborative response method and device for large and small models, equipment, a medium and a program. The method comprises the steps of calling a low-rank adaptation small model to generate an initial response through an edge node when an input instruction of a user is received; calculating the whole sentence confidence coefficient in the initial response; if the whole sentence confidence coefficient is smaller than a sentence-level confidence coefficient threshold value, a collaborative response instruction is sent to the cloud server cluster; selecting a specified number of target large models from the plurality of large models, and correcting at least one lexical element based on a lexical element level confidence coefficient threshold to obtain a plurality of collaborative responses; and selecting the target collaborative response with the highest confidence as a real response, and returning the real response to the user. According to the embodiment of the invention, the large model and the small model are utilized to cooperatively work to quickly give a preliminary reply to a simple question, the large model intervenes in optimization when a complex question is encountered, a satisfactory answer is provided for a user, the user experience is improved, and the instruction processing capability is enhanced by performing fine tuning optimization on the low-rank adaptive small model.

The invention discloses a multi-modal text travel knowledge base response method and system based on a large language model. The method comprises the following steps: acquiring a text and an image input by a user; performing retrieval through an image similarity retrieval technology and a text knowledge base; and returning tourism information related to user input. According to the system architecture, a large language model, image similarity retrieval and text knowledge base construction are integrated, multi-modal input can be efficiently processed, and personalized and accurate tourism information services are achieved. The system further comprises a set of Web user interface which supports the user to interact through multi-mode query of images and texts. According to the method, the response speed and accuracy of the system can be effectively improved, and the expansibility of supporting voice interaction, multi-modal data processing and knowledge base content automatic updating is achieved. The method and the system are suitable for scenes needing to provide personalized tourism information services, and have relatively high practicability and innovativeness.

The invention discloses an intelligent reply method for an electric power marketing inspection work order. The method comprises the following steps: S1, obtaining work order data of a current inspection work order and a multi-dimensional capability index of a current business person to construct a state vector; s2, inputting the state vector into a pre-trained hierarchical reinforcement learning model, and outputting a decision instruction containing a high-level strategy instruction and a bottom-layer reply action; s3, determining a target interaction mode from a plurality of preset interaction modes based on the high-level strategy instruction; s4, displaying a corresponding reply auxiliary information set on a user interface based on different target interaction modes, and receiving actual operation of a user; the auxiliary information set is generated based on bottom-layer reply actions; and S5, calculating a reward value based on an actual operation result, and updating the hierarchical reinforcement learning model by using the reward value. According to the invention, capability states and work order characteristics of business personnel can be dynamically sensed, and real personalized intelligent assistance is provided.

The invention relates to a control instruction response method and device, a robot and a storage medium. The method comprises the steps of collecting an audio signal when a preset triggering condition is met; performing semantic recognition processing on the audio signal to obtain corresponding text information; the text information is judged through a pre-established large language model agent, a response mode is determined, and the response mode comprises action execution and / or question answering; rehearsing the response mode based on the constructed robot model, and evaluating a rehearsing result of the response mode through a large language model agent to obtain a corresponding evaluation result; and when the evaluation result meets the requirement, controlling the robot to broadcast a question answer generated by the robot model during rehearsal, and / or execute an action sequence generated by the robot model during rehearsal. Therefore, the response mode can be adaptively determined according to the text content corresponding to the audio signal, so that the response mode is more diversified, the response feedback is more intelligent, and the interaction experience is improved.

The embodiment of the invention discloses an intelligent response method and system based on a large model and a storage medium. The system comprises a user input module which is used for acquiring input information of a user; the keyword extraction module is used for extracting key information corresponding to the input information through a large model; the historical record conjoint analysis module is used for determining a plurality of dimension features of the user according to the key information and the historical record information; the permission judgment module is used for determining the knowledge base with the access permission of the user according to the permission level; the comprehensive portrait establishing module is used for generating a dynamic user portrait according to the multiple dimension features of the user; and the knowledge recommendation module is used for outputting a personalized reply of the user according to the dynamic user portrait and the knowledge base with the access authority of the user. According to the embodiment of the invention, through multi-dimensional analysis of user behaviors and historical data, an accurate user portrait is constructed, user demands are accurately matched, personalized knowledge recommendation is provided, and the knowledge service accuracy is remarkably improved.

The invention relates to the technical field of collaborative response management, and provides a business system collaborative response method and platform driven by a video AI analysis result, and the method comprises the steps: receiving a frame sequence generated by aligning a multi-source heterogeneous video stream, carrying out the parallelization AI reasoning to extract multi-dimensional features into an event feature set, carrying out the association mapping with a business strategy library to generate a collaborative instruction packet, and carrying out the collaborative response of the business system. The information is distributed to an unmanned aerial vehicle cluster through a distributed message bus and the like according to a route, and a terminal linkage response is triggered. The technical problem that communication among unmanned aerial vehicle clusters is unstable due to the fact that a communication network is prone to being interfered or interrupted in complex environments such as a disaster site is solved, events in a video are analyzed and processed, the cooperative instruction packet is generated and distributed to the target unmanned aerial vehicle cluster, the unmanned aerial vehicle clusters can efficiently execute tasks, and the efficiency of the unmanned aerial vehicle clusters is improved. And a self-organizing network protocol is deployed to carry out distributed collaborative decision making, so that the communication stability of the unmanned aerial vehicle cluster is improved.

The invention relates to the technical field of artificial intelligence and legal information processing, in particular to a legal consultation intelligent response method based on deep learning. The method comprises the steps that data preprocessing is conducted on legal consultation questions input by a user, and a semantic nested matrix is generated; dynamically adjusting a nesting weight, and constructing a generation matrix in combination with the semantic nesting matrix; comparing the generated matrix with a legal knowledge base matrix layer by layer to confirm the matching degree of the generated solution and the elements of the legal knowledge base matrix; taking a result with the highest matching degree as a preliminarily generated solution, and generating a feedback matrix; and optimizing and updating the generated matrix based on the feedback matrix to obtain a generated solution matrix. The problems that an existing intelligent legal consultation system is insufficient in legal semantic understanding, limited in semantic weight adjustment, poor in logicality of generated content, low in legal clause matching degree, lack of a self-adaptive optimization mechanism fed back by a user and incomplete in expression ability of natural language output are solved.

The invention discloses a large language model system and a request response method thereof, and relates to the technical field of large language models. According to the specific implementation mode of the method, a large language model system comprises a pre-filling instance and a decoding instance, the pre-filling instance and the decoding instance are deployed on first hardware and second hardware respectively, the first hardware comprises at least one first processor, and the second hardware comprises at least one second processor; the computing power of the first processors and the total number of the first processors are determined according to the load characteristics of the pre-filled instances, the computing power of the second processors and the total number of the second processors are determined according to the load characteristics of the decoded instances, and the total computing power of the first processors is higher than the total computing power of the second processors. According to the implementation mode, flexible configuration of hardware resources and full utilization of the hardware resources are realized, and the hardware cost is reduced.

The embodiment of the invention provides a message response method and device, computing equipment, a storage medium and a program product. Wherein the target server side provides account authorization prompt information for the target client side, responds to a first session authorization request of a first media account sent by the target client side for the account authorization prompt information, and sends a second session authorization request to the multimedia server side, and the multimedia server authorizes the first media account and feeds back the session access token. Based on the session access token, obtaining a first session message sent by a second client of the second media account to a first client of the first media account, generating a first response message by adopting a target artificial intelligence model corresponding to the first media account in combination with domain knowledge, and sending the first response message to the first client of the first media account through the multimedia server, and sending the first response message as a response message of the first client to the second client. According to the technical scheme provided by the embodiment of the invention, the session message in the multimedia application is automatically answered with low cost and high efficiency.

The invention discloses an inference type dialogue response method and system based on a large model, and a storage medium, relates to the technical field of service consultation dialogue response, and solves the problems that the existing service consultation dialogue response is generally relatively simple, lacks an active inquiry strategy, only can be passively responded and cannot simulate a questioning mechanism of service personnel, and the service consultation dialogue response cannot be simulated. The technical problems of insufficient suggestion judgment accuracy and high misjudgment rate caused by difficulty in comprehensively and accurately collecting the conditions of the demanders in the prior art are solved. The method comprises the steps of obtaining voice data of a demander for service consultation; the method comprises the steps of performing feature extraction on voice input data to obtain condition feature data; inquiring the demander through a pre-trained condition inquiry model based on the condition characteristic data to obtain judgment data; matching from a historical consultation database based on the condition feature data and the judgment data to obtain a reasoning target; and feeding back a judgment suggestion to the demander based on the reasoning target, thereby solving the technical problem.

To respond to a user with rich content.SOLUTION: An automatic response apparatus is configured to cooperate with a service providing apparatus that provides a service via a network and a terminal device of a user, to automatically respond to a message with a natural sentence from the user, the automatic response apparatus including: a request parameter acquisition unit which acquires a request parameter to be input to a response model by inputting at least the message to an AI chatbot trained so as to make a response on receipt of a natural sentence; a response content acquisition unit which acquires content of a response to the user by inputting the acquired request parameter and personal information of the user related to the service to the response model; and a reply unit which transmits the acquired content of the response to the user to the terminal device.SELECTED DRAWING: Figure 7

The invention relates to the technical field of data processing, and discloses a response method and device based on a large model, electronic equipment and a storage medium, and the method comprises the steps: obtaining an original cue word which is determined based on a question text; obtaining intention information and / or target preference information, and modifying the original cue word based on the intention information and / or the target preference information to obtain a target cue word; and inputting the target cue word into a pre-trained first large model to obtain a response result, the target cue word being used for guiding the first large model to output the response result. On the basis, intentions or preferences of different users for the question can be displayed by obtaining the intention information and the target preference information. And modifying the original cue word to obtain a target cue word, and inputting the target cue word into the first large model to obtain a response result, so as to generate a response result more conforming to user differences.

The invention discloses a model training method, a response method and a related product, which are used for accurately and comprehensively analyzing session semantics so as to accurately identify a session intention. The model training method comprises the following steps: identifying a first intention and first entity information of a first session text; fusing the first intention and the first entity information to obtain a second intention of the first session text, and training a model based on the first session text and the second intention to obtain a first model; fusing the first intention, the first entity information and the second intention to obtain a third intention of the first session text, and training the model based on the first session text and the third intention to obtain a second model; and comparing the recognition accuracy of the first model and the recognition accuracy of the second model on the second session text, and determining a third model from the first model and the second model based on a comparison result.

The invention relates to the technical field of language processing and intelligent customer service systems, in particular to a robot response method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current to-be-responded question, carrying out the recognition through a pre-constructed large model, outputting a corresponding numerical vector, and storing the numerical vector in a database; based on a pre-constructed user-customer service robot vector database, retrieving all question and answer pair vectors corresponding to the numerical vector to obtain a question and answer pair data set of the numerical vector, calculating the similarity between all question and answer pair vectors in the question and answer pair data set and the numerical vector, determining a target question and answer pair vector meeting a preset similarity condition, and sending the target question and answer pair vector to the customer service robot; and answering the current to-be-answered question by using the generated target answer. Therefore, the problems that the customer service robot cannot provide accurate answers based on the questions of the user, and consequently the questions of the user cannot be effectively solved are solved, and through model training and question and answer pair retrieval and generation, the retrieval efficiency and the generation flexibility are improved, so that the answers of the questions of the user are more accurate.

The invention discloses a network public opinion event multi-department coordination response method and system, and relates to the technical field of multi-department response, and the method comprises the following steps: S1, an enterprise establishes a public opinion group in which a plurality of departments participate, and the public opinion group carries out the unified commanding and scheduling, and guarantees that each department can rapidly respond and cooperate with each other when a public opinion event occurs; and S2, the public gateway department assists in monitoring the dynamic state of the public opinion by defining the monitoring target and range, utilizing a whole-network public opinion monitoring tool, utilizing a social network public opinion monitoring tool, judging emotional tendency, searching engines, user feedback, public opinion analysis and information visualization. In the network public opinion management, through coordination response work of multiple departments, the departments closely cooperate and jointly face challenges according to clear responsibility division and efficient communication processes, so that the cooperation mode not only accelerates information transmission and processing, but also effectively avoids information delay and misunderstanding; and the timeliness and accuracy of the public opinion information are greatly improved.

The invention discloses a training method of a reply key point determination model, a verbal skill reply method and a related product, and the method comprises the steps: obtaining instruction information used for model training, and the instruction information is used for instructing a to-be-trained model to output historical reply key points corresponding to historical dialogue data based on the historical dialogue data, the historical reply key points are to-be-replied keyword information analyzed from historical dialogue data; constructing a training data set, wherein the training data set comprises historical dialogue data and historical reply key points; the to-be-trained model is trained based on the instruction information and the training data set, a reply key point determination model is obtained after training is finished, and the reply key point determination model is used for outputting reply key points corresponding to the to-be-processed dialogue data based on the to-be-processed dialogue data. Therefore, the customer service reply efficiency and response speed can be remarkably improved, and the customer satisfaction degree is further improved.

The invention relates to an A / B test problem response method and device based on a large model agent, and relates to the technical field of large models, agents and computers. The method comprises the steps of displaying an intelligent interaction page associated with an intelligent agent in response to a triggering operation on an intelligent agent identifier in an A / B test platform; in response to a work order creation operation in the intelligent interaction page, intercepting a target work order request corresponding to the work order creation operation sent to a work order server, and judging whether an intelligent agent can process a target problem in a target work order corresponding to the work order creation operation; acquiring a first similar question and a target answer corresponding to the first similar question in a question and answer knowledge base through a large model under the condition that the intelligent agent can process the target question; and displaying the target answer on the intelligent interaction page. Therefore, work orders submitted by the user can be reduced, waste of platform resources is reduced, the problem response speed and efficiency can be improved, and the use experience of the user is improved.

The embodiment of the invention provides an intelligent reply method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining a target question initiated by a target object, and obtaining user behavior data, user portrait data and current dialogue data of the target object; performing emergency degree evaluation on the target question according to the current dialogue data to obtain a reply emergency degree; performing priority evaluation on the target problem according to the user portrait data and the user behavior data to obtain a problem priority; determining reply demand prediction information according to the question category, the reply emergency degree, the question priority and the current dialogue data; screening out a target customer service system from preset candidate customer service systems according to the question complexity of the target question and the reply demand prediction information; and replying the target question through the target customer service system. The method can be applied to business systems needing a large amount of data, such as financial science and technology and health medical treatment, and can improve reply intelligence, accuracy and user experience.