357 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 a user question information response method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring question information input by a user; based on semantic retrieval and vector retrieval, retrieving corresponding private domain knowledge information from a preset private domain knowledge base; obtaining a first cue word according to the question information, the private domain knowledge information and a pre-configured agent database; inputting the first prompt word into the large model, obtaining an agent calling link through the large model, and calling a corresponding agent according to the agent calling link; obtaining a second prompt word, inputting the second prompt word and an execution result obtained by calling the corresponding agent according to the agent calling link into the large model, and determining whether the execution result meets a condition or not through the large model; if yes, response information for the problem information is output based on the execution result. By adopting the method, resource loss caused by fixed code development can be reduced.

Provided are a session response method and apparatus, an electronic device and a storage medium. The method comprises: acquiring multi-modal session information arranged according to a session time sequence, wherein the multi-modal session information comprises first modal information and second modal information (S110); performing multi-modal semantic representation on at least two modal features in the first modal information to obtain a first representation vector (S120); performing semantic representation of a corresponding modal on the second modal information to obtain a second representation vector (S130); respectively inputting the first representation vector, the second representation vector and a corresponding session time sequence into a session matching model to obtain a matching result (S140); and responding to the multi-modal session information according to the matching result (S150).

The invention relates to the field of artificial intelligence, and discloses a user question answering method and device, equipment and a medium, and the method comprises the steps: obtaining user question information, and sleeving the user question information into a preset intention reasoning prompt template to obtain an intention reasoning prompt text; an intention reasoning model is called, response strategy information is generated based on the intention reasoning prompt text, and the response strategy information comprises a fusion strategy prompt template, a target intention and a corresponding model input prompt text; calling a domain professional model corresponding to each target intention, enabling each domain professional model to input a prompt text according to the model corresponding to the target intention, generating an intention task feedback text, and sleeving the intention task feedback text into a fusion strategy prompt template to obtain a fusion strategy prompt text; and calling a response fusion model, and generating a response text based on the fusion strategy prompt text. According to the method, more intelligent, natural and similar dialogue services can be provided for the user, and an important progress of a dialogue system technology in the field of artificial intelligence is represented.

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 discloses an intelligent customer service response method and system based on a large model, and the method comprises the steps: obtaining user input and a historical dialogue of a user, splicing the user input and the historical dialogue with a preset first prompt, inputting a large model, and outputting query rewriting and intention recognition; responding to the intention recognition, splicing the intention recognition with a preset second prompt, and inputting the large model to output a calling instruction; and responding to the calling instruction, and generating content output. By obtaining the content input by the user and combining the historical dialogue corresponding to the user, the processing content for the user can be automatically completed, the large model can effectively rewrite and identify the intention of the processing content through the prompt generated by the processing content, the requirement for the content input by the user is greatly reduced through the mode, the processing process is more flexible, and the user experience is improved. Meanwhile, after accurate intention recognition is obtained, next-step processing can be carried out more accurately, so that accurate content output is obtained, and the efficiency and accuracy of processing user problems are higher.

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 provides a large model pruning method, and a response method, device and equipment based on a large model, and relates to the technical field of artificial intelligence, in particular to the technical fields of large models, generative models, pre-training models, fine tuning, dialogue models, deep learning and the like. According to the specific implementation scheme, on the basis of multiple model parameters of an initial large model, according to a first sample and a first label which meet preset requirements and a second sample and a second label which do not meet the preset requirements, a processor is used for executing a loss calculation task, and a first loss value of the first sample and a second loss value of the second sample are obtained; then executing the evaluation task by utilizing a processor to obtain importance indexes of the plurality of model parameters; wherein the importance index represents the degree that the output information determined based on the model parameters meets the predetermined requirement; and based on the importance indexes, utilizing a processor to execute pruning tasks on the plurality of model parameters to obtain a pruned large model.

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 user question response method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: receiving problem data sent by a user terminal, and determining a problem type according to the problem data; wherein the question type is one of a knowledge question and answer class and a task execution class; if the problem type is a task execution type, determining a task execution process according to the problem data, and generating at least one level of cue word according to the task execution process; and calling a task execution tool according to the prompt words at all levels, determining response data, and feeding back the response data to the user terminal. According to the technical scheme, the problems that an existing building operation and maintenance management mode is tedious in operation and low in efficiency are solved, and the experience threshold of a user is lowered, the operation and maintenance operation process is simplified and the operation and maintenance management efficiency is improved by constructing the question processing service in the building operation and maintenance management system.

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.

A response method, a terminal, and a storage medium. The response method comprises: determining, at a first time point by means of speech recognition processing, a first target text corresponding to the first time point (1001); determining, according to the first target text, a first predicted intention and an answer to be pushed, wherein said answer is used for responding to speech information (1002); continuing to determine, by means of the speech recognition processing, a second target text corresponding to a second time point and a second predicted intention, wherein the second time point is the next successive time point of the first time point (1003); determining, according to the first predicted intention and the second predicted intention, whether a preset response condition is satisfied (1004); and responding according to said answer if the preset response condition is determined to be satisfied (1005).

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 provides a dialogue response method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: determining a reply bedding verbal skill corresponding to dialogue content based on the dialogue content inputted by a user, and carrying out the knowledge acquisition of the dialogue content, and obtaining the knowledge content corresponding to the dialogue content; transmitting the reply bedding verbal skill to the man-machine interaction end to enable the man-machine interaction end to output the reply bedding verbal skill, and performing continuous writing on the reply bedding verbal skill based on the knowledge content to obtain dialogue reply information corresponding to the dialogue content; and transmitting the dialogue reply information to the man-machine interaction end, so that the man-machine interaction end outputs information except the reply underlay verbal skill in the dialogue reply information. According to the scheme, the reply bedding verbal skill can be generated and output while the knowledge content is obtained, then the reply bedding verbal skill is continuously written to obtain and output the dialogue reply information, and when the dialogue reply information is not generated, the reply bedding verbal skill is firstly output to respond to the dialogue, so that the dialogue response efficiency and the user experience effect are improved.