KR-20260064594-A - METHOD AND SYSTEM FOR PROVIDING PATIENT-CUSTOMIZED FEEDBACK BASED ON ARTIFICIAL INTELLIGENCE

KR20260064594AKR 20260064594 AKR20260064594 AKR 20260064594AKR-20260064594-A

Abstract

The present invention relates to an artificial intelligence-based method and system for providing patient-customized feedback. The artificial intelligence-based method for providing patient-customized feedback according to the present invention may include the steps of: collecting patient information related to the patient's indications; extracting emotional information related to the patient's emotional state from the patient information; analyzing the emotional state related to the indications based on the extracted emotional information and generating a prompt requesting the generation of feedback content related to the emotional state; processing the generated prompt as input to a pre-trained feedback generation model to obtain the feedback content related to the emotional state; and providing the feedback content related to the emotional state to a user terminal.

Inventors

윤찬
최치현

Assignees

에버엑스 주식회사

Dates

Publication Date: 20260507
Application Date: 20251029
Priority Date: 20241030

Claims (12)

A step of collecting patient information related to the patient's indications; A step of extracting emotional information related to the emotional state of the patient from the patient information; A step of analyzing the emotional state related to the indication based on the extracted emotional information, and generating a prompt requesting the generation of feedback content related to the emotional state; A step of processing the generated prompt as input to a pre-trained feedback generation model to obtain the feedback content related to the emotional state; and An AI-based method for providing patient-customized feedback, characterized by including the step of providing feedback content related to the emotional state to a user terminal.
In paragraph 1, The above emotional information is, Type 1 emotional information related to the general emotions of the above patient and An AI-based method for providing patient-customized feedback, characterized by including at least one of a second type of emotional information related to the patient's indications.
In paragraph 2, In the step of generating the above prompt, Generate the prompt based on at least one of the above emotional information, the result of the emotional state analysis, and the above patient information, and The results of the above emotional state analysis are, The first type emotional state analysis result corresponding to the above first type emotional information and An artificial intelligence-based method for providing patient-customized feedback, characterized by including at least one of the second type emotional state analysis results corresponding to the second type emotional information.
In paragraph 3, The above prompt is, A first type prompt generated based on at least one of the above patient information, the above first type emotion information, and the above first type emotion state analysis result, and An artificial intelligence-based method for providing patient-customized feedback, characterized by including at least one of a second type prompt generated based on at least one of the above patient information, the above second type emotion information, and the above second type emotion state analysis result.
In paragraph 1, In the step of providing the above feedback content to the user terminal, An AI-based patient-customized feedback provision method characterized by detecting the occurrence of a preset feedback event and, in response to detecting the occurrence of the feedback event, providing the feedback content to the user terminal.
In paragraph 5, It further includes a step of monitoring the emotional state associated with the above indication, and In the above monitoring step, An AI-based method for providing patient-customized feedback characterized by monitoring changes in emotion classes corresponding to the emotional states analyzed sequentially over time.
In paragraph 6, In the step of providing the above feedback content to the user terminal, In response to detecting a change in the type of the above emotion class, the above feedback event occurs, and An AI-based patient-customized feedback provision method characterized by determining the time at which the above feedback event occurs as the time of feedback provision, and providing the feedback content to the user terminal at the time of feedback provision.
In paragraph 1, In the step of providing the above feedback content to the user terminal, An artificial intelligence-based patient-customized feedback provision method characterized by providing a user interface to the user terminal for providing at least one of a feedback program and a counseling program corresponding to the emotional state along with the feedback content.
In paragraph 8, The above counseling program is, An AI-based method for providing patient-customized feedback, characterized by providing an answer to a user query entered in relation to at least one of the patient's indications, emotional state, and feedback content, based on a previously trained large language model.
In paragraph 2, In the step of providing the above feedback content to the user terminal, A method for providing AI-based patient-customized feedback characterized by providing at least one of a first emotional state analysis report generated by analyzing the first type of emotional information and a second emotional state analysis report generated by analyzing the second type of emotional information.
In electronic devices, Memory for storing instructions; and It includes at least one processor electrically connected to the memory, and When the above instructions are executed by the at least one processor, the at least one processor, Collect patient information related to the patient's indications, and Emotional information related to the emotional state of the patient is extracted from the patient information above, and Based on the extracted emotional information, analyze the emotional state related to the indication and generate a prompt requesting the creation of feedback content related to the emotional state, and The generated prompt is processed as input to a pre-trained feedback generation model to obtain the feedback content related to the emotional state, and An AI-based patient-customized feedback provision system characterized by providing the above-mentioned feedback content related to the above-mentioned emotional state to a 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 patient information related to the patient's indications; A step of extracting emotional information related to the emotional state of the patient from the patient information; A step of analyzing the emotional state related to the indication based on the extracted emotional information, and generating a prompt requesting the generation of feedback content related to the emotional state; A step of processing the generated prompt as input to a pre-trained feedback generation model to obtain the feedback content related to the emotional state; and A program stored on a computer-readable recording medium characterized by including instructions that perform the step of providing the feedback content related to the above emotional state to a user terminal.

Description

Method and System for Providing Patient-Customized Feedback Based on Artificial Intelligence The present invention relates to a method and system for providing patient-customized feedback based on artificial intelligence. With the rapid advancement of artificial intelligence (AI) technology in recent years, generative AI models capable of natural conversation with humans (e.g., ChatGPT) have emerged. In particular, unlike existing chatbots, Large Language Models (LLMs) provide conversational quality similar to humans based on their ability to understand various contexts and generate natural language, and are being rapidly adopted in various industrial fields such as medical, education, and healthcare. With the advancement of such artificial intelligence technology, interest in AI-based patient-tailored treatment support technologies is rapidly expanding in the medical field. Specifically, the medical industry is seeing a growing need for technologies that utilize AI to provide non-face-to-face rehabilitation treatment, and to offer feedback that reinforces motivation for treatment or induces emotional stability by analyzing data on the patient's condition, emotions, and behaviors during the rehabilitation process. In this regard, existing non-face-to-face feedback methods have limitations in that they fail to adequately reflect individual patients' situations or emotional states because they provide predefined, fixed feedback uniformly to various patients. Furthermore, the uniform feedback provided by existing non-face-to-face methods is perceived as noise by patients, which lowers their motivation to participate in treatment. Specifically, under existing non-face-to-face feedback methods, patients are unable to engage with content irrelevant to their condition and are more likely to drop out early, which can significantly lower treatment continuation rates and overall treatment satisfaction. Accordingly, there is a need for technology that can simultaneously enhance patient motivation for treatment and emotional support by analyzing changes in a patient's condition and emotions through artificial intelligence and generating and providing personalized feedback in response to the analysis results. FIG. 1 is a conceptual diagram illustrating an artificial intelligence-based patient-customized feedback provision system according to the present invention. FIG. 2 is a flowchart illustrating the overall method for providing patient-customized feedback based on artificial intelligence according to the present invention. FIGS. 3a to 3d are conceptual diagrams for specifically explaining patient information and user feedback information according to the present invention. FIGS. 4a and FIGS. 4b are conceptual diagrams illustrating the process of extracting different types of emotional information from patient information according to the present invention. FIG. 5 is a conceptual diagram illustrating the process of generating a prompt according to the present invention. FIGS. 6a to 6c are conceptual diagrams illustrating the process of providing feedback content to a user terminal according to the present invention. FIGS. 7a and FIGS. 7b are conceptual diagrams for explaining a feedback program according to the present invention. FIGS. 7c and FIGS. 7d are conceptual diagrams for explaining a counseling program according to the present invention. FIGS. 7e to 7g are conceptual diagrams for explaining an emotional state analysis report according to the present invention. FIG. 8 is a block diagram illustrating a computing system in which the present invention can be implemented. FIGS. 9 and FIGS. 10 are block diagrams illustrating an embodiment of a computing device according to the present invention. Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Identical or similar components are assigned the same reference number regardless of the drawing symbols, and redundant descriptions thereof will be omitted. The suffixes "module" and "part" used for components in the following description are assigned or used interchangeably solely for the ease of drafting the specification and do not have distinct meanings or roles in themselves. Furthermore, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of related prior art could obscure the essence of the embodiments disclosed in this specification, such detailed description will be omitted. Additionally, the attached drawings are intended only to facilitate understanding of the embodiments disclosed in this specification; the technical concept disclosed in this specification is not limited by the attached drawings, and it should be understood that they include all modifications, equivalents, and substitutions that fall within the spirit and technical scope of the present invention. Terms including ordinal numbers, such as first, second, etc., may be