KR-20260064552-A - METHOD AND SYSTEM FOR PROVIDING CONSULTATION SERVICE USING A CHATBOT BASED ON A LARGE LANGUAGE MODEL

KR20260064552AKR 20260064552 AKR20260064552 AKR 20260064552AKR-20260064552-A

Abstract

The present invention relates to a method and system for providing a consultation service using a large language model-based chatbot. The method for providing a consultation service using a large language model-based chatbot according to the present invention may include the steps of: collecting user-related information related to a user account logged into a user terminal; identifying similar users among different users who have characteristics similar to said user based on said user-related information; creating a user group including said user and said similar users; creating a group data set based on user-related information of a plurality of users included in said user group; constructing a group chatbot model corresponding to said user group using said group data set based on a previously trained large language model; and generating an answer corresponding to a user query received from said user terminal using said group chatbot model, and providing said generated answer to said user terminal.

Inventors

윤찬
최재혁

Assignees

에버엑스 주식회사

Dates

Publication Date: 20260507
Application Date: 20251023
Priority Date: 20241030

Claims (14)

A step of collecting user-related information associated with a user account logged into a user terminal; Based on the above user-related information, a step of identifying similar users among different users who have characteristics similar to the above user; A step of creating a user group including the above user and the above similar users; A step of generating a group data set based on user-related information of a plurality of users included in the above user group; A step of constructing a group chatbot model corresponding to the user group using the group dataset based on a previously trained large language model; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of generating an answer corresponding to a user query received from the user terminal using the group chatbot model above, and providing the generated answer to the user terminal.
In paragraph 1, The step of building the above group chatbot model is, A step of fine-tuning the previously trained large language model using the group data set corresponding to the user group; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of constructing a group chatbot model that generates an answer to a user query based on a group dataset using the finely tuned large language model.
In paragraph 2, The step of providing the above answer to the user terminal is, A step of generating a prompt that generates the answer to the user query based on at least one of the above user-related information and the above group data set; A step of processing the above prompt as input to the above fine-tuned large language model to obtain the above answer to the user query through the above large language model; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of providing the above answer to the group chatbot model, and providing the above answer to the user query to the user terminal through the group chatbot model.
In paragraph 3, The step of generating the above prompt is, A step of extracting keywords related to the user query from the group data set; A step of identifying specific user-related information related to the keyword among a plurality of user-related information included in the group data set; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of generating a prompt that generates an answer to a user query using the specific user-related information.
In paragraph 1, The step of identifying the aforementioned similar users is, A step of searching for similar user-related information in a database based on similarity with the above user-related information; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of identifying a specific user corresponding to the similar user-related information as a similar user based on the similarity.
In paragraph 5, The step of searching for the above-mentioned similar user-related information is, A step of embedding the above user-related information to obtain a target user vector corresponding to the above user-related information; A step of calculating the similarity between a registered user vector corresponding to each of the different users stored in the database and the target user vector; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of specifying registered user-related information corresponding to at least one registered user vector satisfying a preset similarity condition as similar user-related information.
In paragraph 1, The step of providing the above answer to the user terminal is, A step of extracting a query-response list including a plurality of query-response items related to the user query from the group data set; and A method for providing a consultation service using a large language model-based chatbot, characterized by further including the step of providing a consultation page to a user terminal capable of performing follow-up consultation related to at least one question-and-answer item included in the above question-and-answer list.
In Paragraph 7, The step of providing the above consultation page to a user terminal is: A step of specifying one of the plurality of question-and-answer items constituting the question-and-answer list according to a first user input input to the user terminal; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of providing a consultation page to a user terminal where a user query can be entered, so as to perform a follow-up consultation related to any one of the aforementioned specified question-and-answer items.
In paragraph 3, In relation to the above user query, the method further includes the step of creating a group chat room capable of query-answering between the user included in the user group and the plurality of users, and providing it to the user terminal. In the step of generating the above prompt, A method for providing a consultation service using a large language model-based chatbot, characterized by generating a prompt that generates an answer to a user query using question-and-answer information including conversation content performed in the group chat room.
In Paragraph 9, In the step of providing the above group chat room to the user terminal, A step of outputting a list of group members for each of multiple user groups to a user terminal; A step of generating an invitation icon corresponding to each of the group members included in the group member list in a part area of the group member list; Based on receiving a second user input for a specific invitation icon from a user terminal, a step of transmitting an invitation request to a guest user terminal where the guest user's user account is logged in, so that the guest user corresponding to the specific invitation icon among the group members can perform a question and answer in the group chat room; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of providing the group chat room that the guest user can participate in to the user terminal based on the occurrence of an approval event for the invitation request.
In paragraph 1, The above user-related information is, A method for providing a consultation service using a large language model-based chatbot, characterized by including at least one of medical information, exercise history information, question and answer information, and exercise program information related to the user.
In paragraph 1, The method further includes the step of updating the exercise program set in the user account according to the user query received from the user terminal, and The step of updating the above exercise program is, A step of generating an update prompt to update the exercise program assigned to the user account using the above user-related information; A step of inputting the above update prompt into the above large language model, and generating an updated exercise program according to the user query through the above large language model; A step of transmitting an approval request for the above-mentioned updated exercise program to a medical staff terminal; and A method for providing a consultation service using a large language model-based chatbot, characterized by including the step of setting the updated exercise program to the user account based on the occurrence of an approval event according to the approval request from the medical staff terminal.
A communication unit that receives a user query from a user terminal; and It includes a control unit that collects user-related information related to a user account logged into the user terminal, and The above control unit is, Based on the above user-related information, similar users having characteristics similar to the above user are identified among different users, and Creates a user group including the above user and the above similar users, Based on user-related information of multiple users included in the above user group, a group data set is generated, and Based on a pre-trained large language model, a group chatbot model corresponding to the user group is constructed using the group dataset, and A consultation service provision system using a large language model-based chatbot, characterized by using the group chatbot model above to generate an answer corresponding to the user query received from the user terminal and providing the answer to the user terminal.
A program that is executed by one or more processes in an electronic device and stored on a computer-readable recording medium, The above program is, A step of collecting user-related information associated with a user account logged into a user terminal; Based on the above user-related information, a step of identifying similar users among different users who have characteristics similar to the above user; A step of creating a user group including the above user and the above similar users; A step of generating a group data set based on user-related information of a plurality of users included in the above user group; A step of constructing a group chatbot model corresponding to the user group using the group dataset based on a previously trained large language model; and A program stored on a computer-readable recording medium, characterized by including instructions that, using the group chatbot model above, generate an answer corresponding to a user query received from the user terminal and provide the answer to the user terminal.

Description

Method and System for Providing Consultation Service Using a Chatbot Based on a Large Language Model The present invention relates to a method and system for providing consultation services to a user using a chatbot based on a large language model. Recently, with the rapid advancement of artificial intelligence (AI) technology, generative language models (e.g., ChatGPT) capable of interacting naturally with humans are gaining attention. In particular, chatbots based on Large Language Models (LLMs) are being widely utilized in areas such as information provision, consultation, and recommendations, based on advanced language processing technology that understands context and freely generates responses, unlike existing chatbots that only performed simple responses within limited scenarios. Such technological advancements are presenting new possibilities in the field of rehabilitation treatment and management. During the rehabilitation process, patients experience various physical and psychological difficulties, such as pain, limited range of motion, and anxiety regarding repetitive movements; therefore, providing accurate information and emotional support tailored to these situations serves as crucial elements of the rehabilitation process. However, receiving real-time consultation from medical professionals whenever patients need it is often difficult due to realistic limitations, such as time constraints, a shortage of medical personnel, and financial burdens. To address these issues, there has recently been a surge in demand for non-face-to-face consultation using large language model-based chatbots. Large language model-based chatbots go beyond simply conveying information to partially replace the role of a counselor and are also receiving positive evaluations for their ability to empathize with emotions. However, while large language models are trained on vast amounts of general-purpose data and possess excellent generality capable of responding to various topics and situations, they have limitations in providing personalized information that reflects the context of individual users or specific user groups. In other words, by generating generalized responses without considering the user's state, background, or preferences, there is a possibility that information unsuitable for individual needs or unnecessary information may be provided. To overcome these challenges, there is a growing demand for technologies that improve the precision of user-customized responses while maintaining the generality of large language models. In particular, active research is being conducted on techniques that data-drivenly cluster user groups with similar characteristics—such as health status, behavioral history, medical history, and interests—and fine-tune large language models based on the groups' conversation history or question-and-answer data. As such, there is a need to provide customized consultation services to users by building a chatbot model with response tendencies specialized for specific groups based on a large language model. FIG. 1 is a conceptual diagram illustrating a consultation service provision system using a large language model-based chatbot according to the present invention. FIG. 2 is a flowchart illustrating a method for providing a consultation service using a large language model-based chatbot according to the present invention. FIG. 3 is a conceptual diagram illustrating user-related information according to the present invention. Figure 4 is a conceptual diagram illustrating the process of optimizing a large language model according to a user group according to the present invention. FIG. 5 is a conceptual diagram illustrating the process of generating a prompt to be input into a large language model according to the present invention. FIGS. 6a and FIGS. 6b are conceptual diagrams for explaining the process of providing an exercise guide by analyzing image data according to the present invention. FIGS. 7a and 7b are conceptual diagrams illustrating the process of providing answers to user queries through a group-based chatbot according to the present invention. FIG. 8a is a conceptual diagram illustrating an embodiment in which a user continues a conversation with a group member of a user group, such as a user, according to the present invention. FIG. 8b is a conceptual diagram illustrating an embodiment of performing a conversation between group members and generating a prompt based on the conversation content through a group chat room of a user group according to the present invention. FIGS. 9a and 9b are conceptual diagrams illustrating an embodiment of updating an exercise program according to a user query according to the present invention. FIG. 10 is a block diagram illustrating a computing system in which the present invention can be implemented. FIGS. 11 and FIGS. 12 are block diagrams illustrating an embodiment of a computing device according to the present invention. Hereinafter, embod