System

Abstract

An object of a system according to an embodiment is to promote early detection of a disease at home.SOLUTION: A system includes an inquiry unit, an analysis unit, and an advice unit. The inquiry unit hears an official inquiry on a conversation basis using the generated AI. The analysis unit analyzes the data collected by the medical inquiry unit. The advice unit provides advice to the user on the basis of the result analyzed by the analysis unit.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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently detect diseases early at home.

[0005] The system according to the embodiment aims to promote early detection of diseases in the home. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical interview unit, an analysis unit, and an advice unit. The medical interview unit uses a generation AI to conduct a conversation-based formal medical interview. The analysis unit analyzes the data collected by the medical interview unit. The advice unit provides advice to the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate early detection of diseases in the home. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. 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 process 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The home medical interview tool according to an embodiment of the present invention is a system that uses generative AI to conduct conversation-based interviews with official medical interviews and promotes early detection of diseases at home. As a result, the home medical interview tool promotes early detection and treatment of diseases at home, and can resolve issues such as an aging population, a shortage of medical personnel, and rising social security costs.

[0029] The home medical interview tool according to the embodiment includes a medical interview unit, an analysis unit, and an advice unit. The medical interview unit uses a generation AI to conduct a conversation-based formal medical interview. For example, the generation AI asks the user questions such as, "Have you noticed any changes in your physical condition recently?" or "Are you experiencing pain or discomfort in a specific area?" The generation AI then asks more detailed questions based on the user's answers to identify changes in symptoms and physical condition. The generation AI receives input in the form of prompts containing instructions from the user regarding what the user wants the generation AI to do, and the generation AI proceeds with the medical interview based on the prompts. The analysis unit analyzes the data collected by the medical interview unit. For example, the generation AI analyzes the user's answers to identify specific symptoms and risk factors. The analysis unit also uses an algorithm to assess disease risk based on the user's answers. The advice unit provides advice to the user based on the results of the analysis by the analysis unit. For example, the generation AI may advise the user, "You should see a doctor as soon as possible about these symptoms." The advice unit also provides specific advice for maintaining health based on the user's lifestyle and health condition. As a result, the home medical interview tool according to the embodiment enables early detection of illnesses and appropriate advice at home. For example, a user can easily take a medical interview at home and obtain information to determine whether or not to visit a medical institution. In addition, the generation AI continuously monitors the user's health condition and can respond early if an abnormality is detected.

[0030] The interview unit can analyze the user's voice tone and speaking patterns to detect signs of stress or anxiety. For example, the generation AI in the interview unit analyzes the user's voice tone and speaking patterns in real time to detect signs of stress or anxiety. For example, it analyzes the pitch, speed, and pauses of the voice to assess the stress level. The interview unit also collects voice data when the user speaks, and the generation AI analyzes that data to identify signs of stress or anxiety. For example, it detects voice fluctuations and unnatural pauses. The interview unit also uses an algorithm to analyze the user's voice tone and speaking patterns to detect signs of stress or anxiety. For example, it analyzes the frequency and rhythm of the voice to assess the stress level. This enables the generation AI to detect signs of stress or anxiety in the user and take appropriate measures.

[0031] The medical interview unit can refer to the user's past medical interview data and generate customized questions based on the user's health history. In the medical interview unit, for example, the generation AI refers to the user's past medical interview data and generates customized questions based on the user's individual health history. For example, the questions are adjusted based on previously reported symptoms and medical history. The medical interview unit also analyzes the user's past medical interview data and the generation AI generates customized questions based on that data. For example, questions related to a high risk of a particular disease are prioritized. In the medical interview unit, the generation AI refers to the user's health history and generates questions tailored to individual needs. For example, more detailed questions are asked based on past diagnosis results and treatment history. This makes it possible to generate customized questions based on the user's individual health history.

[0032] The analysis unit can provide health advice based on environmental factors, based on the user's living environment. For example, the generation AI collects the user's living environment data (temperature and humidity) and provides health advice based on that data. For example, if the humidity is low, it recommends using a humidifier. The analysis unit also monitors the temperature and humidity in the user's home, and the generation AI provides health advice based on that data. For example, if the temperature is high, it recommends appropriate hydration. The analysis unit also analyzes the user's living environment data and provides health advice based on environmental factors. For example, if the humidity is high, it suggests measures to prevent mold growth. This makes it possible to provide health advice based on the user's living environment.

[0033] The medical interview unit can generate questions to support the health management of the entire family, taking into account the user's family composition and lifestyle habits. In the medical interview unit, for example, the generation AI takes into account the user's family composition and lifestyle habits and generates questions to support the health management of the entire family. For example, it asks questions about the eating habits and exercise habits of all family members. In addition, the medical interview unit collects data on the user's family composition and lifestyle habits, and the generation AI generates questions to support the health management of the entire family based on that data. For example, it asks questions to assess the health risks of all family members. In addition, the medical interview unit generates questions to support the health management of the entire family, taking into account the user's family composition and lifestyle habits. For example, it asks questions to monitor the health status of all family members. In this way, questions to support the health management of the entire family can be generated.

[0034] The analysis unit analyzes the user's answers and evaluates the correlation between multiple symptoms to identify complex disease risks. In the analysis unit, for example, the generation AI analyzes the user's answers and evaluates the correlation between multiple symptoms to identify complex disease risks. For example, the analysis unit evaluates the correlation between headache and fatigue to identify potential disease risks. In addition, the analysis unit collects user answer data, and the generation AI analyzes the data to evaluate the correlation between multiple symptoms. For example, the analysis unit evaluates the correlation between cough and fever to identify the risk of infectious diseases. In addition, the analysis unit analyzes the user's answers and evaluates the correlation between multiple symptoms to identify complex disease risks. For example, the analysis unit evaluates the correlation between chest pain and shortness of breath to identify the risk of heart disease. In this way, the analysis unit can evaluate the correlation between multiple symptoms and identify complex disease risks.

[0035] The analysis unit analyzes the user's lifestyle data and can detect the risk of lifestyle-related diseases early. In the analysis unit, for example, the generation AI analyzes the user's lifestyle data (records of meals and exercise) and detects the risk of lifestyle-related diseases early. For example, it evaluates the content of meals and frequency of exercise to identify the risk of diabetes. The analysis unit also collects the user's lifestyle data, and the generation AI analyzes that data to detect the risk of lifestyle-related diseases early. For example, it evaluates the balance of meals and exercise habits to identify the risk of high blood pressure. The analysis unit also analyzes the user's lifestyle data and detects the risk of lifestyle-related diseases early. For example, it evaluates the calorie intake of meals and the amount of exercise to identify the risk of obesity. This allows the risk of lifestyle-related diseases to be detected early.

[0036] The analysis unit can monitor the health condition of the user's pet and evaluate the impact of the pet's health on the user's health. For example, the analysis unit has the generation AI monitor the health condition of the user's pet and evaluate the impact on the user's health based on that data. For example, it analyzes the pet's activity level and dietary content. The analysis unit also collects the health condition of the user's pet, and the generation AI analyzes that data to evaluate the impact on the user's health. For example, it evaluates the correlation between the pet's health condition and the user's stress level. The analysis unit also has the generation AI monitor the health condition of the user's pet and evaluate the impact on the user's health based on that data. For example, it evaluates the correlation between the pet's health condition and the user's exercise habits. This makes it possible to evaluate the impact of the pet's health on the user's health.

[0037] The analysis unit can assess workplace health risks based on the user's work environment and work stress level. For example, the generation AI collects data on the user's work environment and assesses workplace health risks based on that data. For example, it analyzes noise levels and lighting conditions in the workplace. The analysis unit also monitors the user's work stress level, and the generation AI assesses workplace health risks based on that data. For example, it evaluates stress levels and the risk of heart disease. The analysis unit also evaluates workplace health risks by having the generation AI take the user's work environment and work stress level into consideration. For example, it evaluates the correlation between long working hours and lack of sleep. This makes it possible to assess workplace health risks.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The home medical questionnaire tool can be equipped with a nutritional evaluation unit that records the user's diet and evaluates nutritional balance. For example, the user can take a photo of the meal they ate, and the generation AI can analyze the photo to evaluate the amount of nutrients they ingest. The user can also enter the details of their meal in text, and the generation AI can evaluate the nutritional balance based on that data. Furthermore, the nutritional evaluation unit can suggest areas for improving nutritional balance based on the user's diet history. This allows the user to review their diet and receive specific advice for maintaining a healthy diet.

[0040] The home questionnaire tool can be equipped with an exercise evaluation unit that records the user's exercise habits and evaluates the amount of exercise. For example, the user inputs the details of their daily exercise, and the generation AI evaluates the amount of exercise based on that data. It can also collect data from a wearable device worn by the user while exercising, and the generation AI analyzes that data to evaluate the amount of exercise. Furthermore, the exercise evaluation unit can suggest areas for improving the amount of exercise based on the user's exercise history. This allows the user to review their exercise habits and receive specific advice for maintaining a healthy lifestyle.

[0041] The home questionnaire tool can be equipped with a sleep evaluation unit that records the user's sleep patterns and evaluates the quality of their sleep. For example, the user can input their daily sleep duration and wake-up time, and the generation AI can evaluate their sleep quality based on that data. It can also collect data from a wearable device worn by the user while sleeping, and the generation AI can analyze that data to evaluate their sleep quality. Furthermore, the sleep evaluation unit can suggest areas for improving sleep quality based on the user's sleep history. This allows the user to review their sleep patterns and receive specific advice for maintaining a healthy lifestyle.

[0042] The home medical questionnaire tool can include a schedule management unit that manages vaccination and regular checkup schedules based on the user's health data. For example, it can suggest appropriate vaccination times based on the user's age and health condition. It can also manage regular checkup schedules based on the user's health history. Furthermore, the schedule management unit can send reminders for vaccinations and regular checkups to the user. This allows the user to receive vaccinations and regular checkups at the appropriate times, enabling effective health management.

[0043] The home medical questionnaire tool may include a supplement suggestion unit that suggests appropriate supplement intake based on the user's health data. For example, the tool may evaluate the user's nutritional balance and suggest supplements to compensate for nutrient deficiencies. It may also suggest supplements to reduce specific health risks based on the user's health condition. Furthermore, the supplement suggestion unit may continuously monitor the user's health data and suggest supplement intake at appropriate times. This allows the user to take supplements according to their health condition and maintain their health.

[0044] The home medical questionnaire tool may include a fitness suggestion unit that proposes an appropriate fitness plan based on the user's health data. For example, the fitness suggestion unit may evaluate the user's exercise habits and health condition and propose a fitness plan tailored to the user's individual needs. It may also provide specific exercise menus and training plans based on the user's goals. Furthermore, the fitness suggestion unit may continuously monitor the user's health data and update the fitness plan at appropriate times. This allows the user to implement a fitness plan tailored to their health condition and maintain a healthy lifestyle.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The medical interview section uses the generation AI to conduct a conversation-based formal medical interview. For example, the generation AI asks the user questions such as, "Have you noticed any changes in your physical condition recently?" or "Are you experiencing pain or discomfort in a particular area?" The generation AI then asks more detailed questions based on the user's answers to understand any changes in symptoms or physical condition. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI proceeds with the medical interview based on that prompt. Step 2: The analysis unit analyzes the data collected by the interview unit. For example, the generation AI analyzes the user's responses and identifies specific symptoms and risk factors. The analysis unit also uses algorithms to assess the risk of disease based on the user's responses. Step 3: The advice unit provides advice to the user based on the results of the analysis by the analysis unit. For example, the generation AI may advise, "You should see a doctor as soon as possible if you have this symptom." The advice unit also provides specific advice for maintaining health based on the user's lifestyle and health condition.

[0047] (Example 2) The home medical interview tool according to an embodiment of the present invention is a system that uses generative AI to conduct conversation-based interviews with official medical interviews and promotes early detection of diseases at home. As a result, the home medical interview tool promotes early detection and treatment of diseases at home, and can resolve issues such as an aging population, a shortage of medical personnel, and rising social security costs.

[0048] The home medical interview tool according to the embodiment includes a medical interview unit, an analysis unit, and an advice unit. The medical interview unit uses a generation AI to conduct a conversation-based formal medical interview. For example, the generation AI asks the user questions such as, "Have you noticed any changes in your physical condition recently?" or "Are you experiencing pain or discomfort in a specific area?" The generation AI then asks more detailed questions based on the user's answers to identify changes in symptoms and physical condition. The generation AI receives input in the form of prompts containing instructions from the user regarding what the user wants the generation AI to do, and the generation AI proceeds with the medical interview based on the prompts. The analysis unit analyzes the data collected by the medical interview unit. For example, the generation AI analyzes the user's answers to identify specific symptoms and risk factors. The analysis unit also uses an algorithm to assess disease risk based on the user's answers. The advice unit provides advice to the user based on the results of the analysis by the analysis unit. For example, the generation AI may advise the user, "You should see a doctor as soon as possible about these symptoms." The advice unit also provides specific advice for maintaining health based on the user's lifestyle and health condition. As a result, the home medical interview tool according to the embodiment enables early detection of illnesses and appropriate advice at home. For example, a user can easily take a medical interview at home and obtain information to determine whether or not to visit a medical institution. In addition, the generation AI continuously monitors the user's health condition and can respond early if an abnormality is detected.

[0049] The interview unit can analyze the user's voice tone and speaking patterns to detect signs of stress or anxiety. For example, the generation AI in the interview unit analyzes the user's voice tone and speaking patterns in real time to detect signs of stress or anxiety. For example, it analyzes the pitch, speed, and pauses of the voice to assess the stress level. The interview unit also collects voice data when the user speaks, and the generation AI analyzes that data to identify signs of stress or anxiety. For example, it detects voice fluctuations and unnatural pauses. The interview unit also uses an algorithm to analyze the user's voice tone and speaking patterns to detect signs of stress or anxiety. For example, it analyzes the frequency and rhythm of the voice to assess the stress level. This enables the generation AI to detect signs of stress or anxiety in the user and take appropriate measures.

[0050] The medical interview unit can refer to the user's past medical interview data and generate customized questions based on the user's health history. In the medical interview unit, for example, the generation AI refers to the user's past medical interview data and generates customized questions based on the user's individual health history. For example, the questions are adjusted based on previously reported symptoms and medical history. The medical interview unit also analyzes the user's past medical interview data and the generation AI generates customized questions based on that data. For example, questions related to a high risk of a particular disease are prioritized. In the medical interview unit, the generation AI refers to the user's health history and generates questions tailored to individual needs. For example, more detailed questions are asked based on past diagnosis results and treatment history. This makes it possible to generate customized questions based on the user's individual health history.

[0051] The interview unit can use the emotion estimation function to evaluate the user's emotional state in real time and adjust the order and content of questions to help the user relax. The interview unit, for example, can use the emotion estimation function to evaluate the user's emotional state in real time and adjust the order and content of questions to help the user relax. For example, if the user is nervous, it will ask questions to help them relax first. The interview unit also uses the generation AI to analyze the user's emotional state and adjust the order and content of questions to help the user relax. For example, if the user is feeling anxious, it will prioritize questions that will give them a sense of security. The interview unit also uses the emotion estimation function to evaluate the user's emotional state and adjust the order and content of questions to help the user relax. For example, if the user is relaxed, it will ask more detailed questions. This makes it possible to adjust the order and content of questions to help the user relax.

[0052] The analysis unit can provide health advice based on environmental factors, based on the user's living environment. For example, the generation AI collects the user's living environment data (temperature and humidity) and provides health advice based on that data. For example, if the humidity is low, it recommends using a humidifier. The analysis unit also monitors the temperature and humidity in the user's home, and the generation AI provides health advice based on that data. For example, if the temperature is high, it recommends appropriate hydration. The analysis unit also analyzes the user's living environment data and provides health advice based on environmental factors. For example, if the humidity is high, it suggests measures to prevent mold growth. This makes it possible to provide health advice based on the user's living environment.

[0053] The medical interview unit can generate questions to support the health management of the entire family, taking into account the user's family composition and lifestyle habits. In the medical interview unit, for example, the generation AI takes into account the user's family composition and lifestyle habits and generates questions to support the health management of the entire family. For example, it asks questions about the eating habits and exercise habits of all family members. In addition, the medical interview unit collects data on the user's family composition and lifestyle habits, and the generation AI generates questions to support the health management of the entire family based on that data. For example, it asks questions to assess the health risks of all family members. In addition, the medical interview unit generates questions to support the health management of the entire family, taking into account the user's family composition and lifestyle habits. For example, it asks questions to monitor the health status of all family members. In this way, questions to support the health management of the entire family can be generated.

[0054] The interview unit can use the emotion estimation function to identify the health topic in which the user is most interested and prioritize questions related to that topic. The interview unit, for example, can use the emotion estimation function to identify the health topic in which the user is most interested and prioritize questions related to that topic. For example, it can ask detailed questions about the topic in which the user has shown interest. The interview unit also uses the generation AI to analyze the user's emotional response and identify the health topic in which the user is most interested. It can prioritize questions related to that topic. For example, it can provide health information that the user is interested in. The interview unit can also use the emotion estimation function to identify the health topic in which the user is most interested and prioritize questions related to that topic. For example, it can provide advice about the topic in which the user has shown interest. This allows it to prioritize questions related to the health topic in which the user is most interested.

[0055] The analysis unit analyzes the user's answers and evaluates the correlation between multiple symptoms to identify complex disease risks. In the analysis unit, for example, the generation AI analyzes the user's answers and evaluates the correlation between multiple symptoms to identify complex disease risks. For example, the analysis unit evaluates the correlation between headache and fatigue to identify potential disease risks. In addition, the analysis unit collects user answer data, and the generation AI analyzes the data to evaluate the correlation between multiple symptoms. For example, the analysis unit evaluates the correlation between cough and fever to identify the risk of infectious diseases. In addition, the analysis unit analyzes the user's answers and evaluates the correlation between multiple symptoms to identify complex disease risks. For example, the analysis unit evaluates the correlation between chest pain and shortness of breath to identify the risk of heart disease. In this way, the analysis unit can evaluate the correlation between multiple symptoms and identify complex disease risks.

[0056] The analysis unit analyzes the user's lifestyle data and can detect the risk of lifestyle-related diseases early. In the analysis unit, for example, the generation AI analyzes the user's lifestyle data (records of meals and exercise) and detects the risk of lifestyle-related diseases early. For example, it evaluates the content of meals and frequency of exercise to identify the risk of diabetes. The analysis unit also collects the user's lifestyle data, and the generation AI analyzes that data to detect the risk of lifestyle-related diseases early. For example, it evaluates the balance of meals and exercise habits to identify the risk of high blood pressure. The analysis unit also analyzes the user's lifestyle data and detects the risk of lifestyle-related diseases early. For example, it evaluates the calorie intake of meals and the amount of exercise to identify the risk of obesity. This allows the risk of lifestyle-related diseases to be detected early.

[0057] The analysis unit can use the emotion estimation function to analyze the association between the user's emotional state and health state, and evaluate the impact of emotional stress on health. For example, the analysis unit uses the emotion estimation function to analyze the association between the user's emotional state and health state, and evaluate the impact of emotional stress on health. For example, it evaluates the association between stress level and blood pressure. The analysis unit also uses the generation AI to analyze the user's emotional state, and evaluates the association with the health state. For example, it evaluates the association between emotional stress and sleep quality. The analysis unit also uses the emotion estimation function to analyze the association between the user's emotional state and health state, and evaluate the impact of emotional stress on health. For example, it evaluates the association between stress level and immune function. This makes it possible to evaluate the impact of emotional stress on health.

[0058] The analysis unit can monitor the health condition of the user's pet and evaluate the impact of the pet's health on the user's health. For example, the analysis unit has the generation AI monitor the health condition of the user's pet and evaluate the impact on the user's health based on that data. For example, it analyzes the pet's activity level and dietary content. The analysis unit also collects the health condition of the user's pet, and the generation AI analyzes that data to evaluate the impact on the user's health. For example, it evaluates the correlation between the pet's health condition and the user's stress level. The analysis unit also has the generation AI monitor the health condition of the user's pet and evaluate the impact on the user's health based on that data. For example, it evaluates the correlation between the pet's health condition and the user's exercise habits. This makes it possible to evaluate the impact of the pet's health on the user's health.

[0059] The analysis unit can assess workplace health risks based on the user's work environment and work stress level. For example, the generation AI collects data on the user's work environment and assesses workplace health risks based on that data. For example, it analyzes noise levels and lighting conditions in the workplace. The analysis unit also monitors the user's work stress level, and the generation AI assesses workplace health risks based on that data. For example, it evaluates stress levels and the risk of heart disease. The analysis unit also evaluates workplace health risks by having the generation AI take the user's work environment and work stress level into consideration. For example, it evaluates the correlation between long working hours and lack of sleep. This makes it possible to assess workplace health risks.

[0060] The analysis unit can use the emotion estimation function to identify the health risk that the user is most anxious about and propose specific measures for that risk. For example, the analysis unit can use the emotion estimation function to identify the health risk that the user is most anxious about and propose specific measures for that risk. For example, it can provide preventive measures for symptoms that the user is anxious about. The analysis unit also uses the generation AI to analyze the user's emotional response and identify the health risk that the user is most anxious about. It can propose specific measures for that risk. For example, it can propose methods for stress management. The analysis unit also uses the emotion estimation function to identify the health risk that the user is most anxious about and propose specific measures for that risk. For example, it can provide methods for early detection of health risks. This makes it possible to propose specific measures for the health risk that the user is most anxious about.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The home medical questionnaire tool can be equipped with a nutritional evaluation unit that records the user's diet and evaluates nutritional balance. For example, the user can take a photo of the meal they ate, and the generation AI can analyze the photo to evaluate the amount of nutrients they ingest. The user can also enter the details of their meal in text, and the generation AI can evaluate the nutritional balance based on that data. Furthermore, the nutritional evaluation unit can suggest areas for improving nutritional balance based on the user's diet history. This allows the user to review their diet and receive specific advice for maintaining a healthy diet.

[0063] The home questionnaire tool can be equipped with an exercise evaluation unit that records the user's exercise habits and evaluates the amount of exercise. For example, the user inputs the details of their daily exercise, and the generation AI evaluates the amount of exercise based on that data. It can also collect data from a wearable device worn by the user while exercising, and the generation AI analyzes that data to evaluate the amount of exercise. Furthermore, the exercise evaluation unit can suggest areas for improving the amount of exercise based on the user's exercise history. This allows the user to review their exercise habits and receive specific advice for maintaining a healthy lifestyle.

[0064] The home questionnaire tool can be equipped with a sleep evaluation unit that records the user's sleep patterns and evaluates the quality of their sleep. For example, the user can input their daily sleep duration and wake-up time, and the generation AI can evaluate their sleep quality based on that data. It can also collect data from a wearable device worn by the user while sleeping, and the generation AI can analyze that data to evaluate their sleep quality. Furthermore, the sleep evaluation unit can suggest areas for improving sleep quality based on the user's sleep history. This allows the user to review their sleep patterns and receive specific advice for maintaining a healthy lifestyle.

[0065] The home medical questionnaire tool may include a relaxation suggestion unit that estimates the user's emotional state and suggests relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the relaxation suggestion unit may suggest relaxation methods such as deep breathing or meditation. If the user is feeling anxious, the relaxation suggestion unit may suggest relaxing music or aromatherapy. Furthermore, the relaxation suggestion unit may continuously monitor the user's emotional state and suggest relaxation methods at appropriate times. This allows the user to practice relaxation methods that suit their emotional state and reduce stress and anxiety.

[0066] The home medical questionnaire tool may include an exercise suggestion unit that estimates the user's emotional state and suggests an appropriate exercise method based on the estimated emotion. For example, if the user is feeling stressed, the exercise suggestion unit may suggest relaxing yoga or stretching. If the user is feeling energetic, the exercise suggestion unit may suggest exercises such as running or aerobics. Furthermore, the exercise suggestion unit may continuously monitor the user's emotional state and suggest an exercise method at an appropriate time. This allows the user to practice an exercise method that suits their emotional state and maintain a healthy lifestyle.

[0067] The home medical questionnaire tool may include a meal suggestion unit that estimates the user's emotional state and suggests an appropriate meal menu based on the estimated emotion. For example, if the user is feeling stressed, the unit may suggest relaxing herbal tea or a nutritionally balanced meal. Also, if the user is tired, the unit may suggest a meal suitable for replenishing energy. Furthermore, the meal suggestion unit may continuously monitor the user's emotional state and suggest a meal menu at an appropriate time. This allows the user to practice a meal menu that suits their emotional state and maintain a healthy lifestyle.

[0068] The home medical questionnaire tool may include a sleep environment suggestion unit that estimates the user's emotional state and suggests an appropriate sleep environment based on the estimated emotion. For example, if the user is feeling stressed, it may suggest aromas or music with a relaxing effect. Also, if the user is feeling anxious, it may suggest lighting or bedding that gives a sense of security. Furthermore, the sleep environment suggestion unit may continuously monitor the user's emotional state and suggest a sleep environment at an appropriate time. This allows the user to create a sleep environment that suits their emotional state and achieve high-quality sleep.

[0069] The home medical questionnaire tool can include a schedule management unit that manages vaccination and regular checkup schedules based on the user's health data. For example, it can suggest appropriate vaccination times based on the user's age and health condition. It can also manage regular checkup schedules based on the user's health history. Furthermore, the schedule management unit can send reminders for vaccinations and regular checkups to the user. This allows the user to receive vaccinations and regular checkups at the appropriate times, enabling effective health management.

[0070] The home medical questionnaire tool may include a supplement suggestion unit that suggests appropriate supplement intake based on the user's health data. For example, the tool may evaluate the user's nutritional balance and suggest supplements to compensate for nutrient deficiencies. It may also suggest supplements to reduce specific health risks based on the user's health condition. Furthermore, the supplement suggestion unit may continuously monitor the user's health data and suggest supplement intake at appropriate times. This allows the user to take supplements according to their health condition and maintain their health.

[0071] The home medical questionnaire tool may include a fitness suggestion unit that proposes an appropriate fitness plan based on the user's health data. For example, the fitness suggestion unit may evaluate the user's exercise habits and health condition and propose a fitness plan tailored to the user's individual needs. It may also provide specific exercise menus and training plans based on the user's goals. Furthermore, the fitness suggestion unit may continuously monitor the user's health data and update the fitness plan at appropriate times. This allows the user to implement a fitness plan tailored to their health condition and maintain a healthy lifestyle.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The medical interview section uses the generation AI to conduct a conversation-based formal medical interview. For example, the generation AI asks the user questions such as, "Have you noticed any changes in your physical condition recently?" or "Are you experiencing pain or discomfort in a particular area?" The generation AI then asks more detailed questions based on the user's answers to understand any changes in symptoms or physical condition. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI proceeds with the medical interview based on that prompt. Step 2: The analysis unit analyzes the data collected by the interview unit. For example, the generation AI analyzes the user's responses and identifies specific symptoms and risk factors. The analysis unit also uses algorithms to assess the risk of disease based on the user's responses. Step 3: The advice unit provides advice to the user based on the results of the analysis by the analysis unit. For example, the generation AI may advise, "You should see a doctor as soon as possible if you have this symptom." The advice unit also provides specific advice for maintaining health based on the user's lifestyle and health condition.

[0074] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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, RAM 30, and 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.

[0080] 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.

[0081] 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 audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] 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 carried out in a secure state.

[0084] Fig. 4 shows an example of the 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.

[0085] 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.

[0086] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0088] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0090] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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, RAM 30, and 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.

[0095] 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.

[0096] 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 audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] 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 carried out in a secure state.

[0099] 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.

[0100] 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.

[0101] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 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 specific processing unit 290 using these models.

[0103] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0105] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the 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.

[0109] 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, RAM 30, and 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.

[0110] 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.

[0111] 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 audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] 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 carried out in a secure state.

[0114] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. 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.

[0115] 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.

[0116] 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.

[0117] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The robot 414 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 voice indicating a user input regarding 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.

[0121] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0123] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] 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.

[0131] 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 USB (Universal Serial Bus) 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.

[0132] Alternatively, 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 in response to a request from the data processing device 12.

[0133] 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.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0136] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 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. The medical interview section uses generative AI to conduct conversation-based interviews with the official medical interviewer. an analysis unit that analyzes the data collected by the interview unit; an advice unit that provides advice to a user based on the results of the analysis by the analysis unit. A system characterized by:

2. The interview unit Analyzing the user's tone of voice and speaking patterns to detect signs of stress or anxiety 2. The system of claim 1.

3. The interview unit References the user's past medical history data to generate customized questions based on their health history.

2. The system of claim 1.

4. The interview unit Evaluating the user's emotional state in real time and adjusting the order and content of questions to help the user relax 2. The system of claim 1.

5. The analysis unit Providing health advice based on environmental factors based on the user's living environment 2. The system of claim 1.

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