System

Abstract

To provide a system that streamlines the initial consultation process and guides users to suitable medical institutions.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a referral unit, and a provision unit. The reception unit accepts user responses. The analysis unit analyzes the data received by the reception unit. The referral unit introduces a suitable medical institution based on the information analyzed by the analysis unit. The provision unit provides the information collected by the analysis unit to a doctor.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the initial medical history and selection of an appropriate medical institution are not carried out smoothly, and there is room for improvement.

[0005] The system according to the embodiment aims to make the interview process at the first visit more efficient and to introduce a patient to an appropriate medical institution. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a referral unit, and a provision unit. The reception unit receives a user's answer. The analysis unit analyzes the information received by the reception unit. The referral unit refers a user to an appropriate medical institution based on the information analyzed by the analysis unit. The provision unit provides the information collected by the analysis unit to a doctor. Effect of the Invention

[0007] The system according to the embodiment can make the initial medical interview more efficient and introduce a patient to an appropriate medical institution. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The first consultation interview system according to the embodiment of the present invention is a system that allows a user to complete a medical interview at home in advance. This system starts when the user answers the question "What's wrong with you today?" at home and inputs the answer into the system. The system uses AI to analyze the user's answer and conducts multiple interviews to grasp the user's symptoms and condition in detail. This allows the next preparation to proceed smoothly. In addition, if the user does not have a family doctor or does not know which department to go to, the AI ​​will introduce the appropriate medical institution. However, a mechanism is built in that prevents the user from making their own judgment or dealing with the problem on their own, and the system functions only as support before seeing a doctor. For example, a user answers the question "What's wrong with you today?" at home. At this time, the user inputs details of their symptoms and condition. For example, they input information such as "I have a headache," "I have a fever," and "I have a cough." This information is entered into the system and analyzed by AI. Next, the AI ​​analyzes the input information and grasps the user's symptoms and condition. The AI ​​asks additional questions based on the user's answers and collects detailed information. For example, if the user answers "I have a headache," the AI ​​will ask additional questions such as "When did the pain start?" and "How strong is the pain?" This allows the user to understand the user's symptoms and condition in detail. Furthermore, the AI ​​can smoothly proceed with the next preparation based on the information collected. For example, when the user arrives at the medical institution, the information collected in advance is provided to the doctor. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. In addition, if the user does not have a family doctor or does not know which department to go to, the AI ​​will introduce the appropriate medical institution. For example, if the user answers "I have a headache," the AI ​​will suggest an appropriate department such as "internal medicine" or "neurology." This allows the user to select the appropriate medical institution. However, a mechanism is built in that prevents the user from making their own judgment or dealing with the situation on their own. For example, the AI ​​will provide advice such as "This symptom is highly urgent, so please visit a medical institution immediately." This allows the user to visit an appropriate medical institution and functions as support before seeing a doctor.This allows the initial consultation interview system to allow the user to complete the interview in advance at home, allowing the examination at the medical institution to proceed smoothly.

[0029] The initial consultation interview system according to the embodiment includes a reception unit, an analysis unit, a referral unit, and a provision unit. The reception unit receives a user's answer. The user's answer includes, but is not limited to, information such as "I have a headache," "I have a fever," and "I have a cough." The reception unit, for example, allows the user to answer a question such as "What's wrong with you today?" at home and inputs the answer into the system. The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI to analyze the user's answer and grasps the user's symptoms and condition. For example, the analysis unit asks additional questions based on the user's answer and collects detailed information. For example, if the user answers "I have a headache," the analysis unit asks additional questions such as "When did the pain start?" and "How strong is the pain?" This makes it possible to grasp the user's symptoms and condition in detail. The referral unit introduces an appropriate medical institution based on the information analyzed by the analysis unit. The referral unit uses AI to suggest an appropriate medical institution based on the user's symptoms and taking into account past data and the expertise of the medical institution. For example, if the user answers "I have a headache," the referral unit suggests an appropriate department, such as "internal medicine" or "neurology." This allows the user to select an appropriate medical institution. The providing unit provides the information collected by the analyzing unit to the doctor. The providing unit automatically transfers the collected information to the electronic medical record system. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. For example, the providing unit automatically transfers the collected information to the electronic medical record system, allowing the doctor to quickly examine the user. This allows the initial consultation interview system according to the embodiment to accept and analyze the user's answers, introduce an appropriate medical institution, and provide the information to the doctor, allowing the initial consultation interview to be completed at home in advance.

[0030] The reception unit receives the user's answer. The user's answer includes, but is not limited to, information such as "I have a headache," "I have a fever," and "I have a cough." For example, the reception unit allows the user to answer the question "How are you feeling today?" at home and inputs the answer into the system. Specifically, the reception unit provides a mechanism for the user to use a smartphone or a PC to access a dedicated application or website and input the answer into a medical questionnaire. The user can input symptoms in multiple choice format or free description format, which allows the user to accurately convey detailed symptoms and conditions. Furthermore, the reception unit has a function of checking the user's input content in real time and automatically presenting additional questions if there is missing information or unclear points. For example, for a user who answers "I have a headache," detailed questions such as "Where is the pain?" and "How often do you experience pain?" are asked to collect more specific information. Furthermore, the reception unit safely manages the user's answer data and protects the data using encryption technology to protect privacy. This allows the user to report their symptoms with peace of mind. Furthermore, the reception unit has multilingual support and a voice input function, and can accommodate a variety of users, including users who speak different languages, visually impaired people, etc. This enables the reception unit to flexibly respond to the diverse needs of users, and can improve the efficiency and accuracy of initial consultation interviews.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI to analyze the user's answers and understand the user's symptoms and condition. Specifically, the analysis unit uses natural language processing technology to perform text analysis of the user's answers and extracts symptom keywords and related information. For example, if the user answers "I have a headache," the analysis unit extracts the keyword "headache" and generates additional questions such as "When did the pain start?" and "How strong is the pain?" This allows the user's symptoms and condition to be understood in detail. The analysis unit analyzes the pattern and association of symptoms based on the user's answer data and predicts possible diagnosis results. For example, if headache symptoms occur frequently and the pain intensity is increasing, the analysis unit suggests the possibility of migraine or tension headache. In addition, the analysis unit refers to past data and medical literature to provide information for making a more accurate diagnosis based on the combination of symptoms and frequency of occurrence. In addition, the analysis unit anonymizes the user's answer data and performs statistical analysis to understand trends and patterns related to specific symptoms and conditions. This allows the analysis unit to contribute not only to the symptoms of individual users, but also to understanding health conditions at a group level and planning preventive measures. The analysis unit can continuously improve the accuracy of the analysis using AI learning algorithms. For example, every time new data on symptoms or conditions is added, the AI ​​model is retrained to improve the accuracy of the analysis. This allows the analysis unit to always provide highly accurate analysis based on the latest information and support the user's health management.

[0032] The referral unit introduces an appropriate medical institution based on the information analyzed by the analysis unit. The referral unit uses AI to suggest an appropriate medical institution based on the user's symptoms, taking into account past data and the specialty of the medical institution. Specifically, an algorithm is used to select the most suitable medical department and medical institution according to the user's symptoms and condition. For example, if the user answers "I have a headache," the referral unit not only suggests an appropriate department such as "internal medicine" or "neurology," but also selects the most suitable medical institution by comprehensively considering past medical data, the specialty of the medical institution, the user's place of residence, and ease of access. The referral unit also refers to the evaluation and word-of-mouth information of the medical institution to provide the user with reliable information. For example, based on the evaluation and word-of-mouth of past patients, it evaluates the medical quality and response of the medical institution and presents the user with appropriate options. In addition, the referral unit considers the user's wishes and constraints, and suggests a medical institution taking into account the ease of making an appointment and the flexibility of consultation hours. This allows the user to select the medical institution that best suits his or her needs. In addition, the referral unit also provides support for the user to make an appointment at the proposed medical institution. For example, the online reservation system allows users to easily make reservations and also provides functions such as reservation confirmation and reminder notifications, etc. This allows the referral department to support users so that they can smoothly visit medical institutions and streamline the initial consultation process.

[0033] The providing unit provides the information collected by the analysis unit to the doctor. The providing unit automatically transfers the collected information to the electronic medical record system. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. Specifically, the providing unit integrates the user's answer data and analysis results into the electronic medical record system, allowing the doctor to check the necessary information before the examination. For example, if the user answers "I have a headache" and the analysis unit analyzes that "it is highly likely to be a migraine," the information is automatically recorded in the electronic medical record and the doctor can refer to it during the examination. This allows the doctor to understand the user's symptoms and condition in advance and perform a prompt and appropriate examination. Furthermore, the providing unit provides information that is useful for follow-up after the examination and for planning a treatment plan. For example, the information on the examination results and prescribed medicines is recorded in the electronic medical record, so that it can be referred to at the next examination. In addition, the providing unit centrally manages the user's examination history and treatment progress, and supports the doctor to perform examinations and treatments based on past information. This allows the providing unit to facilitate information sharing between the doctor and the user, improving the efficiency and accuracy of the examination. Furthermore, the provider takes into consideration the protection of user privacy and ensures the security of information by encrypting data and controlling access, which allows the provider to improve the quality of medical care while gaining the trust of users.

[0034] The analysis unit can ask additional questions based on the user's answer to collect detailed information. The detailed information includes, for example, details of symptoms, past medical history, etc., but is not limited to such examples. The analysis unit, for example, asks additional questions based on the user's answer to collect detailed information. For example, if the user answers "I have a headache," the analysis unit asks additional questions such as "When did the pain start?" and "How strong is the pain?". The analysis unit can also ask questions about the past medical history and current health condition based on the user's answer. For example, if the user answers "I have a fever," the analysis unit asks additional questions such as "Since when have you had a fever?" and "What other symptoms do you have?". This allows the analysis unit to grasp the user's symptoms and condition in detail. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the user's answer to the generation AI and causes the generation AI to execute additional questions. This allows detailed information to be collected by asking additional questions based on the user's answer.

[0035] The referral unit can suggest an appropriate medical institution based on past data or the specialty of the medical institution based on the user's symptoms. Past data includes, for example, past medical records, statistical data, etc., but is not limited to such examples. The specialty of the medical institution includes, for example, the number of specialists, treatment results, etc., but is not limited to such examples. The referral unit, for example, refers to past data based on the user's symptoms and suggests an appropriate medical institution. For example, if the user answers "I have a headache," the referral unit refers to past medical records and suggests an appropriate department such as "internal medicine" or "neurology." The referral unit can also suggest an appropriate medical institution based on the user's symptoms, taking into account the specialty of the medical institution. For example, if the user answers "I have a fever," the referral unit suggests a medical institution with a high treatment record. This allows the user to select an appropriate medical institution. A part or all of the above-mentioned processing in the referral unit may be performed, for example, using AI, or may be performed without using AI. For example, the referral unit can input the user's symptoms into the generation AI and cause the generation AI to execute the suggestion of an appropriate medical institution. This allows the user to select an appropriate medical institution by suggesting an appropriate medical institution based on the user's symptoms.

[0036] The providing unit can automatically transfer the collected information to the electronic medical record system. The method of automatic transfer includes, for example, API linkage, data format, and the like, but is not limited to such examples. The providing unit, for example, automatically transfers the collected information to the electronic medical record system. For example, the providing unit transfers the collected information to the electronic medical record system using API linkage. The providing unit can also transfer information using a specific data format. For example, the providing unit transfers information using the HL7 (Health Level 7) format. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected information to the generating AI and cause the generating AI to execute transfer to the electronic medical record system. This allows the doctor to quickly examine the user by automatically transferring the collected information to the electronic medical record system.

[0037] The analysis unit can detect symptoms that require urgent treatment and display an alert to encourage the user to seek emergency medical treatment. Examples of symptoms that require urgent treatment include, but are not limited to, a specific symptom list, an algorithm, and the like. The analysis unit detects symptoms that require urgent treatment based on, for example, a user's answer. For example, when the user answers "my chest hurts," the analysis unit detects the symptom as an urgent symptom and displays an alert to encourage the user to seek emergency medical treatment. The analysis unit can also detect symptoms that require urgent treatment using a specific algorithm. For example, the analysis unit uses AI to analyze the user's answer and detects symptoms that require urgent treatment. This allows the user to seek medical treatment at an appropriate time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer to the generation AI and cause the generation AI to detect symptoms that require urgent treatment. This allows the user to seek medical treatment at an appropriate time by detecting symptoms that require urgent treatment and encouraging the user to seek emergency medical treatment.

[0038] The reception unit can analyze the user's past answer history and determine the optimal order of questions. The reception unit, for example, analyzes the user's past answer history and determines the optimal order of questions. For example, the reception unit preferentially displays questions that the user has frequently answered in the past. The reception unit can also automatically select highly relevant questions from the user's past answer history. Furthermore, the reception unit can analyze the user's answer pattern and propose an efficient order of questions. For example, the reception unit determines the optimal order of questions based on the user's past answer pattern. As a result, an efficient order of questions can be determined by analyzing the user's past answer history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's past answer history to the generation AI and cause the generation AI to execute the determination of the optimal order of questions. As a result, an efficient order of questions can be determined by analyzing the user's past answer history.

[0039] The reception unit can customize the content of the question based on the user's current health condition and lifestyle habits when asking a question. The reception unit customizes the content of the question based on the user's current health condition and lifestyle habits, for example. For example, the reception unit adds related questions based on the user's existing medical history. The reception unit can also ask specific questions based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can select appropriate questions based on the user's current health condition (fatigue, stress, etc.). For example, the reception unit customizes the content of the question based on the user's health condition and lifestyle habits. As a result, by customizing the content of the question based on the user's current health condition and lifestyle habits, more appropriate questions can be asked. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit inputs data on the user's health condition and lifestyle habits into the generation AI, and causes the generation AI to customize the content of the question. As a result, by customizing the content of the question based on the user's current health condition and lifestyle habits, more appropriate questions can be asked.

[0040] When asking a question, the reception unit can prioritize questions with high relevance based on the geographical location information of the user. The reception unit, for example, prioritizes questions with high relevance taking into account the geographical location information of the user. For example, if the user lives in a specific area, the reception unit asks questions based on health risks associated with the area. In addition, if the user is traveling, the reception unit can also ask questions based on health risks at the travel destination. Furthermore, if the user is close to a specific medical institution, the reception unit can also ask questions based on the expertise of the medical institution. For example, the reception unit prioritizes questions with high relevance based on the geographical location information of the user. As a result, by taking into account the geographical location information of the user, questions with high relevance can be prioritized. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the geographical location information of the user to the generation AI and causes the generation AI to select questions with high relevance. As a result, by taking into account the geographical location information of the user, questions with high relevance can be prioritized.

[0041] The reception unit can analyze the social media activity of the user and add related questions when asking a question. The reception unit, for example, analyzes the social media activity of the user and adds related questions. For example, when the user posts about health on social media, the reception unit asks a question based on the content of the post. In addition, when the user shows an interest in a specific health topic, the reception unit can ask a question related to the topic. Furthermore, the reception unit can add questions about lifestyle habits from the social media activity of the user. For example, the reception unit analyzes the social media activity of the user and adds related questions. As a result, related questions can be added by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit inputs the social media activity of the user to the generation AI and causes the generation AI to execute the addition of related questions. As a result, related questions can be added by analyzing the social media activity of the user.

[0042] The analysis unit can optimize the analysis algorithm by referring to the user's past medical history during the analysis. The analysis unit adjusts the analysis algorithm, for example, based on the user's past diagnosis results. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past diagnosis results. The analysis unit can also optimize the analysis result by taking into account the user's past treatment history. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past treatment history. Furthermore, the analysis unit can customize the analysis algorithm based on the user's past health condition. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past health condition. As a result, the analysis algorithm can be optimized by referring to the user's past medical history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the user's past medical history into the generation AI and causes the generation AI to execute optimization of the analysis algorithm. As a result, the analysis algorithm can be optimized by referring to the user's past medical history.

[0043] The analysis unit can perform the analysis based on the user's lifestyle or environmental factors at the time of analysis. The analysis unit adjusts the analysis result, for example, taking into account the user's smoking habits. For example, the analysis unit optimizes the analysis algorithm by referring to the user's smoking habits. The analysis unit can also optimize the analysis algorithm by considering the user's drinking habits. For example, the analysis unit adjusts the analysis algorithm by referring to the user's drinking habits. Furthermore, the analysis unit can customize the analysis result by considering the user's living environment (urban area, rural area, etc.). For example, the analysis unit optimizes the analysis algorithm by referring to the user's living environment. As a result, a more appropriate analysis result can be provided by taking into account the user's lifestyle and environmental factors. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the user's lifestyle and environmental factors to the generation AI and cause the generation AI to execute the analysis. As a result, a more appropriate analysis result can be provided by taking into account the user's lifestyle and environmental factors.

[0044] The analysis unit can perform the analysis based on the geographical location information of the user at the time of analysis. For example, when the user lives in a specific area, the analysis unit performs the analysis taking into account the health risks of the area. For example, the analysis unit refers to the geographical location information of the user and performs the analysis taking into account the health risks of the area. In addition, when the user is traveling, the analysis unit can also perform the analysis taking into account the health risks of the travel destination. For example, the analysis unit refers to the geographical location information of the user and performs the analysis taking into account the health risks of the travel destination. In addition, when the user is close to a specific medical institution, the analysis unit can also perform the analysis taking into account the expertise of the medical institution. For example, the analysis unit refers to the geographical location information of the user and performs the analysis taking into account the expertise of the specific medical institution. As a result, by taking into account the geographical location information of the user, a more appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the analysis. As a result, by taking into account the geographical location information of the user, a more appropriate analysis result can be provided.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during the analysis. For example, when the user posts on health on social media, the analysis unit reflects the content in the analysis. For example, the analysis unit refers to the user's social media activity and reflects the content of the health post in the analysis. In addition, when the user shows an interest in a specific health topic, the analysis unit can also reflect information related to the topic in the analysis. For example, the analysis unit refers to the user's social media activity and reflects information related to the specific health topic in the analysis. In addition, the analysis unit can also reflect information on lifestyle habits from the user's social media activity in the analysis. For example, the analysis unit refers to the user's social media activity and reflects information on lifestyle habits in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity to the generation AI and cause the generation AI to execute the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's social media activity.

[0046] The introduction unit can select the most suitable medical institution by referring to the user's past medical institution use history at the time of introduction. The introduction unit, for example, refers to the user's past medical history and suggests the most suitable medical institution. For example, the introduction unit suggests the most suitable medical institution based on the medical institution used by the user in the past. The introduction unit can also select an appropriate medical institution by considering the user's past treatment history. For example, the introduction unit refers to the user's past treatment history and suggests a highly specialized medical institution. Furthermore, the introduction unit can also select the most suitable medical institution based on the user's past health condition. For example, the introduction unit refers to the user's past health condition and suggests the most suitable medical institution. As a result, the most suitable medical institution can be selected by referring to the user's past medical institution use history. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input the user's past medical institution use history to the generation AI and cause the generation AI to select the most suitable medical institution. As a result, the most suitable medical institution can be selected by referring to the user's past medical institution use history.

[0047] The introduction unit can suggest a medical institution based on the user's lifestyle or environmental factors at the time of introduction. The introduction unit, for example, takes into account the user's smoking habits to suggest an appropriate medical institution. For example, the introduction unit refers to the user's smoking habits to suggest an appropriate medical institution. The introduction unit can also suggest a highly specialized medical institution in consideration of the user's drinking habits. For example, the introduction unit refers to the user's drinking habits to suggest a highly specialized medical institution. Furthermore, the introduction unit can also select an optimal medical institution in consideration of the user's living environment (urban area, rural area, etc.). For example, the introduction unit refers to the user's living environment to suggest an optimal medical institution. As a result, a more appropriate medical institution can be suggested by taking into account the user's lifestyle and environmental factors. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input data on the user's lifestyle and environmental factors into the generation AI and have the generation AI suggest a medical institution. As a result, a more appropriate medical institution can be suggested by taking into account the user's lifestyle and environmental factors.

[0048] The introduction unit can suggest the most suitable medical institution based on the geographical location information of the user at the time of introduction. For example, if the user lives in a specific area, the introduction unit preferentially suggests a medical institution in that area. For example, the introduction unit refers to the geographical location information of the user and preferentially suggests a medical institution in that area. In addition, if the user is traveling, the introduction unit can also suggest a medical institution at the travel destination. For example, the introduction unit refers to the geographical location information of the user and suggests a medical institution at the travel destination. In addition, if the user is close to a specific medical institution, the introduction unit can also preferentially suggest that medical institution. For example, the introduction unit refers to the geographical location information of the user and preferentially suggests a specific medical institution. In this way, the most suitable medical institution can be suggested by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input the geographical location information of the user to the generation AI and have the generation AI suggest a medical institution. In this way, the most suitable medical institution can be suggested by taking into account the geographical location information of the user.

[0049] The introduction unit can suggest a medical institution by referring to the user's social media activity at the time of introduction. For example, when the user posts about health on social media, the introduction unit suggests a medical institution based on the content of the post. For example, the introduction unit refers to the user's social media activity and suggests a medical institution based on the content of the post about health. In addition, when the user shows an interest in a specific health topic, the introduction unit can also suggest a medical institution related to the topic. For example, the introduction unit refers to the user's social media activity and suggests a medical institution related to a specific health topic. Furthermore, the introduction unit can also suggest a medical institution related to lifestyle habits from the user's social media activity. For example, the introduction unit refers to the user's social media activity and suggests a medical institution related to lifestyle habits. As a result, a more appropriate medical institution can be suggested by referring to the user's social media activity. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input the user's social media activity to the generation AI and have the generation AI suggest a medical institution. As a result, a more appropriate medical institution can be suggested by referring to the user's social media activity.

[0050] The providing unit can provide optimal information by referring to the user's past medical history when providing information. The providing unit provides relevant information based on, for example, the user's past diagnosis results. For example, the providing unit provides relevant information by referring to the user's past diagnosis results. The providing unit can also provide appropriate information by taking into account the user's past treatment history. For example, the providing unit provides appropriate information by referring to the user's past treatment history. Furthermore, the providing unit can also provide customized information based on the user's past health condition. For example, the providing unit provides customized information by referring to the user's past health condition. As a result, optimal information can be provided by referring to the user's past medical history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past medical history into the generating AI and cause the generating AI to execute information provision. As a result, optimal information can be provided by referring to the user's past medical history.

[0051] The providing unit can customize information by considering the user's lifestyle and environmental factors when providing information. The providing unit provides relevant information by considering, for example, the user's smoking habit. For example, the providing unit provides relevant information by referring to the user's smoking habit. The providing unit can also provide appropriate information by considering the user's drinking habit. For example, the providing unit provides appropriate information by referring to the user's drinking habit. Furthermore, the providing unit can also provide customized information by considering the user's living environment (urban area, rural area, etc.). For example, the providing unit provides customized information by referring to the user's living environment. As a result, more appropriate information can be provided by considering the user's lifestyle and environmental factors. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's lifestyle and environmental factors to the generating AI and cause the generating AI to execute information provision. As a result, more appropriate information can be provided by considering the user's lifestyle and environmental factors.

[0052] The providing unit can provide optimal information based on the geographical location information of the user when providing information. For example, when the user lives in a specific area, the providing unit provides information on health risks in that area. For example, the providing unit refers to the geographical location information of the user and provides information on health risks in that area. In addition, when the user is traveling, the providing unit can also provide information on health risks at the travel destination. For example, the providing unit refers to the geographical location information of the user and provides information on health risks at the travel destination. In addition, when the user is close to a specific medical institution, the providing unit can also provide information related to the medical institution. For example, the providing unit refers to the geographical location information of the user and provides information related to the specific medical institution. In this way, optimal information can be provided by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to execute information provision. In this way, optimal information can be provided by taking into account the geographical location information of the user.

[0053] The providing unit can provide information by referring to the user's social media activity when providing information. For example, when the user posts about health on social media, the providing unit provides information based on the content of the posting. For example, the providing unit refers to the user's social media activity and provides information based on the content of the posting about health. In addition, when the user shows an interest in a specific health topic, the providing unit can also provide information related to the topic. For example, the providing unit refers to the user's social media activity and provides information related to a specific health topic. Furthermore, the providing unit can also provide information related to lifestyle habits from the user's social media activity. For example, the providing unit refers to the user's social media activity and provides information related to lifestyle habits. As a result, more appropriate information can be provided by referring to the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity to the generating AI and cause the generating AI to execute information provision. As a result, more appropriate information can be provided by referring to the user's social media activity.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can not only accept the user's answer, but also provide real-time feedback based on the user's answer. For example, if the user answers "I have a headache," the reception unit can immediately provide advice such as "I recommend you drink fluids" or "It is important to take a rest." The reception unit can also suggest simple exercises and stretching methods that will help relieve symptoms based on the user's answer. Furthermore, the reception unit can provide measures to take if symptoms worsen and emergency contact information based on the information entered by the user. This allows the user to take initial action at home quickly.

[0056] The analysis unit can not only ask additional questions based on the user's answers, but also evaluate the reliability of the user's answers. For example, the analysis unit can check whether the user's answers are consistent and ask reconfirmation questions if there are any inconsistencies. The analysis unit can also compare the user's answers with past answer history and data of other users to evaluate the reliability of the user's answers. Furthermore, the analysis unit can adjust the reliability of the analysis result based on the reliability of the user's answers. This allows the analysis unit to provide a more accurate analysis result.

[0057] The referral unit can not only suggest appropriate medical institutions based on the user's symptoms, but also suggest medical institutions according to the user's individual needs. For example, if the user desires a doctor who speaks a specific language, the referral unit can suggest medical institutions that can speak that language. Also, if the user desires a specific treatment, the referral unit can suggest medical institutions that provide that treatment. Furthermore, the referral unit can take into account the user's insurance information and suggest medical institutions where treatment can be performed within the scope of insurance coverage. This allows the user to select a medical institution that best suits their needs.

[0058] The providing unit can not only automatically transfer the collected information to the electronic medical record system, but also notify the medical institution staff in real time. For example, if the user reports an emergency symptom, the providing unit can immediately send an alert to the medical institution staff. The providing unit can also notify the medical institution of the user's expected arrival time to allow time for the staff to prepare. Furthermore, the providing unit can instruct the medical institution staff to make specific preparations or responses based on the user's symptoms. This allows the medical institution to respond quickly and appropriately.

[0059] The analysis unit can detect symptoms that require urgent treatment and not only display an alert to encourage the user to seek emergency medical treatment, but also guide the user to the nearest emergency medical institution based on the user's location information. For example, if the user responds that they have "chest pain," the analysis unit can detect this as a symptom that requires urgent treatment and provide the user with the address and contact information of the nearest emergency hospital. The analysis unit can also assist in arranging the nearest ambulance based on the user's location information. Furthermore, the analysis unit can provide first aid methods based on the user's symptoms. This allows the user to take prompt and appropriate action.

[0060] The reception unit can analyze the user's past answer history and not only determine the optimal question order, but also adjust the difficulty of the questions based on the user's answer pattern. For example, the reception unit asks more detailed questions if the user has answered simple questions correctly in the past. The reception unit can also lower the difficulty of the questions if the user has not answered difficult questions sufficiently in the past. Furthermore, the reception unit can change the format of the questions based on the user's answer pattern. This allows the user to proceed with the medical interview at his or her own pace.

[0061] When asking questions, the reception unit can customize the questions based on the user's current health condition and lifestyle habits, as well as add questions based on the user's family history. For example, if the user has a particular family history of a disease, the reception unit can ask questions related to the family history. The reception unit can also add questions about genetic risks based on the user's family history. Furthermore, the reception unit can provide advice on preventive measures and health management based on the user's family history. This allows the user to receive a more comprehensive medical interview.

[0062] When asking a question, the reception unit not only prioritizes relevant questions based on the user's geographical location information, but also provides information on region-specific health risks based on the user's geographical location information. For example, if the user lives in a particular region, the reception unit provides information on diseases and infectious diseases prevalent in that region. In addition, if the user is traveling, the reception unit can provide information on health risks at the travel destination. Furthermore, the reception unit can provide information on local medical institutions and pharmacies based on the user's geographical location information. This allows the user to take measures against health risks in their own region or travel destination.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives the user's answer. The user's answer includes information such as "I have a headache," "I have a fever," and "I have a cough." For example, the user answers the question "What's wrong with you today?" at home and enters the answer into the system. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI to analyze the user's responses and understand the user's symptoms and condition. For example, if the user responds with "I have a headache," the analysis unit will ask additional questions such as "When did the pain start?" and "How intense is the pain?" to collect more detailed information. Step 3: The referral department refers the user to an appropriate medical institution based on the information analyzed by the analysis department. Using AI, the referral department recommends an appropriate medical institution based on the user's symptoms, taking into account past data and the specialty of the medical institution. For example, if the user responds with "I have a headache," the referral department will suggest an appropriate department such as "internal medicine" or "neurology." Step 4: The providing unit provides the information collected by the analysis unit to the doctor. The providing unit automatically transfers the collected information to the electronic medical record system. This allows the doctor to quickly examine the user after understanding their symptoms and condition.

[0065] (Example 2) The first consultation interview system according to the embodiment of the present invention is a system that allows a user to complete a medical interview at home in advance. This system starts when the user answers the question "What's wrong with you today?" at home and inputs the answer into the system. The system uses AI to analyze the user's answer and conducts multiple interviews to grasp the user's symptoms and condition in detail. This allows the next preparation to proceed smoothly. In addition, if the user does not have a family doctor or does not know which department to go to, the AI ​​will introduce the appropriate medical institution. However, a mechanism is built in that prevents the user from making their own judgment or dealing with the problem on their own, and the system functions only as support before seeing a doctor. For example, a user answers the question "What's wrong with you today?" at home. At this time, the user inputs details of their symptoms and condition. For example, they input information such as "I have a headache," "I have a fever," and "I have a cough." This information is entered into the system and analyzed by AI. Next, the AI ​​analyzes the input information and grasps the user's symptoms and condition. The AI ​​asks additional questions based on the user's answers and collects detailed information. For example, if the user answers "I have a headache," the AI ​​will ask additional questions such as "When did the pain start?" and "How strong is the pain?" This allows the user to understand the user's symptoms and condition in detail. Furthermore, the AI ​​can smoothly proceed with the next preparation based on the information collected. For example, when the user arrives at the medical institution, the information collected in advance is provided to the doctor. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. In addition, if the user does not have a family doctor or does not know which department to go to, the AI ​​will introduce the appropriate medical institution. For example, if the user answers "I have a headache," the AI ​​will suggest an appropriate department such as "internal medicine" or "neurology." This allows the user to select the appropriate medical institution. However, a mechanism is built in that prevents the user from making their own judgment or dealing with the situation on their own. For example, the AI ​​will provide advice such as "This symptom is highly urgent, so please visit a medical institution immediately." This allows the user to visit an appropriate medical institution and functions as support before seeing a doctor.This allows the initial consultation interview system to allow the user to complete the interview in advance at home, allowing the examination at the medical institution to proceed smoothly.

[0066] The initial consultation interview system according to the embodiment includes a reception unit, an analysis unit, a referral unit, and a provision unit. The reception unit receives a user's answer. The user's answer includes, but is not limited to, information such as "I have a headache," "I have a fever," and "I have a cough." The reception unit, for example, allows the user to answer a question such as "What's wrong with you today?" at home and inputs the answer into the system. The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI to analyze the user's answer and grasps the user's symptoms and condition. For example, the analysis unit asks additional questions based on the user's answer and collects detailed information. For example, if the user answers "I have a headache," the analysis unit asks additional questions such as "When did the pain start?" and "How strong is the pain?" This makes it possible to grasp the user's symptoms and condition in detail. The referral unit introduces an appropriate medical institution based on the information analyzed by the analysis unit. The referral unit uses AI to suggest an appropriate medical institution based on the user's symptoms and taking into account past data and the expertise of the medical institution. For example, if the user answers "I have a headache," the referral unit suggests an appropriate department, such as "internal medicine" or "neurology." This allows the user to select an appropriate medical institution. The providing unit provides the information collected by the analyzing unit to the doctor. The providing unit automatically transfers the collected information to the electronic medical record system. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. For example, the providing unit automatically transfers the collected information to the electronic medical record system, allowing the doctor to quickly examine the user. This allows the initial consultation interview system according to the embodiment to accept and analyze the user's answers, introduce an appropriate medical institution, and provide the information to the doctor, allowing the initial consultation interview to be completed at home in advance.

[0067] The reception unit receives the user's answer. The user's answer includes, but is not limited to, information such as "I have a headache," "I have a fever," and "I have a cough." For example, the reception unit allows the user to answer the question "How are you feeling today?" at home and inputs the answer into the system. Specifically, the reception unit provides a mechanism for the user to use a smartphone or a PC to access a dedicated application or website and input the answer into a medical questionnaire. The user can input symptoms in multiple choice format or free description format, which allows the user to accurately convey detailed symptoms and conditions. Furthermore, the reception unit has a function of checking the user's input content in real time and automatically presenting additional questions if there is missing information or unclear points. For example, for a user who answers "I have a headache," detailed questions such as "Where is the pain?" and "How often do you experience pain?" are asked to collect more specific information. Furthermore, the reception unit safely manages the user's answer data and protects the data using encryption technology to protect privacy. This allows the user to report their symptoms with peace of mind. Furthermore, the reception unit has multilingual support and a voice input function, and can accommodate a variety of users, including users who speak different languages, visually impaired people, etc. This enables the reception unit to flexibly respond to the diverse needs of users, and can improve the efficiency and accuracy of initial consultation interviews.

[0068] The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI to analyze the user's answers and understand the user's symptoms and condition. Specifically, the analysis unit uses natural language processing technology to perform text analysis of the user's answers and extracts symptom keywords and related information. For example, if the user answers "I have a headache," the analysis unit extracts the keyword "headache" and generates additional questions such as "When did the pain start?" and "How strong is the pain?" This allows the user's symptoms and condition to be understood in detail. The analysis unit analyzes the pattern and association of symptoms based on the user's answer data and predicts possible diagnosis results. For example, if headache symptoms occur frequently and the pain intensity is increasing, the analysis unit suggests the possibility of migraine or tension headache. In addition, the analysis unit refers to past data and medical literature to provide information for making a more accurate diagnosis based on the combination of symptoms and frequency of occurrence. In addition, the analysis unit anonymizes the user's answer data and performs statistical analysis to understand trends and patterns related to specific symptoms and conditions. This allows the analysis unit to contribute not only to the symptoms of individual users, but also to understanding health conditions at a group level and planning preventive measures. The analysis unit can continuously improve the accuracy of the analysis using AI learning algorithms. For example, every time new data on symptoms or conditions is added, the AI ​​model is retrained to improve the accuracy of the analysis. This allows the analysis unit to always provide highly accurate analysis based on the latest information and support the user's health management.

[0069] The referral unit introduces an appropriate medical institution based on the information analyzed by the analysis unit. The referral unit uses AI to suggest an appropriate medical institution based on the user's symptoms, taking into account past data and the specialty of the medical institution. Specifically, an algorithm is used to select the most suitable medical department and medical institution according to the user's symptoms and condition. For example, if the user answers "I have a headache," the referral unit not only suggests an appropriate department such as "internal medicine" or "neurology," but also selects the most suitable medical institution by comprehensively considering past medical data, the specialty of the medical institution, the user's place of residence, and ease of access. The referral unit also refers to the evaluation and word-of-mouth information of the medical institution to provide the user with reliable information. For example, based on the evaluation and word-of-mouth of past patients, it evaluates the medical quality and response of the medical institution and presents the user with appropriate options. In addition, the referral unit considers the user's wishes and constraints, and suggests a medical institution taking into account the ease of making an appointment and the flexibility of consultation hours. This allows the user to select the medical institution that best suits his or her needs. In addition, the referral unit also provides support for the user to make an appointment at the proposed medical institution. For example, the online reservation system allows users to easily make reservations and also provides functions such as reservation confirmation and reminder notifications, etc. This allows the referral department to support users so that they can smoothly visit medical institutions and streamline the initial consultation process.

[0070] The providing unit provides the information collected by the analysis unit to the doctor. The providing unit automatically transfers the collected information to the electronic medical record system. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. Specifically, the providing unit integrates the user's answer data and analysis results into the electronic medical record system, allowing the doctor to check the necessary information before the examination. For example, if the user answers "I have a headache" and the analysis unit analyzes that "it is highly likely to be a migraine," the information is automatically recorded in the electronic medical record and the doctor can refer to it during the examination. This allows the doctor to understand the user's symptoms and condition in advance and perform a prompt and appropriate examination. Furthermore, the providing unit provides information that is useful for follow-up after the examination and for planning a treatment plan. For example, the information on the examination results and prescribed medicines is recorded in the electronic medical record, so that it can be referred to at the next examination. In addition, the providing unit centrally manages the user's examination history and treatment progress, and supports the doctor to perform examinations and treatments based on past information. This allows the providing unit to facilitate information sharing between the doctor and the user, improving the efficiency and accuracy of the examination. Furthermore, the provider takes into consideration the protection of user privacy and ensures the security of information by encrypting data and controlling access, which allows the provider to improve the quality of medical care while gaining the trust of users.

[0071] The analysis unit can ask additional questions based on the user's answer to collect detailed information. The detailed information includes, for example, details of symptoms, past medical history, etc., but is not limited to such examples. The analysis unit, for example, asks additional questions based on the user's answer to collect detailed information. For example, if the user answers "I have a headache," the analysis unit asks additional questions such as "When did the pain start?" and "How strong is the pain?". The analysis unit can also ask questions about the past medical history and current health condition based on the user's answer. For example, if the user answers "I have a fever," the analysis unit asks additional questions such as "Since when have you had a fever?" and "What other symptoms do you have?". This allows the analysis unit to grasp the user's symptoms and condition in detail. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the user's answer to the generation AI and causes the generation AI to execute additional questions. This allows detailed information to be collected by asking additional questions based on the user's answer.

[0072] The referral unit can suggest an appropriate medical institution based on past data or the specialty of the medical institution based on the user's symptoms. Past data includes, for example, past medical records, statistical data, etc., but is not limited to such examples. The specialty of the medical institution includes, for example, the number of specialists, treatment results, etc., but is not limited to such examples. The referral unit, for example, refers to past data based on the user's symptoms and suggests an appropriate medical institution. For example, if the user answers "I have a headache," the referral unit refers to past medical records and suggests an appropriate department such as "internal medicine" or "neurology." The referral unit can also suggest an appropriate medical institution based on the user's symptoms, taking into account the specialty of the medical institution. For example, if the user answers "I have a fever," the referral unit suggests a medical institution with a high treatment record. This allows the user to select an appropriate medical institution. A part or all of the above-mentioned processing in the referral unit may be performed, for example, using AI, or may be performed without using AI. For example, the referral unit can input the user's symptoms into the generation AI and cause the generation AI to execute the suggestion of an appropriate medical institution. This allows the user to select an appropriate medical institution by suggesting an appropriate medical institution based on the user's symptoms.

[0073] The providing unit can automatically transfer the collected information to the electronic medical record system. The method of automatic transfer includes, for example, API linkage, data format, and the like, but is not limited to such examples. The providing unit, for example, automatically transfers the collected information to the electronic medical record system. For example, the providing unit transfers the collected information to the electronic medical record system using API linkage. The providing unit can also transfer information using a specific data format. For example, the providing unit transfers information using the HL7 (Health Level 7) format. This allows the doctor to quickly examine the user after understanding the user's symptoms and condition. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected information to the generating AI and cause the generating AI to execute transfer to the electronic medical record system. This allows the doctor to quickly examine the user by automatically transferring the collected information to the electronic medical record system.

[0074] The analysis unit can detect symptoms that require urgent treatment and display an alert to encourage the user to seek emergency medical treatment. Examples of symptoms that require urgent treatment include, but are not limited to, a specific symptom list, an algorithm, and the like. The analysis unit detects symptoms that require urgent treatment based on, for example, a user's answer. For example, when the user answers "my chest hurts," the analysis unit detects the symptom as an urgent symptom and displays an alert to encourage the user to seek emergency medical treatment. The analysis unit can also detect symptoms that require urgent treatment using a specific algorithm. For example, the analysis unit uses AI to analyze the user's answer and detects symptoms that require urgent treatment. This allows the user to seek medical treatment at an appropriate time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's answer to the generation AI and cause the generation AI to detect symptoms that require urgent treatment. This allows the user to seek medical treatment at an appropriate time by detecting symptoms that require urgent treatment and encouraging the user to seek emergency medical treatment.

[0075] The reception unit can estimate the user's emotion and adjust the way of expressing the question based on the estimated emotion of the user. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in the facial expression and adjusts the way of expressing the question. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the way of expressing the question. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the way of expressing the question. This makes it possible to give the user a sense of security by adjusting the way of expressing the question based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions. This can provide the user with a sense of security by adjusting the way the question is phrased based on the user's emotions.

[0076] The reception unit can analyze the user's past answer history and determine the optimal order of questions. The reception unit, for example, analyzes the user's past answer history and determines the optimal order of questions. For example, the reception unit preferentially displays questions that the user has frequently answered in the past. The reception unit can also automatically select highly relevant questions from the user's past answer history. Furthermore, the reception unit can analyze the user's answer pattern and propose an efficient order of questions. For example, the reception unit determines the optimal order of questions based on the user's past answer pattern. As a result, an efficient order of questions can be determined by analyzing the user's past answer history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's past answer history to the generation AI and cause the generation AI to execute the determination of the optimal order of questions. As a result, an efficient order of questions can be determined by analyzing the user's past answer history.

[0077] The reception unit can customize the content of the question based on the user's current health condition and lifestyle habits when asking a question. The reception unit customizes the content of the question based on the user's current health condition and lifestyle habits, for example. For example, the reception unit adds related questions based on the user's existing medical history. The reception unit can also ask specific questions based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can select appropriate questions based on the user's current health condition (fatigue, stress, etc.). For example, the reception unit customizes the content of the question based on the user's health condition and lifestyle habits. As a result, by customizing the content of the question based on the user's current health condition and lifestyle habits, more appropriate questions can be asked. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit inputs data on the user's health condition and lifestyle habits into the generation AI, and causes the generation AI to customize the content of the question. As a result, by customizing the content of the question based on the user's current health condition and lifestyle habits, more appropriate questions can be asked.

[0078] The reception unit can estimate the user's emotion and adjust the timing of the question based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression and adjusts the timing of the question. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of the question. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the timing of the question. This makes it possible to give the user a sense of security by adjusting the timing of the question based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI execute emotion estimation. This allows the timing of questions to be adjusted based on the user's emotions, giving the user a sense of security.

[0079] When asking a question, the reception unit can prioritize questions with high relevance based on the geographical location information of the user. The reception unit, for example, prioritizes questions with high relevance taking into account the geographical location information of the user. For example, if the user lives in a specific area, the reception unit asks questions based on health risks associated with the area. In addition, if the user is traveling, the reception unit can also ask questions based on health risks at the travel destination. Furthermore, if the user is close to a specific medical institution, the reception unit can also ask questions based on the expertise of the medical institution. For example, the reception unit prioritizes questions with high relevance based on the geographical location information of the user. As a result, by taking into account the geographical location information of the user, questions with high relevance can be prioritized. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit inputs the geographical location information of the user to the generation AI and causes the generation AI to select questions with high relevance. As a result, by taking into account the geographical location information of the user, questions with high relevance can be prioritized.

[0080] The reception unit can analyze the social media activity of the user and add related questions when asking a question. The reception unit, for example, analyzes the social media activity of the user and adds related questions. For example, when the user posts about health on social media, the reception unit asks a question based on the content of the post. In addition, when the user shows an interest in a specific health topic, the reception unit can ask a question related to the topic. Furthermore, the reception unit can add questions about lifestyle habits from the social media activity of the user. For example, the reception unit analyzes the social media activity of the user and adds related questions. As a result, related questions can be added by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit inputs the social media activity of the user to the generation AI and causes the generation AI to execute the addition of related questions. As a result, related questions can be added by analyzing the social media activity of the user.

[0081] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the accuracy of the analysis. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the accuracy of the analysis. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the accuracy of the analysis. As a result, by adjusting the accuracy of the analysis based on the user's emotion, it is possible to provide a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data to the generation AI and have the generation AI execute emotion estimation. This allows the accuracy of analysis to be adjusted based on the user's emotion, thereby providing more appropriate analysis results.

[0082] The analysis unit can optimize the analysis algorithm by referring to the user's past medical history during the analysis. The analysis unit adjusts the analysis algorithm, for example, based on the user's past diagnosis results. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past diagnosis results. The analysis unit can also optimize the analysis result by taking into account the user's past treatment history. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past treatment history. Furthermore, the analysis unit can customize the analysis algorithm based on the user's past health condition. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past health condition. As a result, the analysis algorithm can be optimized by referring to the user's past medical history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the user's past medical history into the generation AI and causes the generation AI to execute optimization of the analysis algorithm. As a result, the analysis algorithm can be optimized by referring to the user's past medical history.

[0083] The analysis unit can perform the analysis based on the user's lifestyle or environmental factors at the time of analysis. The analysis unit adjusts the analysis result, for example, taking into account the user's smoking habits. For example, the analysis unit optimizes the analysis algorithm by referring to the user's smoking habits. The analysis unit can also optimize the analysis algorithm by considering the user's drinking habits. For example, the analysis unit adjusts the analysis algorithm by referring to the user's drinking habits. Furthermore, the analysis unit can customize the analysis result by considering the user's living environment (urban area, rural area, etc.). For example, the analysis unit optimizes the analysis algorithm by referring to the user's living environment. As a result, a more appropriate analysis result can be provided by taking into account the user's lifestyle and environmental factors. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the user's lifestyle and environmental factors to the generation AI and cause the generation AI to execute the analysis. As a result, a more appropriate analysis result can be provided by taking into account the user's lifestyle and environmental factors.

[0084] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the display method of the analysis result. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the analysis result. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the display method of the analysis result. This allows the user to feel at ease by adjusting the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions. This can provide the user with a sense of security by adjusting the display method of the analysis result based on the user's emotions.

[0085] The analysis unit can perform the analysis based on the geographical location information of the user at the time of analysis. For example, when the user lives in a specific area, the analysis unit performs the analysis taking into account the health risks of the area. For example, the analysis unit refers to the geographical location information of the user and performs the analysis taking into account the health risks of the area. In addition, when the user is traveling, the analysis unit can also perform the analysis taking into account the health risks of the travel destination. For example, the analysis unit refers to the geographical location information of the user and performs the analysis taking into account the health risks of the travel destination. In addition, when the user is close to a specific medical institution, the analysis unit can also perform the analysis taking into account the expertise of the medical institution. For example, the analysis unit refers to the geographical location information of the user and performs the analysis taking into account the expertise of the specific medical institution. As a result, by taking into account the geographical location information of the user, a more appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the analysis. As a result, by taking into account the geographical location information of the user, a more appropriate analysis result can be provided.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during the analysis. For example, when the user posts on health on social media, the analysis unit reflects the content in the analysis. For example, the analysis unit refers to the user's social media activity and reflects the content of the health post in the analysis. In addition, when the user shows an interest in a specific health topic, the analysis unit can also reflect information related to the topic in the analysis. For example, the analysis unit refers to the user's social media activity and reflects information related to the specific health topic in the analysis. In addition, the analysis unit can also reflect information on lifestyle habits from the user's social media activity in the analysis. For example, the analysis unit refers to the user's social media activity and reflects information on lifestyle habits in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity to the generation AI and cause the generation AI to execute the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's social media activity.

[0087] The introduction unit can estimate the user's emotion and adjust the way of expressing the introduction based on the estimated emotion of the user. For example, the introduction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the introduction unit calculates an emotion score based on the change in the facial expression and adjusts the way of expressing the introduction. The introduction unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the introduction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the way of expressing the introduction. Furthermore, the introduction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the introduction unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the way of expressing the introduction. This allows the user to feel at ease by adjusting the way of expressing the introduction based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions. This allows the user to feel at ease by adjusting the way the introduction is expressed based on the user's emotions.

[0088] The introduction unit can select the most suitable medical institution by referring to the user's past medical institution use history at the time of introduction. The introduction unit, for example, refers to the user's past medical history and suggests the most suitable medical institution. For example, the introduction unit suggests the most suitable medical institution based on the medical institution used by the user in the past. The introduction unit can also select an appropriate medical institution by considering the user's past treatment history. For example, the introduction unit refers to the user's past treatment history and suggests a highly specialized medical institution. Furthermore, the introduction unit can also select the most suitable medical institution based on the user's past health condition. For example, the introduction unit refers to the user's past health condition and suggests the most suitable medical institution. As a result, the most suitable medical institution can be selected by referring to the user's past medical institution use history. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input the user's past medical institution use history to the generation AI and cause the generation AI to select the most suitable medical institution. As a result, the most suitable medical institution can be selected by referring to the user's past medical institution use history.

[0089] The introduction unit can suggest a medical institution based on the user's lifestyle or environmental factors at the time of introduction. The introduction unit, for example, takes into account the user's smoking habits to suggest an appropriate medical institution. For example, the introduction unit refers to the user's smoking habits to suggest an appropriate medical institution. The introduction unit can also suggest a highly specialized medical institution in consideration of the user's drinking habits. For example, the introduction unit refers to the user's drinking habits to suggest a highly specialized medical institution. Furthermore, the introduction unit can also select an optimal medical institution in consideration of the user's living environment (urban area, rural area, etc.). For example, the introduction unit refers to the user's living environment to suggest an optimal medical institution. As a result, a more appropriate medical institution can be suggested by taking into account the user's lifestyle and environmental factors. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input data on the user's lifestyle and environmental factors into the generation AI and have the generation AI suggest a medical institution. As a result, a more appropriate medical institution can be suggested by taking into account the user's lifestyle and environmental factors.

[0090] The introduction unit can estimate the user's emotion and adjust the timing of the introduction based on the estimated user's emotion. For example, the introduction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the introduction unit calculates an emotion score based on the change in facial expression and adjusts the timing of the introduction. The introduction unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the introduction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of the introduction. Furthermore, the introduction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the introduction unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the timing of the introduction. This allows the user to feel at ease by adjusting the timing of the introduction based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion. This allows the timing of the introduction to be adjusted based on the user's emotion, giving the user a sense of security.

[0091] The introduction unit can suggest the most suitable medical institution based on the geographical location information of the user at the time of introduction. For example, if the user lives in a specific area, the introduction unit preferentially suggests a medical institution in that area. For example, the introduction unit refers to the geographical location information of the user and preferentially suggests a medical institution in that area. In addition, if the user is traveling, the introduction unit can also suggest a medical institution at the travel destination. For example, the introduction unit refers to the geographical location information of the user and suggests a medical institution at the travel destination. In addition, if the user is close to a specific medical institution, the introduction unit can also preferentially suggest that medical institution. For example, the introduction unit refers to the geographical location information of the user and preferentially suggests a specific medical institution. In this way, the most suitable medical institution can be suggested by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input the geographical location information of the user to the generation AI and have the generation AI suggest a medical institution. In this way, the most suitable medical institution can be suggested by taking into account the geographical location information of the user.

[0092] The introduction unit can suggest a medical institution by referring to the user's social media activity at the time of introduction. For example, when the user posts about health on social media, the introduction unit suggests a medical institution based on the content of the post. For example, the introduction unit refers to the user's social media activity and suggests a medical institution based on the content of the post about health. In addition, when the user shows an interest in a specific health topic, the introduction unit can also suggest a medical institution related to the topic. For example, the introduction unit refers to the user's social media activity and suggests a medical institution related to a specific health topic. Furthermore, the introduction unit can also suggest a medical institution related to lifestyle habits from the user's social media activity. For example, the introduction unit refers to the user's social media activity and suggests a medical institution related to lifestyle habits. As a result, a more appropriate medical institution can be suggested by referring to the user's social media activity. A part or all of the above-mentioned processing in the introduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the introduction unit can input the user's social media activity to the generation AI and have the generation AI suggest a medical institution. As a result, a more appropriate medical institution can be suggested by referring to the user's social media activity.

[0093] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in facial expression and adjusts the method of providing information. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method of providing information. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the method of providing information. This makes it possible to give the user a sense of security by adjusting the method of providing information based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's facial expression data to the generating AI and cause the generating AI to estimate emotions. This can provide the user with a sense of security by adjusting the method of providing information based on the user's emotions.

[0094] The providing unit can provide optimal information by referring to the user's past medical history when providing information. The providing unit provides relevant information based on, for example, the user's past diagnosis results. For example, the providing unit provides relevant information by referring to the user's past diagnosis results. The providing unit can also provide appropriate information by taking into account the user's past treatment history. For example, the providing unit provides appropriate information by referring to the user's past treatment history. Furthermore, the providing unit can also provide customized information based on the user's past health condition. For example, the providing unit provides customized information by referring to the user's past health condition. As a result, optimal information can be provided by referring to the user's past medical history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past medical history into the generating AI and cause the generating AI to execute information provision. As a result, optimal information can be provided by referring to the user's past medical history.

[0095] The providing unit can customize information by considering the user's lifestyle and environmental factors when providing information. The providing unit provides relevant information by considering, for example, the user's smoking habit. For example, the providing unit provides relevant information by referring to the user's smoking habit. The providing unit can also provide appropriate information by considering the user's drinking habit. For example, the providing unit provides appropriate information by referring to the user's drinking habit. Furthermore, the providing unit can also provide customized information by considering the user's living environment (urban area, rural area, etc.). For example, the providing unit provides customized information by referring to the user's living environment. As a result, more appropriate information can be provided by considering the user's lifestyle and environmental factors. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's lifestyle and environmental factors to the generating AI and cause the generating AI to execute information provision. As a result, more appropriate information can be provided by considering the user's lifestyle and environmental factors.

[0096] The providing unit can estimate the user's emotion and adjust the timing of providing information based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in facial expression and adjusts the timing of providing information. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of providing information. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the timing of providing information. This allows the user to feel at ease by adjusting the timing of providing information based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's facial expression data to the generating AI and cause the generating AI to estimate emotions. This can provide the user with a sense of security by adjusting the timing of providing information based on the user's emotions.

[0097] The providing unit can provide optimal information based on the geographical location information of the user when providing information. For example, when the user lives in a specific area, the providing unit provides information on health risks in that area. For example, the providing unit refers to the geographical location information of the user and provides information on health risks in that area. In addition, when the user is traveling, the providing unit can also provide information on health risks at the travel destination. For example, the providing unit refers to the geographical location information of the user and provides information on health risks at the travel destination. In addition, when the user is close to a specific medical institution, the providing unit can also provide information related to the medical institution. For example, the providing unit refers to the geographical location information of the user and provides information related to the specific medical institution. In this way, optimal information can be provided by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to execute information provision. In this way, optimal information can be provided by taking into account the geographical location information of the user.

[0098] The providing unit can provide information by referring to the user's social media activity when providing information. For example, when the user posts about health on social media, the providing unit provides information based on the content of the posting. For example, the providing unit refers to the user's social media activity and provides information based on the content of the posting about health. In addition, when the user shows an interest in a specific health topic, the providing unit can also provide information related to the topic. For example, the providing unit refers to the user's social media activity and provides information related to a specific health topic. Furthermore, the providing unit can also provide information related to lifestyle habits from the user's social media activity. For example, the providing unit refers to the user's social media activity and provides information related to lifestyle habits. As a result, more appropriate information can be provided by referring to the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity to the generating AI and cause the generating AI to execute information provision. As a result, more appropriate information can be provided by referring to the user's social media activity.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The reception unit can not only accept the user's answer, but also provide real-time feedback based on the user's answer. For example, if the user answers "I have a headache," the reception unit can immediately provide advice such as "I recommend you drink fluids" or "It is important to take a rest." The reception unit can also suggest simple exercises and stretching methods that will help relieve symptoms based on the user's answer. Furthermore, the reception unit can provide measures to take if symptoms worsen and emergency contact information based on the information entered by the user. This allows the user to take initial action at home quickly.

[0101] The analysis unit can not only ask additional questions based on the user's answers, but also evaluate the reliability of the user's answers. For example, the analysis unit can check whether the user's answers are consistent and ask reconfirmation questions if there are any inconsistencies. The analysis unit can also compare the user's answers with past answer history and data of other users to evaluate the reliability of the user's answers. Furthermore, the analysis unit can adjust the reliability of the analysis result based on the reliability of the user's answers. This allows the analysis unit to provide a more accurate analysis result.

[0102] The referral unit can not only suggest appropriate medical institutions based on the user's symptoms, but also suggest medical institutions according to the user's individual needs. For example, if the user desires a doctor who speaks a specific language, the referral unit can suggest medical institutions that can speak that language. Also, if the user desires a specific treatment, the referral unit can suggest medical institutions that provide that treatment. Furthermore, the referral unit can take into account the user's insurance information and suggest medical institutions where treatment can be performed within the scope of insurance coverage. This allows the user to select a medical institution that best suits their needs.

[0103] The providing unit can not only automatically transfer the collected information to the electronic medical record system, but also notify the medical institution staff in real time. For example, if the user reports an emergency symptom, the providing unit can immediately send an alert to the medical institution staff. The providing unit can also notify the medical institution of the user's expected arrival time to allow time for the staff to prepare. Furthermore, the providing unit can instruct the medical institution staff to make specific preparations or responses based on the user's symptoms. This allows the medical institution to respond quickly and appropriately.

[0104] The analysis unit can detect symptoms that require urgent treatment and not only display an alert to encourage the user to seek emergency medical treatment, but also guide the user to the nearest emergency medical institution based on the user's location information. For example, if the user responds that they have "chest pain," the analysis unit can detect this as a symptom that requires urgent treatment and provide the user with the address and contact information of the nearest emergency hospital. The analysis unit can also assist in arranging the nearest ambulance based on the user's location information. Furthermore, the analysis unit can provide first aid methods based on the user's symptoms. This allows the user to take prompt and appropriate action.

[0105] The reception unit can estimate the user's emotions and adjust the way questions are asked based on the estimated user's emotions, and can also suggest relaxation methods according to the user's emotions. For example, if the user feels stressed, the reception unit can suggest deep breathing or meditation methods. Also, if the user feels anxious, the reception unit can provide relaxing music or images. Furthermore, the reception unit can provide advice on creating an environment in which the user can relax. This allows the user to receive the medical interview with peace of mind.

[0106] The reception unit can analyze the user's past answer history and not only determine the optimal question order, but also adjust the difficulty of the questions based on the user's answer pattern. For example, the reception unit asks more detailed questions if the user has answered simple questions correctly in the past. The reception unit can also lower the difficulty of the questions if the user has not answered difficult questions sufficiently in the past. Furthermore, the reception unit can change the format of the questions based on the user's answer pattern. This allows the user to proceed with the medical interview at his or her own pace.

[0107] When asking questions, the reception unit can customize the questions based on the user's current health condition and lifestyle habits, as well as add questions based on the user's family history. For example, if the user has a particular family history of a disease, the reception unit can ask questions related to the family history. The reception unit can also add questions about genetic risks based on the user's family history. Furthermore, the reception unit can provide advice on preventive measures and health management based on the user's family history. This allows the user to receive a more comprehensive medical interview.

[0108] The reception unit can estimate the user's emotions and adjust the timing of questions based on the estimated user's emotions, as well as display support messages according to the user's emotions. For example, if the user feels anxious, the reception unit can display a message such as "It's okay, please take your time to answer." If the user feels stressed, the reception unit can also display a message such as "Relax and take a deep breath." Furthermore, the reception unit can take short breaks between questions to allow the user to relax. This allows the user to receive the medical interview with peace of mind.

[0109] When asking a question, the reception unit not only prioritizes relevant questions based on the user's geographical location information, but also provides information on region-specific health risks based on the user's geographical location information. For example, if the user lives in a particular region, the reception unit provides information on diseases and infectious diseases prevalent in that region. In addition, if the user is traveling, the reception unit can provide information on health risks at the travel destination. Furthermore, the reception unit can provide information on local medical institutions and pharmacies based on the user's geographical location information. This allows the user to take measures against health risks in their own region or travel destination.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The reception unit receives the user's answer. The user's answer includes information such as "I have a headache," "I have a fever," and "I have a cough." For example, the user answers the question "What's wrong with you today?" at home and enters the answer into the system. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI to analyze the user's responses and understand the user's symptoms and condition. For example, if the user responds with "I have a headache," the analysis unit will ask additional questions such as "When did the pain start?" and "How intense is the pain?" to collect more detailed information. Step 3: The referral department refers the user to an appropriate medical institution based on the information analyzed by the analysis department. Using AI, the referral department recommends an appropriate medical institution based on the user's symptoms, taking into account past data and the specialty of the medical institution. For example, if the user responds with "I have a headache," the referral department will suggest an appropriate department such as "internal medicine" or "neurology." Step 4: The providing unit provides the information collected by the analysis unit to the doctor. The providing unit automatically transfers the collected information to the electronic medical record system. This allows the doctor to quickly examine the user after understanding their symptoms and condition.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the above-mentioned reception unit, analysis unit, introduction unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an appropriate medical institution. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the collected information to a doctor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned reception unit, analysis unit, introduction unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an appropriate medical institution. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the collected information to a doctor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned reception unit, analysis unit, introduction unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an appropriate medical institution. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the collected information to a doctor. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] Each of the multiple elements including the reception unit, analysis unit, introduction unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an appropriate medical institution. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the collected information to a doctor. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) a reception unit for receiving an answer from a user; an analysis unit that analyzes the information received by the reception unit; A referral unit that introduces an appropriate medical institution based on the information analyzed by the analysis unit; A providing unit that provides the information collected by the analysis unit to a doctor. A system characterized by: (Appendix 2) The analysis unit is Ask follow-up questions based on the user's answers to gather more information 2. The system of claim 1, (Appendix 3) The introduction unit is Based on the user's symptoms, the system recommends appropriate medical institutions based on past data or the expertise of medical institutions. 2. The system of claim 1, (Appendix 4) The providing unit is Automatically transfer collected information to electronic medical record system 2. The system of claim 1, (Appendix 5) The analysis unit is Detects emergency symptoms and displays an alert to encourage users to seek emergency medical attention 2. The system of claim 1, (Appendix 6) The reception unit is Infer user sentiment and adjust question wording based on the inferred sentiment 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past answer history to determine the optimal question order 2. The system of claim 1, (Appendix 8) The reception unit is Customize questions based on the user's current health and lifestyle 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and adjust the timing of questions based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The reception unit is When asking questions, prioritize the most relevant questions based on the user's geographic location 2. The system of claim 1, (Appendix 11) The reception unit is When asking questions, analyze the user's social media activity and add related questions 2. The system of claim 1, (Appendix 12) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the analysis algorithm is optimized by referring to the user's past medical history. 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing, the analysis is based on the user's lifestyle or environmental factors. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is At analysis time, the analysis is based on the user's geographic location. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, we refer to users' social media activities to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 18) The introduction unit is To estimate a user's emotion and adjust a presentation method of an introduction based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The introduction unit is When making a referral, the system selects the most suitable medical institution by referring to the user's past medical institution usage history. 2. The system of claim 1, (Appendix 20) The introduction unit is When making a referral, suggest medical institutions based on the user's lifestyle or environmental factors 2. The system of claim 1, (Appendix 21) The introduction unit is To estimate a user's emotion and adjust the timing of an introduction based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The introduction unit is When making a referral, suggest the most suitable medical institution based on the user's geographic location 2. The system of claim 1, (Appendix 23) The introduction unit is When making a referral, suggest medical institutions based on the user's social media activity 2. The system of claim 1, (Appendix 24) The providing unit is Estimate the user's emotions and adjust the way information is provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The providing unit is When providing information, the system provides optimal information by referring to the user's past medical history. 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, the information is customized taking into account the user's lifestyle and environmental factors. 2. The system of claim 1, (Appendix 27) The providing unit is Estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions 2. The system of claim 1, (Appendix 28) The providing unit is When providing information, provide the most appropriate information based on the user's geographic location 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, provide information by referring to the user's social media activity 2. The system of claim 1, [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving an answer from a user; an analysis unit that analyzes the information received by the reception unit; A referral unit that introduces an appropriate medical institution based on the information analyzed by the analysis unit; A providing unit that provides the information collected by the analysis unit to a doctor. A system characterized by:

2. The analysis unit is Ask follow-up questions based on the user's answers to gather more information 2. The system of claim 1 .

3. The introduction unit is Based on the user's symptoms, the system recommends appropriate medical institutions based on past data or the expertise of medical institutions.

2. The system of claim 1 .

4. The providing unit is Automatically transfer collected information to electronic medical record system 2. The system of claim 1 .

5. The analysis unit is Detects emergency symptoms and displays an alert to encourage users to seek emergency medical attention 2. The system of claim 1 .

6. The reception unit is Infer user sentiment and adjust question wording based on the inferred sentiment 2. The system of claim 1 .

7. The reception unit is Analyze the user's past answer history to determine the optimal question order 2. The system of claim 1 .

8. The reception unit is Customize questions based on the user's current health and lifestyle 2. The system of claim 1 .

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