system

Abstract

The system according to the embodiment aims to promote early detection of diseases in the home. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects information from a user. The analysis unit analyzes the information collected by the collection unit. The estimation unit estimates the possibility of disease based on the information analyzed by the analysis unit. The provision unit provides advice based on the results estimated by the estimation unit.

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 made it difficult to detect diseases early at home, leading to issues such as a shortage of medical personnel and rising social security costs.

[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 collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects information from a user. The analysis unit analyzes the information collected by the collection unit. The estimation unit estimates the possibility of a disease based on the information analyzed by the analysis unit. The provision unit provides advice based on the result estimated by the estimation 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 promotes early detection and treatment to address issues such as an aging population, a shortage of medical personnel, and rising social security costs. In this system, a user accesses the home medical interview tool, and a generating AI conducts a formal interview with the user in a conversational format. The generating AI analyzes the user's responses and collects necessary information. Based on the collected information, the generating AI then estimates the likelihood of a disease and provides advice for early detection. This allows users to easily undergo medical interviews at home, enabling early disease detection. For example, if a user feels unwell recently, they access the home medical interview tool and the generating AI asks, "What symptoms do you have?" If the user answers, "I have a headache and dizziness," the generating AI asks additional questions such as, "How long has the headache been going on for?" and "When does the dizziness occur?" to collect more detailed information. Based on the collected information, the generating AI provides advice such as, "These symptoms may be due to ____. Please consult a medical institution as soon as possible." This mechanism allows users to easily undergo medical interviews at home, enabling early disease detection. Furthermore, by preventing the personalization of medical interviews at medical institutions and enabling uniform interviews, it also contributes to resolving the shortage of medical personnel. As a result, the home medical interview tool allows users to easily take medical interviews at home, making it possible to detect diseases early. Furthermore, by preventing the personalization of medical interviews at medical institutions and enabling uniform interviews, it also contributes to resolving the shortage of medical personnel.

[0029] A home medical questionnaire tool according to an embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects information from a user. For example, the collection unit uses a generation AI to conduct a formal medical questionnaire in a conversational format. The generation AI analyzes the user's responses and collects necessary information. For example, if a user feels unwell recently, the collection unit asks the generation AI, "What symptoms do you have?" If the user replies, "I have a headache and dizziness," the generation AI asks additional questions such as, "How long have you had a headache?" and "When do you get dizzy?" to collect more detailed information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit includes an algorithm that estimates the possibility of a disease based on the collected information. For example, the analysis unit causes the generation AI to estimate the possibility of a disease based on the collected information. The estimation unit estimates the possibility of a disease based on the information analyzed by the analysis unit. For example, the estimation unit causes the generation AI to estimate the possibility of a disease based on the collected information. The providing unit provides advice based on the result of estimation by the estimating unit. The providing unit provides specific advice based on the result of estimation, for example. For example, the providing unit provides advice such as, "These symptoms may be due to ____. Please visit a medical institution as soon as possible." This allows the home medical interview tool according to the embodiment to allow users to easily undergo medical interviews at home, enabling early detection of diseases. Furthermore, by preventing medical interviews at medical institutions from becoming personalized and enabling uniform medical interviews to be conducted, it also contributes to resolving the shortage of medical personnel.

[0030] The collection unit can use the generation AI to conduct the official medical interview in a conversational format. The collection unit, for example, uses the generation AI to conduct the official medical interview in a conversational format. The generation AI analyzes the user's answers and collects necessary information. For example, if the user feels "unwell recently," the collection unit asks the generation AI, "What symptoms do you have?" If the user answers, "I have a headache and dizziness," the generation AI asks additional questions such as, "How long has the headache been going on for?" and "When do you get dizzy?" to collect more detailed information. In this way, the generation AI can conduct the official medical interview in a conversational format. The generation AI can use, for example, a natural language generation model such as GPT-4 (registered trademark) or Gemini. This allows the generation AI to analyze the user's answers and collect necessary information.

[0031] The analysis unit may include an algorithm that estimates the possibility of disease based on the collected information. The analysis unit may include, for example, an algorithm that estimates the possibility of disease based on the collected information. For example, the analysis unit may have a generative AI that estimates the possibility of disease based on the collected information. By including an algorithm that estimates the possibility of disease based on the collected information, the accuracy of the analysis is improved. The algorithm may be realized using technologies such as neural networks, decision trees, and support vector machines. This allows the analysis unit to estimate the possibility of disease based on the collected information.

[0032] The providing unit can provide specific advice based on the estimation result. The providing unit provides specific advice based on the estimation result, for example. For example, the providing unit provides advice such as, "These symptoms may be due to ____. Please visit a medical institution as soon as possible." By providing specific advice based on the estimation result, it is possible to encourage the user to take appropriate action. The specific advice can include, for example, suggestions for improving lifestyle habits, recommendations for visiting a medical institution, etc. This allows the providing unit to provide specific advice based on the estimation result.

[0033] The collection unit can refer to the user's past health data and select the most appropriate question. The collection unit, for example, refers to the user's past health data and selects the most appropriate question. For example, the collection unit can ask relevant questions based on the user's past diagnosis results. The collection unit can also refer to the user's past health check results and ask questions about specific symptoms. The collection unit can also ask questions about high-risk diseases based on the user's past medical history. This allows more relevant questions to be asked by referring to the user's past health data. The criteria and method for selecting the most appropriate question can be realized, for example, by prioritizing questions based on the past health data. This allows the collection unit to refer to the user's past health data and select the most appropriate question.

[0034] The collection unit can customize the content of the questions taking into account the user's lifestyle habits and environmental information. The collection unit customizes the content of the questions taking into account, for example, the user's lifestyle habits and environmental information. For example, if the user is a smoker, the collection unit can ask questions related to smoking. Furthermore, if the user is not getting enough exercise, the collection unit can ask questions related to exercise. Furthermore, if the user works in a high-stress environment, the collection unit can ask questions related to stress. In this way, by taking into account the user's lifestyle habits and environmental information, more personalized questions can be asked. Specific types and collection methods of lifestyle habits and environmental information are realized, for example, by dietary habits, exercise habits, living environment, etc. In this way, the collection unit can customize the content of the questions taking into account the user's lifestyle habits and environmental information.

[0035] The collection unit can prioritize questions about region-specific diseases by taking into account the user's geographical location information. The collection unit can prioritize questions about region-specific diseases by taking into account, for example, the user's geographical location information. For example, if the user lives in a specific region, the collection unit can ask questions about diseases that are prevalent in that region. Furthermore, if the user is traveling, the collection unit can ask questions about diseases that are prevalent in the region to which the user is traveling. Furthermore, if the user has moved, the collection unit can ask questions about disease risks in the new region. In this way, by taking into account the user's geographical location information, questions about region-specific diseases can be prioritized. Specific types and collection methods of geographical location information can be realized, for example, by GPS data, address information, etc. In this way, the collection unit can prioritize questions about region-specific diseases by taking into account the user's geographical location information.

[0036] The collection unit can analyze the user's social media activity and collect related health information. The collection unit, for example, analyzes the user's social media activity and collects related health information. For example, the collection unit can ask related questions based on health information shared by the user on social media. The collection unit can also estimate the user's stress level from the user's social media activity and ask related questions. The collection unit can also ask detailed questions based on symptoms mentioned by the user on social media. In this way, the related health information can be collected by analyzing the user's social media activity. Specific types and collection methods of social media activity can be realized, for example, by the content of posts, the number of likes, the number of followers, etc. In this way, the collection unit can analyze the user's social media activity and collect related health information.

[0037] The analysis unit can improve the accuracy of the analysis by comparing the collected information with past medical data based on the collected information. The analysis unit, for example, improves the accuracy of the analysis by comparing the collected information with past medical data based on the collected information. For example, the analysis unit compares the collected information with past diagnostic data to improve the accuracy of the analysis. The analysis unit can also compare the collected information with past health check data to improve the accuracy of the analysis. The analysis unit can also compare the collected information with past medical history to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by comparing with past medical data. The specific type and collection method of past medical data can be realized, for example, by electronic medical records, medical records, etc. In this way, the analysis unit can improve the accuracy of the analysis by comparing the collected information with past medical data based on the collected information.

[0038] The analysis unit can correct the analysis results during analysis by taking into account the user's lifestyle habits and environmental information. The analysis unit, for example, corrects the analysis results during analysis by taking into account the user's lifestyle habits and environmental information. For example, if the user is a smoker, the analysis unit corrects the analysis results by taking into account risks associated with smoking. Furthermore, if the user is not getting enough exercise, the analysis unit can also correct the analysis results by taking into account risks associated with lack of exercise. Furthermore, if the user works in a high-stress environment, the analysis unit can also correct the analysis results by taking into account risks associated with stress. In this way, the analysis results can be corrected by taking into account the user's lifestyle habits and environmental information. Specific types and collection methods of lifestyle habits and environmental information are realized, for example, by dietary habits, exercise habits, living environment, etc. In this way, the analysis unit can correct the analysis results by taking into account the user's lifestyle habits and environmental information during analysis.

[0039] The analysis unit can evaluate region-specific disease risk by taking into account the user's geographical location information during analysis. For example, the analysis unit evaluates region-specific disease risk by taking into account the user's geographical location information during analysis. For example, if the user lives in a specific region, the analysis unit evaluates the disease risk prevalent in that region. Furthermore, if the user is traveling, the analysis unit can evaluate the disease risk specific to the region to which the user is traveling. Furthermore, if the user moves, the analysis unit can evaluate the disease risk in the new region. In this way, region-specific disease risk can be evaluated by taking into account the user's geographical location information. Specific evaluation methods and criteria for region-specific disease risk are realized, for example, using regional epidemiological data, environmental factors, etc. In this way, the analysis unit can evaluate region-specific disease risk by taking into account the user's geographical location information during analysis.

[0040] The analysis unit can complement the analysis results by referring to the user's social media activity during analysis. The analysis unit, for example, complements the analysis results by referring to the user's social media activity during analysis. For example, the analysis unit complements the analysis results based on health information shared by the user on social media. The analysis unit can also estimate the user's stress level from the user's social media activity and complement the analysis results. The analysis unit can also complement the analysis results based on symptoms mentioned by the user on social media. In this way, the analysis results can be complemented by referring to the user's social media activity. The specific types and collection methods of social media activity are realized by, for example, the content of posts, the number of likes, the number of followers, etc. In this way, the analysis unit can complement the analysis results by referring to the user's social media activity during analysis.

[0041] The estimation unit can estimate the possibility of disease based on the collected information by comparing it with past medical data. The estimation unit, for example, estimates the possibility of disease based on the collected information by comparing it with past medical data. For example, the estimation unit compares the collected information with past diagnostic data to estimate the possibility of disease. The estimation unit can also compare the collected information with past health check data to estimate the possibility of disease. The estimation unit can also compare the collected information with past medical history to estimate the possibility of disease. In this way, by comparing it with past medical data, the possibility of disease can be more accurately estimated. The specific type and collection method of the past medical data can be realized, for example, by electronic medical records, medical records, etc. In this way, the estimation unit can estimate the possibility of disease based on the collected information by comparing it with past medical data.

[0042] The estimation unit can correct the estimation result by taking into account the user's lifestyle habits and environmental information at the time of estimation. The estimation unit, for example, corrects the estimation result by taking into account the user's lifestyle habits and environmental information at the time of estimation. For example, if the user is a smoker, the estimation unit corrects the estimation result by taking into account risks associated with smoking. Furthermore, if the user is not getting enough exercise, the estimation unit can also correct the estimation result by taking into account risks associated with not getting enough exercise. Furthermore, if the user works in a high-stress environment, the estimation unit can also correct the estimation result by taking into account risks associated with stress. In this way, the estimation result can be corrected by taking into account the user's lifestyle habits and environmental information. Specific types and collection methods of the lifestyle habits and environmental information are realized by, for example, eating habits, exercise habits, living environment, etc. In this way, the estimation unit can correct the estimation result by taking into account the user's lifestyle habits and environmental information at the time of estimation.

[0043] The estimation unit can evaluate the region-specific disease risk by taking into account the user's geographical location information during estimation. For example, the estimation unit evaluates the region-specific disease risk by taking into account the user's geographical location information during estimation. For example, if the user lives in a specific region, the estimation unit evaluates the disease risk prevalent in that region. Furthermore, if the user is traveling, the estimation unit can evaluate the disease risk specific to the region to which the user travels. Furthermore, if the user moves, the estimation unit can evaluate the disease risk in the new region. In this way, the region-specific disease risk can be evaluated by taking into account the user's geographical location information. Specific evaluation methods and criteria for the region-specific disease risk are realized, for example, by regional epidemiological data, environmental factors, etc. In this way, the estimation unit can evaluate the region-specific disease risk by taking into account the user's geographical location information during estimation.

[0044] The estimation unit can complement the estimation result by referring to the user's social media activity during estimation. For example, the estimation unit complements the estimation result by referring to the user's social media activity during estimation. For example, the estimation unit complements the estimation result based on health information shared by the user on social media. The estimation unit can also estimate a stress level from the user's social media activity and complement the estimation result. The estimation unit can also complement the estimation result based on symptoms mentioned by the user on social media. In this way, the estimation result can be complemented by referring to the user's social media activity. Specific types and collection methods of social media activity are realized by, for example, post content, number of likes, number of followers, etc. In this way, the estimation unit can complement the estimation result by referring to the user's social media activity during estimation.

[0045] The providing unit can provide specific advice based on the estimation result by referring to the user's past health data. The providing unit can provide specific advice based on the estimation result by referring to the user's past health data, for example. For example, the providing unit can provide specific advice based on the user's past diagnosis results. The providing unit can also provide specific advice by referring to the user's past health check results. The providing unit can also provide specific advice based on the user's past medical history. In this way, more specific advice can be provided by referring to the user's past health data. The specific type and collection method of the past health data can be realized, for example, by medical records, health check results, etc. In this way, the providing unit can provide specific advice based on the estimation result by referring to the user's past health data.

[0046] The providing unit can customize advice based on the estimation result, taking into account the user's lifestyle habits and environmental information. The providing unit customizes advice based on, for example, the estimation result, taking into account the user's lifestyle habits and environmental information. For example, if the user is a smoker, the providing unit can provide advice related to smoking. Furthermore, if the user is not getting enough exercise, the providing unit can also provide advice related to exercise. Furthermore, if the user works in a high-stress environment, the providing unit can also provide advice related to stress management. In this way, more personalized advice can be provided by taking into account the user's lifestyle habits and environmental information. Specific types and collection methods of lifestyle habits and environmental information are realized, for example, by dietary habits, exercise habits, living environment, etc. In this way, the providing unit can customize advice based on the estimation result, taking into account the user's lifestyle habits and environmental information.

[0047] The providing unit can provide region-specific advice based on the estimation result, taking into account the user's geographical location information. The providing unit can provide region-specific advice based on the estimation result, taking into account the user's geographical location information, for example. For example, if the user lives in a specific region, the providing unit can provide advice about diseases prevalent in that region. Furthermore, if the user is traveling, the providing unit can provide advice about diseases specific to the region of the user's travel destination. Furthermore, if the user has moved, the providing unit can provide advice about disease risks in the new region. In this way, region-specific advice can be provided by taking into account the user's geographical location information. Specific content and criteria of the region-specific advice can be realized, for example, by information on local medical institutions, region-specific health risks, etc. In this way, the providing unit can provide region-specific advice based on the estimation result, taking into account the user's geographical location information.

[0048] The providing unit can complement advice based on the estimation result by referring to the user's social media activity. The providing unit, for example, complements advice based on the estimation result by referring to the user's social media activity. For example, the providing unit complements advice based on health information shared by the user on social media. The providing unit can also estimate a stress level from the user's social media activity and complement advice. The providing unit can also complement advice based on symptoms mentioned by the user on social media. In this way, advice can be complemented by referring to the user's social media activity. Specific types and collection methods of social media activity can be realized, for example, by post content, number of likes, number of followers, etc. In this way, the providing unit can complement advice based on the estimation result by referring to the user's social media activity.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The home medical questionnaire tool stores the user's health data on the cloud and can share it with other medical institutions. For example, when a user visits a medical institution, doctors can refer to the health data on the cloud, enabling faster and more accurate diagnoses. Furthermore, even if a user visits multiple medical institutions, sharing the data on the cloud allows them to receive consistent medical services. Furthermore, analyzing the data on the cloud makes it possible to understand the health status of an entire region and evaluate the risk of diseases specific to that region. This makes it possible to effectively utilize users' health data and improve the quality of medical services.

[0051] The home medical questionnaire tool can collect a user's dietary records and evaluate their nutritional balance. For example, when a user inputs their daily dietary information, the collection unit analyzes the data and evaluates whether they are lacking or overdosing on nutrients. The analysis unit can also estimate health risks based on the user's dietary records. For example, it can provide information that excessive fat intake may increase the risk of heart disease. Furthermore, the provision unit can provide specific advice for improving nutritional balance based on the user's dietary records. This allows the user to reduce health risks through daily dietary management.

[0052] The home questionnaire tool can collect a user's exercise data and evaluate their exercise habits. For example, when a user inputs the amount of exercise they do each day, the collection unit analyzes the data and evaluates the risk of insufficient or excessive exercise. The analysis unit can also estimate health risks based on the user's exercise data. For example, it can provide information that insufficient exercise may increase the risk of obesity or diabetes. Furthermore, the provision unit can provide specific advice on the appropriate amount and method of exercise based on the user's exercise data. This allows users to reduce health risks through daily exercise management.

[0053] The home questionnaire tool can collect a user's sleep data and evaluate their sleep quality. For example, when a user inputs their daily sleep duration and sleep quality, the collection unit analyzes the data and evaluates the risk of sleep deprivation or excessive sleep. The analysis unit can also estimate health risks based on the user's sleep data. For example, the analysis unit can provide information that lack of sleep may increase the risk of stress or a weakened immune system. Furthermore, the provision unit can provide specific advice for improving sleep quality based on the user's sleep data. This allows the user to reduce health risks through daily sleep management.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects information from the user. For example, the collection unit uses the generation AI to conduct a formal interview in a conversational format. The generation AI analyzes the user's answers and collects the necessary information. For example, if the user feels unwell recently, the generation AI asks, "What symptoms do you have?" If the user answers, "I have headaches and dizziness," the generation AI asks additional questions such as, "How long have you had headaches?" and "When do you get dizzy?" to collect more detailed information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit includes, for example, an algorithm that estimates the possibility of disease based on the collected information. For example, the analysis unit causes the generation AI to estimate the possibility of disease based on the collected information. Step 3: The estimation unit estimates the possibility of disease based on the information analyzed by the analysis unit. For example, the estimation unit uses the generation AI to estimate the possibility of disease based on the collected information. Step 4: The providing unit provides advice based on the results of the estimation by the estimating unit. The providing unit provides specific advice based on the results of the estimation. For example, the providing unit provides advice such as "These symptoms may be due to ____. Please consult a medical institution as soon as possible."

[0056] (Example 2) The home medical interview tool according to an embodiment of the present invention is a system that promotes early detection and treatment to address issues such as an aging population, a shortage of medical personnel, and rising social security costs. In this system, a user accesses the home medical interview tool, and a generating AI conducts a formal interview with the user in a conversational format. The generating AI analyzes the user's responses and collects necessary information. Based on the collected information, the generating AI then estimates the likelihood of a disease and provides advice for early detection. This allows users to easily undergo medical interviews at home, enabling early disease detection. For example, if a user feels unwell recently, they access the home medical interview tool and the generating AI asks, "What symptoms do you have?" If the user answers, "I have a headache and dizziness," the generating AI asks additional questions such as, "How long has the headache been going on for?" and "When does the dizziness occur?" to collect more detailed information. Based on the collected information, the generating AI provides advice such as, "These symptoms may be due to ____. Please consult a medical institution as soon as possible." This mechanism allows users to easily undergo medical interviews at home, enabling early disease detection. Furthermore, by preventing the personalization of medical interviews at medical institutions and enabling uniform interviews, it also contributes to resolving the shortage of medical personnel. As a result, the home medical interview tool allows users to easily take medical interviews at home, making it possible to detect diseases early. Furthermore, by preventing the personalization of medical interviews at medical institutions and enabling uniform interviews, it also contributes to resolving the shortage of medical personnel.

[0057] A home medical questionnaire tool according to an embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects information from a user. For example, the collection unit uses a generation AI to conduct a formal medical questionnaire in a conversational format. The generation AI analyzes the user's responses and collects necessary information. For example, if a user feels unwell recently, the collection unit asks the generation AI, "What symptoms do you have?" If the user replies, "I have a headache and dizziness," the generation AI asks additional questions such as, "How long have you had a headache?" and "When do you get dizzy?" to collect more detailed information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit includes an algorithm that estimates the possibility of a disease based on the collected information. For example, the analysis unit causes the generation AI to estimate the possibility of a disease based on the collected information. The estimation unit estimates the possibility of a disease based on the information analyzed by the analysis unit. For example, the estimation unit causes the generation AI to estimate the possibility of a disease based on the collected information. The providing unit provides advice based on the result of estimation by the estimating unit. The providing unit provides specific advice based on the result of estimation, for example. For example, the providing unit provides advice such as, "These symptoms may be due to ____. Please visit a medical institution as soon as possible." This allows the home medical interview tool according to the embodiment to allow users to easily undergo medical interviews at home, enabling early detection of diseases. Furthermore, by preventing medical interviews at medical institutions from becoming personalized and enabling uniform medical interviews to be conducted, it also contributes to resolving the shortage of medical personnel.

[0058] The collection unit can use the generation AI to conduct the official medical interview in a conversational format. The collection unit, for example, uses the generation AI to conduct the official medical interview in a conversational format. The generation AI analyzes the user's answers and collects necessary information. For example, if the user feels "unwell recently," the collection unit asks the generation AI, "What symptoms do you have?" If the user answers, "I have a headache and dizziness," the generation AI asks additional questions such as, "How long have you had this headache?" and "When do you get dizzy?" to collect more detailed information. In this way, the generation AI can conduct the official medical interview in a conversational format. The generation AI can use natural language generation models such as GPT-4 and Gemini. This allows the generation AI to analyze the user's answers and collect necessary information.

[0059] The analysis unit may include an algorithm that estimates the possibility of disease based on the collected information. The analysis unit may include, for example, an algorithm that estimates the possibility of disease based on the collected information. For example, the analysis unit may have a generative AI that estimates the possibility of disease based on the collected information. By including an algorithm that estimates the possibility of disease based on the collected information, the accuracy of the analysis is improved. The algorithm may be realized using technologies such as neural networks, decision trees, and support vector machines. This allows the analysis unit to estimate the possibility of disease based on the collected information.

[0060] The providing unit can provide specific advice based on the estimation result. The providing unit provides specific advice based on the estimation result, for example. For example, the providing unit provides advice such as, "These symptoms may be due to ____. Please visit a medical institution as soon as possible." By providing specific advice based on the estimation result, it is possible to encourage the user to take appropriate action. The specific advice can include, for example, suggestions for improving lifestyle habits, recommendations for visiting a medical institution, etc. This allows the providing unit to provide specific advice based on the estimation result.

[0061] The collection unit can estimate the user's emotions and adjust the content and order of questions in the medical interview based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the content and order of questions in the medical interview based on the estimated user's emotions. For example, if the user is feeling anxious, the collection unit can start with simple questions to relax the user. Furthermore, if the user is relaxed, the collection unit can also ask detailed questions first. Furthermore, if the user is in a hurry, the collection unit can also prioritize important questions. This allows for more appropriate medical interviews by adjusting the content and order of questions in the medical interview based on the user's emotions. Emotion estimation is achieved using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the collection unit to estimate the user's emotions and adjust the content and order of questions in the medical interview based on the estimated user's emotions.

[0062] The collection unit can refer to the user's past health data and select the most appropriate question. The collection unit, for example, refers to the user's past health data and selects the most appropriate question. For example, the collection unit can ask relevant questions based on the user's past diagnosis results. The collection unit can also refer to the user's past health check results and ask questions about specific symptoms. The collection unit can also ask questions about high-risk diseases based on the user's past medical history. This allows more relevant questions to be asked by referring to the user's past health data. The criteria and method for selecting the most appropriate question can be realized, for example, by prioritizing questions based on the past health data. This allows the collection unit to refer to the user's past health data and select the most appropriate question.

[0063] The collection unit can customize the content of the questions taking into account the user's lifestyle habits and environmental information. The collection unit customizes the content of the questions taking into account, for example, the user's lifestyle habits and environmental information. For example, if the user is a smoker, the collection unit can ask questions related to smoking. Furthermore, if the user is not getting enough exercise, the collection unit can ask questions related to exercise. Furthermore, if the user works in a high-stress environment, the collection unit can ask questions related to stress. In this way, by taking into account the user's lifestyle habits and environmental information, more personalized questions can be asked. Specific types and collection methods of lifestyle habits and environmental information are realized, for example, by dietary habits, exercise habits, living environment, etc. In this way, the collection unit can customize the content of the questions taking into account the user's lifestyle habits and environmental information.

[0064] The collection unit can estimate the user's emotions and adjust the difficulty and level of detail of questions based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the difficulty and level of detail of questions based on the estimated user's emotions. For example, the collection unit asks simple, easy-to-understand questions when the user is feeling anxious. The collection unit can also ask detailed, specialized questions when the user is relaxed. The collection unit can also ask short, to-the-point questions when the user is in a hurry. This allows for more appropriate medical interviews by adjusting the difficulty and level of detail of questions according to the user's emotions. The criteria and method for adjusting the difficulty and level of detail of questions can be realized, for example, by adjusting them according to the user's level of understanding or interest. This allows the collection unit to estimate the user's emotions and adjust the difficulty and level of detail of questions based on the estimated user's emotions.

[0065] The collection unit can prioritize questions about region-specific diseases by taking into account the user's geographical location information. The collection unit can prioritize questions about region-specific diseases by taking into account, for example, the user's geographical location information. For example, if the user lives in a specific region, the collection unit can ask questions about diseases that are prevalent in that region. Furthermore, if the user is traveling, the collection unit can ask questions about diseases that are prevalent in the region to which the user is traveling. Furthermore, if the user has moved, the collection unit can ask questions about disease risks in the new region. In this way, by taking into account the user's geographical location information, questions about region-specific diseases can be prioritized. Specific types and collection methods of geographical location information can be realized, for example, by GPS data, address information, etc. In this way, the collection unit can prioritize questions about region-specific diseases by taking into account the user's geographical location information.

[0066] The collection unit can analyze the user's social media activity and collect related health information. The collection unit, for example, analyzes the user's social media activity and collects related health information. For example, the collection unit can ask related questions based on health information shared by the user on social media. The collection unit can also estimate the user's stress level from the user's social media activity and ask related questions. The collection unit can also ask detailed questions based on symptoms mentioned by the user on social media. In this way, the related health information can be collected by analyzing the user's social media activity. Specific types and collection methods of social media activity can be realized, for example, by the content of posts, the number of likes, the number of followers, etc. In this way, the collection unit can analyze the user's social media activity and collect related health information.

[0067] The analysis unit can estimate the user's emotions and adjust the parameters of the analysis algorithm based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the parameters of the analysis algorithm based on the estimated user emotions. For example, the analysis unit increases the sensitivity of the analysis algorithm when the user is feeling anxious. The analysis unit can also set the sensitivity of the analysis algorithm to normal when the user is relaxed. The analysis unit can also prioritize the processing speed of the analysis algorithm when the user is in a hurry. This enables more appropriate analysis by adjusting the parameters of the analysis algorithm according to the user's emotions. Specific methods and standards for adjusting the parameters of the analysis algorithm are realized, for example, by a learning rate, a regularization parameter, etc. This allows the analysis unit to estimate the user's emotions and adjust the parameters of the analysis algorithm based on the estimated user emotions.

[0068] The analysis unit can improve the accuracy of the analysis by comparing the collected information with past medical data based on the collected information. The analysis unit, for example, improves the accuracy of the analysis by comparing the collected information with past medical data based on the collected information. For example, the analysis unit compares the collected information with past diagnostic data to improve the accuracy of the analysis. The analysis unit can also compare the collected information with past health check data to improve the accuracy of the analysis. The analysis unit can also compare the collected information with past medical history to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by comparing with past medical data. The specific type and collection method of past medical data can be realized, for example, by electronic medical records, medical records, etc. In this way, the analysis unit can improve the accuracy of the analysis by comparing the collected information with past medical data based on the collected information.

[0069] The analysis unit can correct the analysis results during analysis by taking into account the user's lifestyle habits and environmental information. The analysis unit, for example, corrects the analysis results during analysis by taking into account the user's lifestyle habits and environmental information. For example, if the user is a smoker, the analysis unit corrects the analysis results by taking into account risks associated with smoking. Furthermore, if the user is not getting enough exercise, the analysis unit can also correct the analysis results by taking into account risks associated with lack of exercise. Furthermore, if the user works in a high-stress environment, the analysis unit can also correct the analysis results by taking into account risks associated with stress. In this way, the analysis results can be corrected by taking into account the user's lifestyle habits and environmental information. Specific types and collection methods of lifestyle habits and environmental information are realized, for example, by dietary habits, exercise habits, living environment, etc. In this way, the analysis unit can correct the analysis results by taking into account the user's lifestyle habits and environmental information during analysis.

[0070] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit provides a simple and easy-to-understand display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Specific adjustment criteria and methods for the display method of the analysis results are realized, for example, by graph display, text display, etc. This allows the analysis unit to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions.

[0071] The analysis unit can evaluate region-specific disease risk by taking into account the user's geographical location information during analysis. For example, the analysis unit evaluates region-specific disease risk by taking into account the user's geographical location information during analysis. For example, if the user lives in a specific region, the analysis unit evaluates the disease risk prevalent in that region. Furthermore, if the user is traveling, the analysis unit can evaluate the disease risk specific to the region to which the user is traveling. Furthermore, if the user moves, the analysis unit can evaluate the disease risk in the new region. In this way, region-specific disease risk can be evaluated by taking into account the user's geographical location information. Specific evaluation methods and criteria for region-specific disease risk are realized, for example, using regional epidemiological data, environmental factors, etc. In this way, the analysis unit can evaluate region-specific disease risk by taking into account the user's geographical location information during analysis.

[0072] The analysis unit can complement the analysis results by referring to the user's social media activity during analysis. The analysis unit, for example, complements the analysis results by referring to the user's social media activity during analysis. For example, the analysis unit complements the analysis results based on health information shared by the user on social media. The analysis unit can also estimate the user's stress level from the user's social media activity and complement the analysis results. The analysis unit can also complement the analysis results based on symptoms mentioned by the user on social media. In this way, the analysis results can be complemented by referring to the user's social media activity. The specific types and collection methods of social media activity are realized by, for example, the content of posts, the number of likes, the number of followers, etc. In this way, the analysis unit can complement the analysis results by referring to the user's social media activity during analysis.

[0073] The estimation unit can estimate the user's emotions and adjust the algorithm for estimating the possibility of disease based on the estimated user emotions. The estimation unit, for example, estimates the user's emotions and adjusts the algorithm for estimating the possibility of disease based on the estimated user emotions. For example, the estimation unit estimates the possibility of disease higher when the user is feeling anxious. The estimation unit can also estimate the possibility of disease normally when the user is relaxed. The estimation unit can also quickly estimate the possibility of disease when the user is in a hurry. This enables more accurate estimation by adjusting the algorithm for estimating the possibility of disease according to the user's emotions. Specific adjustment methods and standards for the algorithm for estimating the possibility of disease are realized, for example, by a probability model, a diagnostic algorithm, etc. This allows the estimation unit to estimate the user's emotions and adjust the algorithm for estimating the possibility of disease based on the estimated user emotions.

[0074] The estimation unit can estimate the possibility of disease based on the collected information by comparing it with past medical data. The estimation unit, for example, estimates the possibility of disease based on the collected information by comparing it with past medical data. For example, the estimation unit compares the collected information with past diagnostic data to estimate the possibility of disease. The estimation unit can also compare the collected information with past health check data to estimate the possibility of disease. The estimation unit can also compare the collected information with past medical history to estimate the possibility of disease. In this way, by comparing it with past medical data, the possibility of disease can be more accurately estimated. The specific type and collection method of the past medical data can be realized, for example, by electronic medical records, medical records, etc. In this way, the estimation unit can estimate the possibility of disease based on the collected information by comparing it with past medical data.

[0075] The estimation unit can correct the estimation result by taking into account the user's lifestyle habits and environmental information at the time of estimation. The estimation unit, for example, corrects the estimation result by taking into account the user's lifestyle habits and environmental information at the time of estimation. For example, if the user is a smoker, the estimation unit corrects the estimation result by taking into account risks associated with smoking. Furthermore, if the user is not getting enough exercise, the estimation unit can also correct the estimation result by taking into account risks associated with not getting enough exercise. Furthermore, if the user works in a high-stress environment, the estimation unit can also correct the estimation result by taking into account risks associated with stress. In this way, the estimation result can be corrected by taking into account the user's lifestyle habits and environmental information. Specific types and collection methods of the lifestyle habits and environmental information are realized by, for example, eating habits, exercise habits, living environment, etc. In this way, the estimation unit can correct the estimation result by taking into account the user's lifestyle habits and environmental information at the time of estimation.

[0076] The estimation unit can estimate the user's emotion and adjust the display method of the estimation result based on the estimated user's emotion. The estimation unit, for example, estimates the user's emotion and adjusts the display method of the estimation result based on the estimated user's emotion. For example, if the user is feeling anxious, the estimation unit provides a simple and easy-to-understand display method. Furthermore, if the user is relaxed, the estimation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the estimation unit can provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the estimation result according to the user's emotion. Specific adjustment criteria and methods for the display method of the estimation result are realized, for example, as a graph display or a text display. This allows the estimation unit to estimate the user's emotion and adjust the display method of the estimation result based on the estimated user's emotion.

[0077] The estimation unit can evaluate the region-specific disease risk by taking into account the user's geographical location information during estimation. For example, the estimation unit evaluates the region-specific disease risk by taking into account the user's geographical location information during estimation. For example, if the user lives in a specific region, the estimation unit evaluates the disease risk prevalent in that region. Furthermore, if the user is traveling, the estimation unit can evaluate the disease risk specific to the region to which the user travels. Furthermore, if the user moves, the estimation unit can evaluate the disease risk in the new region. In this way, the region-specific disease risk can be evaluated by taking into account the user's geographical location information. Specific evaluation methods and criteria for the region-specific disease risk are realized, for example, by regional epidemiological data, environmental factors, etc. In this way, the estimation unit can evaluate the region-specific disease risk by taking into account the user's geographical location information during estimation.

[0078] The estimation unit can complement the estimation result by referring to the user's social media activity during estimation. For example, the estimation unit complements the estimation result by referring to the user's social media activity during estimation. For example, the estimation unit complements the estimation result based on health information shared by the user on social media. The estimation unit can also estimate a stress level from the user's social media activity and complement the estimation result. The estimation unit can also complement the estimation result based on symptoms mentioned by the user on social media. In this way, the estimation result can be complemented by referring to the user's social media activity. Specific types and collection methods of social media activity are realized by, for example, post content, number of likes, number of followers, etc. In this way, the estimation unit can complement the estimation result by referring to the user's social media activity during estimation.

[0079] The providing unit can estimate the user's emotion and adjust the way in which advice is expressed based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the way in which advice is expressed based on the estimated user's emotion. For example, if the user is feeling anxious, the providing unit can provide advice in gentle words. Furthermore, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is in a hurry, the providing unit can provide concise and quick advice. In this way, by adjusting the way in which advice is expressed according to the user's emotion, more appropriate advice can be provided. Specific criteria and methods for adjusting the way in which advice is expressed can be realized, for example, by text, audio, video, etc. In this way, the providing unit can estimate the user's emotion and adjust the way in which advice is expressed based on the estimated user's emotion.

[0080] The providing unit can provide specific advice based on the estimation result by referring to the user's past health data. The providing unit can provide specific advice based on the estimation result by referring to the user's past health data, for example. For example, the providing unit can provide specific advice based on the user's past diagnosis results. The providing unit can also provide specific advice by referring to the user's past health check results. The providing unit can also provide specific advice based on the user's past medical history. In this way, more specific advice can be provided by referring to the user's past health data. The specific type and collection method of the past health data can be realized, for example, by medical records, health check results, etc. In this way, the providing unit can provide specific advice based on the estimation result by referring to the user's past health data.

[0081] The providing unit can customize advice based on the estimation result, taking into account the user's lifestyle habits and environmental information. The providing unit customizes advice based on, for example, the estimation result, taking into account the user's lifestyle habits and environmental information. For example, if the user is a smoker, the providing unit can provide advice related to smoking. Furthermore, if the user is not getting enough exercise, the providing unit can also provide advice related to exercise. Furthermore, if the user works in a high-stress environment, the providing unit can also provide advice related to stress management. In this way, more personalized advice can be provided by taking into account the user's lifestyle habits and environmental information. Specific types and collection methods of lifestyle habits and environmental information are realized, for example, by dietary habits, exercise habits, living environment, etc. In this way, the providing unit can customize advice based on the estimation result, taking into account the user's lifestyle habits and environmental information.

[0082] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and determines the priority of advice based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can prioritize advice to reassure the user. Furthermore, if the user is relaxed, the providing unit can also prioritize detailed advice. Furthermore, if the user is in a hurry, the providing unit can also prioritize important advice. In this way, by determining the priority of advice according to the user's emotions, more appropriate advice can be provided. Specific criteria and methods for determining the priority of advice are realized, for example, by urgency, importance, etc. In this way, the providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions.

[0083] The providing unit can provide region-specific advice based on the estimation result, taking into account the user's geographical location information. The providing unit can provide region-specific advice based on the estimation result, taking into account the user's geographical location information, for example. For example, if the user lives in a specific region, the providing unit can provide advice about diseases prevalent in that region. Furthermore, if the user is traveling, the providing unit can provide advice about diseases specific to the region of the user's travel destination. Furthermore, if the user has moved, the providing unit can provide advice about disease risks in the new region. In this way, region-specific advice can be provided by taking into account the user's geographical location information. Specific content and criteria of the region-specific advice can be realized, for example, by information on local medical institutions, region-specific health risks, etc. In this way, the providing unit can provide region-specific advice based on the estimation result, taking into account the user's geographical location information.

[0084] The providing unit can complement advice based on the estimation result by referring to the user's social media activity. The providing unit, for example, complements advice based on the estimation result by referring to the user's social media activity. For example, the providing unit complements advice based on health information shared by the user on social media. The providing unit can also estimate a stress level from the user's social media activity and complement advice. The providing unit can also complement advice based on symptoms mentioned by the user on social media. In this way, advice can be complemented by referring to the user's social media activity. Specific types and collection methods of social media activity can be realized, for example, by post content, number of likes, number of followers, etc. In this way, the providing unit can complement advice based on the estimation result by referring to the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, estimation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information from the user using a generation AI of the smart device 14. The analysis unit analyzes the collected information, for example, by the specific processing unit 290 of the data processing device 12. The estimation unit estimates the possibility of disease, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides advice based on the estimation result, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, estimation 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 collection unit collects information from the user using a generation AI of the smart glasses 214. The analysis unit analyzes the collected information, for example, by the specific processing unit 290 of the data processing device 12. The estimation unit estimates the possibility of disease, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides advice based on the estimation result, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, estimation 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 collection unit collects information from the user using a generation AI of the headset type terminal 314. The analysis unit analyzes the collected information, for example, by the specific processing unit 290 of the data processing device 12. The estimation unit estimates the possibility of disease, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides advice based on the estimation result by, for example, the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, estimation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information from the user using the generation AI of the robot 414. The analysis unit analyzes the collected information, for example, by the specific processing unit 290 of the data processing device 12. The estimation unit estimates the possibility of disease, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides advice based on the estimation result by, for example, the control unit 46A of the robot 414.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The home interview tool can analyze the user's voice data and estimate the user's emotional state from the tone and speed of the voice. For example, if the user is nervous, they may speak quickly or in a higher tone. By analyzing such voice data, the user's emotional state can be grasped more accurately. Furthermore, based on the results of the voice data analysis, detailed questions can be asked if the user is relaxed, and simple questions can be started if the user is nervous. Furthermore, the results of the voice data analysis can be shared with other elements and reflected in the processing of the analysis and estimation units. This allows for flexible responses according to the user's emotional state.

[0087] The home medical questionnaire tool stores the user's health data on the cloud and can share it with other medical institutions. For example, when a user visits a medical institution, doctors can refer to the health data on the cloud, enabling faster and more accurate diagnoses. Furthermore, even if a user visits multiple medical institutions, sharing the data on the cloud allows them to receive consistent medical services. Furthermore, analyzing the data on the cloud makes it possible to understand the health status of an entire region and evaluate the risk of diseases specific to that region. This makes it possible to effectively utilize users' health data and improve the quality of medical services.

[0088] The home medical questionnaire tool can collect a user's dietary records and evaluate their nutritional balance. For example, when a user inputs their daily dietary information, the collection unit analyzes the data and evaluates whether they are lacking or overdosing on nutrients. The analysis unit can also estimate health risks based on the user's dietary records. For example, it can provide information that excessive fat intake may increase the risk of heart disease. Furthermore, the provision unit can provide specific advice for improving nutritional balance based on the user's dietary records. This allows the user to reduce health risks through daily dietary management.

[0089] The home questionnaire tool can collect a user's exercise data and evaluate their exercise habits. For example, when a user inputs the amount of exercise they do each day, the collection unit analyzes the data and evaluates the risk of insufficient or excessive exercise. The analysis unit can also estimate health risks based on the user's exercise data. For example, it can provide information that insufficient exercise may increase the risk of obesity or diabetes. Furthermore, the provision unit can provide specific advice on the appropriate amount and method of exercise based on the user's exercise data. This allows users to reduce health risks through daily exercise management.

[0090] The home questionnaire tool can collect a user's sleep data and evaluate their sleep quality. For example, when a user inputs their daily sleep duration and sleep quality, the collection unit analyzes the data and evaluates the risk of sleep deprivation or excessive sleep. The analysis unit can also estimate health risks based on the user's sleep data. For example, the analysis unit can provide information that lack of sleep may increase the risk of stress or a weakened immune system. Furthermore, the provision unit can provide specific advice for improving sleep quality based on the user's sleep data. This allows the user to reduce health risks through daily sleep management.

[0091] The home medical questionnaire tool can estimate the user's emotions and provide relaxing music and videos based on the estimated emotions. For example, if the user is feeling anxious, relaxing music can be provided to reduce the user's stress. Also, if the user is relaxed, music and videos that improve concentration can be provided. Furthermore, the providing unit can provide advice on appropriate relaxation methods and stress management methods according to the user's emotional state. This allows the user to control their emotions in their daily lives and maintain their health.

[0092] The home questionnaire tool can estimate the user's emotions and suggest relaxation techniques appropriate for the user based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxation techniques such as deep breathing or meditation. If the user is relaxed, it can also suggest yoga or light exercise. Furthermore, the providing unit can provide specific advice on how to perform relaxation techniques and their effects according to the user's emotional state. This allows the user to practice appropriate relaxation techniques in their daily lives and reduce stress.

[0093] The home medical questionnaire tool can estimate the user's emotions and provide health information appropriate to the user based on the estimated emotions. For example, if the user is feeling anxious, health information that gives a sense of security can be provided. Also, if the user is relaxed, detailed health information can be provided. Furthermore, the providing unit can adjust the method and content of providing health information according to the user's emotional state. This allows the user to obtain appropriate health information according to their emotional state, which can be useful for health management.

[0094] The home questionnaire tool can estimate the user's emotions and suggest an exercise program suitable for the user based on the estimated emotions. For example, if the user is feeling stressed, it can suggest yoga or stretching that has a relaxing effect. Also, if the user is relaxed, it can suggest an energetic exercise program. Furthermore, the providing unit can provide specific advice on how to implement the exercise program and its effects according to the user's emotional state. This allows the user to practice an exercise program appropriate for their emotional state and maintain their health.

[0095] The home medical questionnaire tool can estimate the user's emotions and suggest a meal plan suitable for the user based on the estimated emotions. For example, if the user is feeling stressed, the tool can suggest a meal plan using ingredients that have a relaxing effect. Also, if the user is relaxed, the tool can suggest a nutritionally balanced meal plan. Furthermore, the providing unit can provide specific advice on the content of the meal plan and how to implement it depending on the user's emotional state. This allows the user to implement an appropriate meal plan according to their emotional state and maintain their health.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The collection unit collects information from the user. For example, the collection unit uses the generation AI to conduct a formal interview in a conversational format. The generation AI analyzes the user's answers and collects the necessary information. For example, if the user feels unwell recently, the generation AI asks, "What symptoms do you have?" If the user answers, "I have headaches and dizziness," the generation AI asks additional questions such as, "How long have you had headaches?" and "When do you get dizzy?" to collect more detailed information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit includes, for example, an algorithm that estimates the possibility of disease based on the collected information. For example, the analysis unit causes the generation AI to estimate the possibility of disease based on the collected information. Step 3: The estimation unit estimates the possibility of disease based on the information analyzed by the analysis unit. For example, the estimation unit uses the generation AI to estimate the possibility of disease based on the collected information. Step 4: The providing unit provides advice based on the results of the estimation by the estimating unit. The providing unit provides specific advice based on the results of the estimation. For example, the providing unit provides advice such as "These symptoms may be due to ____. Please consult a medical institution as soon as possible."

[0098] 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.

[0099] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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, in order to avoid confusion and to 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 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 collection unit that collects information from users; an analysis unit that analyzes the information collected by the collection unit; an estimation unit that estimates the possibility of a disease based on the information analyzed by the analysis unit; a providing unit that provides advice based on the result estimated by the estimating unit. A system characterized by:

2. The collecting unit Conducting formal interviews in a conversational format using generative AI 2. The system of claim 1.

3. The analysis unit Includes algorithms that estimate the likelihood of disease based on collected information 2. The system of claim 1.

4. The providing unit Providing specific advice based on the results of the estimation 2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and adjust the content and order of questions in the medical interview based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Refer to the user's past health data to select the most appropriate questions 2. The system of claim 1.

7. The collecting unit Customize questions based on the user's lifestyle and environmental information 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the difficulty and detail of questions based on the estimated user emotions 2. The system of claim 1.

Images