System

Abstract

The system according to the embodiment aims to allow a user to make appropriate medical questions and contribute to health maintenance.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a question formation unit, a provision unit, and an advice unit. The collection unit collects information related to the lifestyle and health background of the user. The analysis unit analyzes the information collected by the collection unit. The question forming unit forms a medical question based on the information analyzed by the analyzing unit. The providing unit provides the question formed by the question forming unit to the user. The advice unit analyzes the question data provided by the providing unit and provides general medical advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies make it difficult for users to ask appropriate medical questions at hospitals and pharmacies, and there is room for improvement to contribute to maintaining health.

[0005] The system according to the embodiment aims to enable users to ask appropriate medical questions and contribute to maintaining their health. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a question formulation unit, a provision unit, and an advice unit. The collection unit collects information related to a user's lifestyle habits and health background. The analysis unit analyzes the information collected by the collection unit. The question formulation unit formulates a medical question based on the information analyzed by the analysis unit. The provision unit provides the question formulated by the question formulation unit to the user. The advice unit analyzes the question data provided by the provision unit and provides general medical advice. [Effects of the Invention]

[0007] The system according to the embodiment allows users to ask appropriate medical questions and contribute to maintaining their health. [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) A medical question proxy system according to an embodiment of the present invention is a system that formulates medical questions based on a user's lifestyle and health background and provides appropriate medical advice. The medical question proxy system collects information about the user's lifestyle and health background, and a generation AI analyzes the collected information to formulate medical questions for medical institutions. The formulated questions are provided as a guide for the user to ask doctors and pharmacists appropriate questions. Furthermore, the generation AI provides general medical advice based on the question data, contributing to the user's health maintenance. For example, the medical question proxy system collects detailed data about the user's daily activities, diet, exercise habits, etc. Next, the generation AI analyzes the collected information to formulate medical questions for medical institutions. For example, if a user complains of specific symptoms, the generation AI formulates questions related to those symptoms. The user can review the formulated questions before visiting a medical institution and ask a doctor based on that list. Furthermore, the generation AI analyzes the question data and provides appropriate medical advice. For example, if a user asks about a specific symptom, the generation AI provides general advice for that symptom. In this way, the medical question proxy system contributes to the user's health maintenance. This allows the medical question answering system to formulate medical questions based on the user's lifestyle and health background and provide appropriate medical advice. For example, users can ask appropriate questions at medical institutions and receive more appropriate advice from doctors and pharmacists. Users can also learn more about their own health conditions and obtain information useful for maintaining their health.

[0029] A medical question proxy system according to an embodiment includes a collection unit, an analysis unit, a question formulation unit, a provision unit, and an advice unit. The collection unit collects information related to a user's lifestyle and health background. The collection unit collects data such as the user's daily activities, diet, and exercise habits. The collection unit can collect information such as how much time the user spends exercising each day and what kind of food the user eats. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information, for example, to understand the user's lifestyle and health background. The analysis unit provides data for formulating a medical question for a medical institution based on the user's lifestyle and health background. The question formulation unit formulates a medical question based on the information analyzed by the analysis unit. For example, if the user complains of a specific symptom, the question formulation unit formulates a question related to the symptom. The question formulation unit uses a generation AI to formulate an appropriate medical question based on the user's lifestyle and health background. The provision unit provides the question formulated by the question formulation unit to the user. The providing unit, for example, enables the user to check the created question list before visiting a medical institution and ask a doctor questions based on the list. The advising unit analyzes the question data provided by the providing unit and provides general medical advice. For example, when a user asks about a specific symptom, the advising unit provides general advice for that symptom. As a result, the medical question proxy system according to the embodiment can create medical questions based on the user's lifestyle habits and health background and provide appropriate medical advice.

[0030] The collection unit can collect data on the user's daily activities, diet, and exercise habits. The collection unit, for example, collects the user's daily activities. For example, the collection unit can collect activity data such as the user's commute, housework, hobbies, etc. The collection unit can also collect the user's diet data. For example, the collection unit can collect data such as the types, frequency, and nutritional balance of the meals the user eats. Furthermore, the collection unit can also collect the user's exercise habit data. For example, the collection unit can collect data such as the types, frequency, and intensity of the exercise the user performs. In this way, the collection unit can understand the user's lifestyle habits by collecting data such as the user's daily activities, diet, and exercise habits.

[0031] The analysis unit analyzes the collected information and can grasp the user's lifestyle habits and health background. The analysis unit, for example, analyzes the collected information. For example, the analysis unit can analyze the collected information to grasp the user's lifestyle habits and health background. The analysis unit can also analyze health background data such as the user's past medical history, family history, and allergy information. Furthermore, the analysis unit can analyze lifestyle habit data such as the user's diet, exercise, and sleep. In this way, the analysis unit can grasp the user's lifestyle habits and health background by analyzing the collected information.

[0032] The question formation unit can form medical questions to be asked at a medical institution based on the analysis results. The question formation unit, for example, forms medical questions to be asked at a medical institution based on the analysis results. For example, if a user complains of a specific symptom, the question formation unit can form questions related to the symptom. The question formation unit can also form appropriate questions for a medical examination at a medical institution based on the user's lifestyle habits and health background. Furthermore, the question formation unit can form questions related to tests depending on the user's health condition. As a result, the question formation unit can ask appropriate questions by forming medical questions to be asked at a medical institution based on the analysis results.

[0033] The providing unit can provide the formed questions to the user as a guide for asking questions to a doctor or pharmacist. The providing unit, for example, provides the formed questions to the user. For example, the providing unit enables the user to check the formed question list before going to a medical institution and ask questions to a doctor based on the list. The providing unit can also provide the question list as a guide for asking appropriate questions when the user asks a question to a pharmacist at a pharmacy. Furthermore, the providing unit can also provide the question list as a guide for asking appropriate questions when the user has an online medical consultation. In this way, the providing unit can provide the formed questions to the user, allowing the user to ask appropriate questions to a doctor or pharmacist.

[0034] The advice unit can analyze the question data and provide general medical advice. The advice unit, for example, analyzes the question data. For example, when a user asks about a specific symptom, the advice unit can provide general advice for that symptom. The advice unit can also provide advice for health management based on the user's lifestyle habits and health background. Furthermore, the advice unit can analyze the user's question data and provide advice on preventive measures. In this way, the advice unit can provide general medical advice by analyzing the question data.

[0035] The collection unit can analyze the user's past health checkup results and select the type of data to collect. The collection unit, for example, analyzes the user's past health checkup results. For example, the collection unit can analyze data such as the user's past blood test results and electrocardiogram results. The collection unit can also prioritize the collection of data related to specific health risks based on the past health checkup results. Furthermore, the collection unit can customize required data items based on the user's past health checkup results. For example, the collection unit can strengthen data collection for a specific period by referring to the user's past health checkup results. This allows the collection unit to select the type of data to collect by analyzing the past health checkup results.

[0036] The collection unit can filter data based on the user's current health condition and living environment when collecting data. The collection unit, for example, takes into account the user's current health condition when collecting data. For example, if the user is currently in poor health, the collection unit can prioritize collecting data related to the user's health condition. The collection unit can also take into account the user's living environment. For example, the collection unit can collect relevant data based on the climate of the area in which the user lives. Furthermore, the collection unit can adjust the type and amount of data to be collected depending on the user's current health condition. For example, the collection unit can collect data such as the user's body temperature, blood pressure, and heart rate. In this way, the collection unit can collect highly relevant data by filtering data based on the user's current health condition and living environment.

[0037] The collection unit can select a collection means according to the user's input method at the time of collection. The collection unit selects a collection means according to, for example, the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the user prefers text input, the collection unit can also preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can also preferentially collect image data. In this way, the collection unit can efficiently collect data by selecting the optimal collection means according to the user's input method.

[0038] During collection, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, takes into account the user's geographical location information. For example, if the user lives in a particular area, the collection unit can prioritize collecting data related to health risks in that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to health risks at the travel destination. Furthermore, if the user frequently visits a particular area, the collection unit can prioritize collecting data related to health risks in that area. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information.

[0039] At the time of collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit can collect related data based on health information shared by the user on social media. The collection unit can also extract health-related topics from the user's social media activities and collect data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, the collection unit can collect related data by analyzing social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, reflects the user's past feedback. For example, the collection unit can adjust the type of data to be collected based on feedback provided by the user in the past. The collection unit can also improve the collection method from the user's past feedback and collect data more efficiently. Furthermore, the collection unit can optimize the collection timing by referring to the user's past feedback. In this way, the collection unit can customize the collection method by reflecting the user's past feedback and collect data efficiently.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the collected data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. The analysis unit applies different analysis algorithms depending on, for example, the category of data. For example, the analysis unit can apply an analysis algorithm specialized for health to health data. The analysis unit can also apply an analysis algorithm specialized for lifestyle habits to lifestyle habit data. Furthermore, the analysis unit can also apply an analysis algorithm specialized for diet to diet data. In this way, the analysis unit can perform more accurate analysis by applying different analysis algorithms depending on the category of data.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm by referring to data such as the user's past diagnosis results and analysis reports. The analysis unit can also set parameters for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit determines the analysis priority based on, for example, the time when the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also determine the analysis priority by referring to past data. Furthermore, the analysis unit can adjust the analysis priority based on the time when the data was collected. This allows the analysis unit to perform analysis efficiently by determining the analysis priority based on the time when the data was collected.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, the analysis unit can perform analysis efficiently by adjusting the order of analysis based on the relevance of the data.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, the analysis unit can provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0047] The question formation unit can adjust the level of detail of the question based on the importance of the analysis result when forming a question. The question formation unit adjusts the level of detail of the question based on, for example, the importance of the analysis result. For example, the question formation unit can provide a detailed question for an analysis result with a high level of importance. The question formation unit can also provide a simplified question for an analysis result with a low level of importance. Furthermore, the question formation unit can adjust the level of detail of the question depending on the importance of the analysis result. In this way, the question formation unit can efficiently form questions by adjusting the level of detail of the question based on the importance of the analysis result.

[0048] The question formation unit can apply different question algorithms depending on the symptom category when forming a question. The question formation unit applies different question algorithms depending on, for example, the symptom category. For example, the question formation unit can apply a health-specific question algorithm to a question about health. Furthermore, the question formation unit can also apply a lifestyle-specific question algorithm to a question about lifestyle. Furthermore, the question formation unit can also apply a diet-specific question algorithm to a question about diet. In this way, the question formation unit can provide more appropriate questions by applying different question algorithms depending on the symptom category.

[0049] When formulating a question, the question formulation unit can improve the accuracy of the question by referring to the user's past question results. The question formulation unit, for example, refers to the user's past question results. For example, the question formulation unit can adjust the question algorithm by referring to data such as the user's past answers and diagnosis results. The question formulation unit can also set parameters for improving the accuracy of the question from the user's past question results. Furthermore, the question formulation unit can also improve the accuracy of the question by referring to the user's past question results. In this way, the question formulation unit can improve the accuracy of the question by referring to the past question results.

[0050] The question formation unit can determine the priority of questions based on the time of symptom onset when forming questions. The question formation unit determines the priority of questions based on, for example, the time of symptom onset. For example, the question formation unit can provide questions preferentially for symptoms that have recently occurred. The question formation unit can also provide questions later for symptoms that have occurred in the past. Furthermore, the question formation unit can adjust the priority of questions based on the time of symptom onset. In this way, the question formation unit can efficiently provide questions by determining the priority of questions based on the time of symptom onset.

[0051] The question formation unit can adjust the order of questions based on the relevance of symptoms when forming questions. The question formation unit adjusts the order of questions based on, for example, the relevance of symptoms. For example, the question formation unit can provide questions preferentially for highly relevant symptoms. The question formation unit can also provide questions later for less relevant symptoms. Furthermore, the question formation unit can also adjust the order of questions based on the relevance of symptoms. In this way, the question formation unit can efficiently provide questions by adjusting the order of questions based on the relevance of symptoms.

[0052] The question formation unit can adjust the use of technical terms in the question according to the user's level of expertise when forming a question. The question formation unit adjusts the use of technical terms in the question according to, for example, the user's level of expertise. For example, if the user has technical knowledge, the question formation unit can provide a question that uses a lot of technical terms. Also, if the user does not have technical knowledge, the question formation unit can provide a question in simple language. Furthermore, the question formation unit can adjust the use of technical terms in the question according to the user's level of expertise. In this way, the question formation unit can provide a question that is easier to understand by adjusting the use of technical terms in the question according to the user's level of expertise.

[0053] At the time of providing, the providing unit can select the optimal providing method by referring to the user's past question history. The providing unit, for example, refers to the user's past question history. For example, the providing unit can select the optimal providing method by referring to data such as the user's past question content and answer history. The providing unit can also customize the providing method from the user's past question history. Furthermore, the providing unit can improve the providing method by referring to the user's past question history. In this way, the providing unit can select the optimal providing method by referring to the past question history.

[0054] The providing unit can customize the content to be provided according to the user's current health condition when providing the information. The providing unit, for example, takes into account the user's current health condition. For example, if the user is currently in poor health, the providing unit can prioritize providing information related to the user's health condition. The providing unit can also customize the content to be provided according to the user's current health condition. Furthermore, the providing unit can adjust the content to be provided taking into account the user's current health condition. In this way, the providing unit can provide more appropriate information by customizing the content to be provided according to the user's current health condition.

[0055] The providing unit can improve the providing method by reflecting the user's feedback when providing information. The providing unit, for example, reflects the user's feedback. For example, the providing unit can improve the providing method based on the user's feedback. The providing unit can also customize the providing method based on the user's feedback. Furthermore, the providing unit can optimize the providing method by referring to the user's feedback. In this way, the providing unit can improve the providing method by reflecting the feedback and provide more appropriate information.

[0056] The providing unit can select a providing method based on the user's geographical location information when providing information. The providing unit, for example, takes into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can prioritize providing information related to health risks in that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to health risks at the travel destination. Furthermore, if the user frequently visits a specific area, the providing unit can prioritize providing information related to health risks in that area. In this way, the providing unit can select the optimal providing method by taking into account the geographical location information.

[0057] The providing unit can customize the content to be provided by analyzing the user's social media activity at the time of providing the information. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit can provide related information based on health information shared by the user on social media. The providing unit can also extract health-related topics from the user's social media activity and provide the information. Furthermore, the providing unit can provide related information by referring to the activity of the user's friends on social media. In this way, the providing unit can customize the content to be provided and provide more appropriate information by analyzing the social media activity.

[0058] The providing unit can customize the providing method by reflecting the user's past feedback when providing information. The providing unit, for example, reflects the user's past feedback. For example, the providing unit can customize the providing method based on the user's past feedback. The providing unit can also improve the providing method from the user's past feedback. Furthermore, the providing unit can also optimize the providing method by referring to the user's past feedback. In this way, the providing unit can customize the providing method by reflecting the user's past feedback and provide more appropriate information.

[0059] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the question data. The advice unit adjusts the level of detail of the advice based on, for example, the importance of the question data. For example, the advice unit can provide detailed advice for question data with a high level of importance. The advice unit can also provide simplified advice for question data with a low level of importance. Furthermore, the advice unit can adjust the level of detail of the advice depending on the importance of the question data. In this way, the advice unit can efficiently provide advice by adjusting the level of detail of the advice based on the importance of the question data.

[0060] When providing advice, the advice unit can apply different advice algorithms depending on the category of the question. For example, the advice unit can apply different advice algorithms depending on the category of the question. For example, the advice unit can apply an advice algorithm specialized for health to a question about health. Furthermore, the advice unit can also apply an advice algorithm specialized for lifestyle habits to a question about lifestyle habits. Furthermore, the advice unit can also apply an advice algorithm specialized for diet to a question about diet. In this way, the advice unit can provide more appropriate advice by applying different advice algorithms depending on the category of the question.

[0061] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past advice results. The advice unit, for example, refers to the user's past advice results. For example, the advice unit can adjust the advice algorithm by referring to data such as the user's past advice content and the user's reaction. The advice unit can also set parameters for improving the accuracy of the advice from the user's past advice results. Furthermore, the advice unit can also improve the accuracy of the advice by referring to the user's past advice results. In this way, the advice unit can improve the accuracy of the advice by referring to the past advice results.

[0062] When providing advice, the advice unit can determine the priority of advice based on the time when the question was asked. The advice unit determines the priority of advice based, for example, on the time when the question was asked. For example, the advice unit can provide advice preferentially for questions that have recently arisen. The advice unit can also provide advice later for questions that have arisen in the past. Furthermore, the advice unit can adjust the priority of advice based on the time when the question was asked. In this way, the advice unit can provide advice efficiently by determining the priority of advice based on the time when the question was asked.

[0063] When providing advice, the advice unit can adjust the order of advice based on the relevance of the questions. The advice unit adjusts the order of advice based on, for example, the relevance of the questions. For example, the advice unit can provide advice preferentially for highly relevant questions. The advice unit can also provide advice later for less relevant questions. Furthermore, the advice unit can adjust the order of advice based on the relevance of the questions. In this way, the advice unit can provide advice efficiently by adjusting the order of advice based on the relevance of the questions.

[0064] When providing advice, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. The advice unit, for example, adjusts the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical knowledge, the advice unit can provide advice that uses a lot of technical terms. Also, if the user does not have technical knowledge, the advice unit can provide advice in simple language. Furthermore, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. In this way, the advice unit can provide advice that is easier to understand by adjusting the use of technical terms in the advice according to the user's level of expertise.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze the user's social media activities and collect health-related data. For example, related data can be collected based on health information shared by the user on social media. It can also extract health-related topics from the user's social media activities and collect data. It can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data by analyzing social media activities.

[0067] The question formulation unit can improve the accuracy of questions by referring to the user's past question results. For example, the question formulation unit can adjust the question algorithm by referring to data such as the user's past answers and diagnosis results. In addition, parameters for improving the accuracy of questions can be set based on the user's past question results. Furthermore, the accuracy of questions can also be improved by referring to the user's past question results. In this way, the accuracy of questions can be improved by referring to the past question results.

[0068] The collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, if the user lives in a specific area, data related to health risks in that area can be prioritized. Also, if the user is traveling, data related to health risks at the travel destination can be prioritized. Furthermore, if the user frequently visits a specific area, data related to health risks in that area can be prioritized. In this way, highly relevant data can be prioritized by taking geographical location information into consideration.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, an analysis algorithm specialized for health can be applied to health data. Also, an analysis algorithm specialized for lifestyle habit can be applied to lifestyle habit data. Furthermore, an analysis algorithm specialized for diet can be applied to diet data. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis can be performed.

[0070] The question formation unit can determine the priority of questions based on the time of symptom onset when forming questions. For example, questions can be provided preferentially for symptoms that have recently occurred. Also, questions can be provided later for symptoms that have occurred in the past. Furthermore, the priority of questions can be adjusted based on the time of symptom onset. In this way, by determining the priority of questions based on the time of symptom onset, questions can be provided efficiently.

[0071] When providing the information, the providing unit can select the optimal providing method by referring to the user's past question history. For example, the optimal providing method can be selected by referring to data such as the user's past question content and answer history. The providing method can also be customized based on the user's past question history. Furthermore, the providing method can also be improved by referring to the user's past question history. In this way, the optimal providing method can be selected by referring to the past question history.

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

[0073] Step 1: The collection unit collects information about the user's lifestyle and health background. For example, it collects data about the user's daily activities, diet, exercise habits, etc. The collection unit can collect information such as how much time the user spends exercising each day and what kind of food they eat. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to understand the user's lifestyle habits and health background. The analysis unit provides data for formulating medical questions at a medical institution based on the user's lifestyle habits and health background. Step 3: The question generator generates medical questions based on the information analyzed by the analyzer. For example, if the user complains of a specific symptom, the generator generates questions related to that symptom. The question generator uses a generation AI to generate appropriate medical questions based on the user's lifestyle and health background. Step 4: The providing unit provides the questions formed by the question forming unit to the user. For example, before going to a medical institution, the user can check the formed question list and ask a doctor questions based on the list. Step 5: The advice unit analyzes the question data provided by the provision unit and provides general medical advice. For example, if the user asks about a specific symptom, the advice unit provides general advice for that symptom.

[0074] (Example 2) A medical question proxy system according to an embodiment of the present invention is a system that formulates medical questions based on a user's lifestyle and health background and provides appropriate medical advice. The medical question proxy system collects information about the user's lifestyle and health background, and a generation AI analyzes the collected information to formulate medical questions for medical institutions. The formulated questions are provided as a guide for the user to ask doctors and pharmacists appropriate questions. Furthermore, the generation AI provides general medical advice based on the question data, contributing to the user's health maintenance. For example, the medical question proxy system collects detailed data about the user's daily activities, diet, exercise habits, etc. Next, the generation AI analyzes the collected information to formulate medical questions for medical institutions. For example, if a user complains of specific symptoms, the generation AI formulates questions related to those symptoms. The user can review the formulated questions before visiting a medical institution and ask a doctor based on that list. Furthermore, the generation AI analyzes the question data and provides appropriate medical advice. For example, if a user asks about a specific symptom, the generation AI provides general advice for that symptom. In this way, the medical question proxy system contributes to the user's health maintenance. This allows the medical question answering system to formulate medical questions based on the user's lifestyle and health background and provide appropriate medical advice. For example, users can ask appropriate questions at medical institutions and receive more appropriate advice from doctors and pharmacists. Users can also learn more about their own health conditions and obtain information useful for maintaining their health.

[0075] A medical question proxy system according to an embodiment includes a collection unit, an analysis unit, a question formulation unit, a provision unit, and an advice unit. The collection unit collects information related to a user's lifestyle and health background. The collection unit collects data such as the user's daily activities, diet, and exercise habits. The collection unit can collect information such as how much time the user spends exercising each day and what kind of food the user eats. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information, for example, to understand the user's lifestyle and health background. The analysis unit provides data for formulating a medical question for a medical institution based on the user's lifestyle and health background. The question formulation unit formulates a medical question based on the information analyzed by the analysis unit. For example, if the user complains of a specific symptom, the question formulation unit formulates a question related to the symptom. The question formulation unit uses a generation AI to formulate an appropriate medical question based on the user's lifestyle and health background. The provision unit provides the question formulated by the question formulation unit to the user. The providing unit, for example, enables the user to check the created question list before visiting a medical institution and ask a doctor questions based on the list. The advising unit analyzes the question data provided by the providing unit and provides general medical advice. For example, when a user asks about a specific symptom, the advising unit provides general advice for that symptom. As a result, the medical question proxy system according to the embodiment can create medical questions based on the user's lifestyle habits and health background and provide appropriate medical advice.

[0076] The collection unit can collect data on the user's daily activities, diet, and exercise habits. The collection unit, for example, collects the user's daily activities. For example, the collection unit can collect activity data such as the user's commute, housework, hobbies, etc. The collection unit can also collect the user's diet data. For example, the collection unit can collect data such as the types, frequency, and nutritional balance of the meals the user eats. Furthermore, the collection unit can also collect the user's exercise habit data. For example, the collection unit can collect data such as the types, frequency, and intensity of the exercise the user performs. In this way, the collection unit can understand the user's lifestyle habits by collecting data such as the user's daily activities, diet, and exercise habits.

[0077] The analysis unit analyzes the collected information and can grasp the user's lifestyle habits and health background. The analysis unit, for example, analyzes the collected information. For example, the analysis unit can analyze the collected information to grasp the user's lifestyle habits and health background. The analysis unit can also analyze health background data such as the user's past medical history, family history, and allergy information. Furthermore, the analysis unit can analyze lifestyle habit data such as the user's diet, exercise, and sleep. In this way, the analysis unit can grasp the user's lifestyle habits and health background by analyzing the collected information.

[0078] The question formation unit can form medical questions to be asked at a medical institution based on the analysis results. The question formation unit, for example, forms medical questions to be asked at a medical institution based on the analysis results. For example, if a user complains of a specific symptom, the question formation unit can form questions related to the symptom. The question formation unit can also form appropriate questions for a medical examination at a medical institution based on the user's lifestyle habits and health background. Furthermore, the question formation unit can form questions related to tests depending on the user's health condition. As a result, the question formation unit can ask appropriate questions by forming medical questions to be asked at a medical institution based on the analysis results.

[0079] The providing unit can provide the formed questions to the user as a guide for asking questions to a doctor or pharmacist. The providing unit, for example, provides the formed questions to the user. For example, the providing unit enables the user to check the formed question list before going to a medical institution and ask questions to a doctor based on the list. The providing unit can also provide the question list as a guide for asking appropriate questions when the user asks a question to a pharmacist at a pharmacy. Furthermore, the providing unit can also provide the question list as a guide for asking appropriate questions when the user has an online medical consultation. In this way, the providing unit can provide the formed questions to the user, allowing the user to ask appropriate questions to a doctor or pharmacist.

[0080] The advice unit can analyze the question data and provide general medical advice. The advice unit, for example, analyzes the question data. For example, when a user asks about a specific symptom, the advice unit can provide general advice for that symptom. The advice unit can also provide advice for health management based on the user's lifestyle habits and health background. Furthermore, the advice unit can analyze the user's question data and provide advice on preventive measures. In this way, the advice unit can provide general medical advice by analyzing the question data.

[0081] The collection unit can estimate the user's emotions and adjust the timing of collecting lifestyle habit data based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can adjust the timing of collecting lifestyle habit data based on the user's emotions. For example, if the user is feeling stressed, the collection unit can collect data during a time period when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can perform collection for a long period of time to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can collect the minimum amount of data necessary in a short period of time. In this way, the collection unit can collect more appropriate data by adjusting the timing of data collection based on the user's emotions.

[0082] The collection unit can analyze the user's past health checkup results and select the type of data to collect. The collection unit, for example, analyzes the user's past health checkup results. For example, the collection unit can analyze data such as the user's past blood test results and electrocardiogram results. The collection unit can also prioritize the collection of data related to specific health risks based on the past health checkup results. Furthermore, the collection unit can customize required data items based on the user's past health checkup results. For example, the collection unit can strengthen data collection for a specific period by referring to the user's past health checkup results. This allows the collection unit to select the type of data to collect by analyzing the past health checkup results.

[0083] The collection unit can filter data based on the user's current health condition and living environment when collecting data. The collection unit, for example, takes into account the user's current health condition when collecting data. For example, if the user is currently in poor health, the collection unit can prioritize collecting data related to the user's health condition. The collection unit can also take into account the user's living environment. For example, the collection unit can collect relevant data based on the climate of the area in which the user lives. Furthermore, the collection unit can adjust the type and amount of data to be collected depending on the user's current health condition. For example, the collection unit can collect data such as the user's body temperature, blood pressure, and heart rate. In this way, the collection unit can collect highly relevant data by filtering data based on the user's current health condition and living environment.

[0084] The collection unit can select a collection means according to the user's input method at the time of collection. The collection unit selects a collection means according to, for example, the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Furthermore, if the user prefers text input, the collection unit can also preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can also preferentially collect image data. In this way, the collection unit can efficiently collect data by selecting the optimal collection means according to the user's input method.

[0085] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. For example, the collection unit can estimate the user's emotion using facial expression recognition technology. The collection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the collection unit can determine the priority of data to be collected based on the user's emotion. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data related to hurry. In this way, the collection unit can prioritize collecting important data by determining the priority of data based on the user's emotion.

[0086] During collection, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, takes into account the user's geographical location information. For example, if the user lives in a particular area, the collection unit can prioritize collecting data related to health risks in that area. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to health risks at the travel destination. Furthermore, if the user frequently visits a particular area, the collection unit can prioritize collecting data related to health risks in that area. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information.

[0087] At the time of collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit can collect related data based on health information shared by the user on social media. The collection unit can also extract health-related topics from the user's social media activities and collect data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, the collection unit can collect related data by analyzing social media activities.

[0088] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, reflects the user's past feedback. For example, the collection unit can adjust the type of data to be collected based on feedback provided by the user in the past. The collection unit can also improve the collection method from the user's past feedback and collect data more efficiently. Furthermore, the collection unit can optimize the collection timing by referring to the user's past feedback. In this way, the collection unit can customize the collection method by reflecting the user's past feedback and collect data efficiently.

[0089] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the analysis unit can adjust the way the analysis is presented based on the user's emotions. For example, the analysis unit can provide a simple, highly visible analysis result when the user is nervous. Furthermore, the analysis unit can provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a concise analysis result when the user is in a hurry. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the way the analysis is presented based on the user's emotions.

[0090] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the collected data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. The analysis unit applies different analysis algorithms depending on, for example, the category of data. For example, the analysis unit can apply an analysis algorithm specialized for health to health data. The analysis unit can also apply an analysis algorithm specialized for lifestyle habits to lifestyle habit data. Furthermore, the analysis unit can also apply an analysis algorithm specialized for diet to diet data. In this way, the analysis unit can perform more accurate analysis by applying different analysis algorithms depending on the category of data.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm by referring to data such as the user's past diagnosis results and analysis reports. The analysis unit can also set parameters for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results.

[0093] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. Furthermore, the analysis unit can adjust the length of the analysis based on the user's emotion. For example, the analysis unit can provide a short and to-the-point analysis result when the user is in a hurry. Furthermore, the analysis unit can provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide an analysis result with visually stimulating effects when the user is excited. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis based on the user's emotion.

[0094] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit determines the analysis priority based on, for example, the time when the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also determine the analysis priority by referring to past data. Furthermore, the analysis unit can adjust the analysis priority based on the time when the data was collected. This allows the analysis unit to perform analysis efficiently by determining the analysis priority based on the time when the data was collected.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, the analysis unit can perform analysis efficiently by adjusting the order of analysis based on the relevance of the data.

[0096] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, the analysis unit can provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0097] The question formulation unit can estimate the user's emotions and adjust the way the question is phrased based on the estimated user's emotions. The question formulation unit, for example, estimates the user's emotions. For example, the question formulation unit can estimate the user's emotions using facial expression recognition technology. The question formulation unit can also estimate the user's emotions using voice analysis technology. Furthermore, the question formulation unit can adjust the way the question is phrased based on the user's emotions. For example, the question formulation unit can provide a simple, highly visible question when the user is nervous. Furthermore, the question formulation unit can provide a detailed question when the user is relaxed. Furthermore, the question formulation unit can provide a question that gets to the point when the user is in a hurry. In this way, the question formulation unit can provide a more appropriate question by adjusting the way the question is phrased based on the user's emotions.

[0098] The question formation unit can adjust the level of detail of the question based on the importance of the analysis result when forming a question. The question formation unit adjusts the level of detail of the question based on, for example, the importance of the analysis result. For example, the question formation unit can provide a detailed question for an analysis result with a high level of importance. The question formation unit can also provide a simplified question for an analysis result with a low level of importance. Furthermore, the question formation unit can adjust the level of detail of the question depending on the importance of the analysis result. In this way, the question formation unit can efficiently form questions by adjusting the level of detail of the question based on the importance of the analysis result.

[0099] The question formation unit can apply different question algorithms depending on the symptom category when forming a question. The question formation unit applies different question algorithms depending on, for example, the symptom category. For example, the question formation unit can apply a health-specific question algorithm to a question about health. Furthermore, the question formation unit can also apply a lifestyle-specific question algorithm to a question about lifestyle. Furthermore, the question formation unit can also apply a diet-specific question algorithm to a question about diet. In this way, the question formation unit can provide more appropriate questions by applying different question algorithms depending on the symptom category.

[0100] When formulating a question, the question formulation unit can improve the accuracy of the question by referring to the user's past question results. The question formulation unit, for example, refers to the user's past question results. For example, the question formulation unit can adjust the question algorithm by referring to data such as the user's past answers and diagnosis results. The question formulation unit can also set parameters for improving the accuracy of the question from the user's past question results. Furthermore, the question formulation unit can also improve the accuracy of the question by referring to the user's past question results. In this way, the question formulation unit can improve the accuracy of the question by referring to the past question results.

[0101] The question formation unit can estimate the user's emotions and adjust the length of the questions based on the estimated user's emotions. The question formation unit, for example, estimates the user's emotions. For example, the question formation unit can estimate the user's emotions using facial expression recognition technology. The question formation unit can also estimate the user's emotions using voice analysis technology. Furthermore, the question formation unit can adjust the length of the questions based on the user's emotions. For example, the question formation unit can provide a short and to-the-point question when the user is in a hurry. Furthermore, the question formation unit can provide a detailed question when the user is relaxed. Furthermore, the question formation unit can provide a question with a visually stimulating effect when the user is excited. In this way, the question formation unit can provide more appropriate questions by adjusting the length of the questions based on the user's emotions.

[0102] The question formation unit can determine the priority of questions based on the time of symptom onset when forming questions. The question formation unit determines the priority of questions based on, for example, the time of symptom onset. For example, the question formation unit can provide questions preferentially for symptoms that have recently occurred. The question formation unit can also provide questions later for symptoms that have occurred in the past. Furthermore, the question formation unit can adjust the priority of questions based on the time of symptom onset. In this way, the question formation unit can efficiently provide questions by determining the priority of questions based on the time of symptom onset.

[0103] The question formation unit can adjust the order of questions based on the relevance of symptoms when forming questions. The question formation unit adjusts the order of questions based on, for example, the relevance of symptoms. For example, the question formation unit can provide questions preferentially for highly relevant symptoms. The question formation unit can also provide questions later for less relevant symptoms. Furthermore, the question formation unit can also adjust the order of questions based on the relevance of symptoms. In this way, the question formation unit can efficiently provide questions by adjusting the order of questions based on the relevance of symptoms.

[0104] The question formation unit can adjust the use of technical terms in the question according to the user's level of expertise when forming a question. The question formation unit adjusts the use of technical terms in the question according to, for example, the user's level of expertise. For example, if the user has technical knowledge, the question formation unit can provide a question that uses a lot of technical terms. Also, if the user does not have technical knowledge, the question formation unit can provide a question in simple language. Furthermore, the question formation unit can adjust the use of technical terms in the question according to the user's level of expertise. In this way, the question formation unit can provide a question that is easier to understand by adjusting the use of technical terms in the question according to the user's level of expertise.

[0105] The providing unit can estimate the user's emotions and adjust the way in which questions are presented based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the providing unit can adjust the way in which questions are presented based on the user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible way to present questions. Furthermore, if the user is relaxed, the providing unit can provide a way to present questions that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a way to present questions that focuses on the main points. In this way, the providing unit can provide more appropriate questions by adjusting the way in which questions are presented based on the user's emotions.

[0106] At the time of providing, the providing unit can select the optimal providing method by referring to the user's past question history. The providing unit, for example, refers to the user's past question history. For example, the providing unit can select the optimal providing method by referring to data such as the user's past question content and answer history. The providing unit can also customize the providing method from the user's past question history. Furthermore, the providing unit can improve the providing method by referring to the user's past question history. In this way, the providing unit can select the optimal providing method by referring to the past question history.

[0107] The providing unit can customize the content to be provided according to the user's current health condition when providing the information. The providing unit, for example, takes into account the user's current health condition. For example, if the user is currently in poor health, the providing unit can prioritize providing information related to the user's health condition. The providing unit can also customize the content to be provided according to the user's current health condition. Furthermore, the providing unit can adjust the content to be provided taking into account the user's current health condition. In this way, the providing unit can provide more appropriate information by customizing the content to be provided according to the user's current health condition.

[0108] The providing unit can improve the providing method by reflecting the user's feedback when providing information. The providing unit, for example, reflects the user's feedback. For example, the providing unit can improve the providing method based on the user's feedback. The providing unit can also customize the providing method based on the user's feedback. Furthermore, the providing unit can optimize the providing method by referring to the user's feedback. In this way, the providing unit can improve the providing method by reflecting the feedback and provide more appropriate information.

[0109] The providing unit can estimate the user's emotions and adjust the order in which questions are presented based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the providing unit can adjust the order in which questions are presented based on the user's emotions. For example, if the user is nervous, the providing unit can prioritize providing simple, highly visible questions. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed questions. Furthermore, if the user is in a hurry, the providing unit can prioritize providing questions that get to the point. In this way, the providing unit can provide more appropriate questions by adjusting the order in which questions are presented based on the user's emotions.

[0110] The providing unit can select a providing method based on the user's geographical location information when providing information. The providing unit, for example, takes into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can prioritize providing information related to health risks in that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to health risks at the travel destination. Furthermore, if the user frequently visits a specific area, the providing unit can prioritize providing information related to health risks in that area. In this way, the providing unit can select the optimal providing method by taking into account the geographical location information.

[0111] The providing unit can customize the content to be provided by analyzing the user's social media activity at the time of providing the information. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit can provide related information based on health information shared by the user on social media. The providing unit can also extract health-related topics from the user's social media activity and provide the information. Furthermore, the providing unit can provide related information by referring to the activity of the user's friends on social media. In this way, the providing unit can customize the content to be provided and provide more appropriate information by analyzing the social media activity.

[0112] The providing unit can customize the providing method by reflecting the user's past feedback when providing information. The providing unit, for example, reflects the user's past feedback. For example, the providing unit can customize the providing method based on the user's past feedback. The providing unit can also improve the providing method from the user's past feedback. Furthermore, the providing unit can also optimize the providing method by referring to the user's past feedback. In this way, the providing unit can customize the providing method by reflecting the user's past feedback and provide more appropriate information.

[0113] The advice unit can estimate the user's emotion and adjust the way in which advice is expressed based on the estimated user's emotion. The advice unit, for example, estimates the user's emotion. For example, the advice unit can estimate the user's emotion using facial expression recognition technology. The advice unit can also estimate the user's emotion using voice analysis technology. Furthermore, the advice unit can adjust the way in which advice is expressed based on the user's emotion. For example, the advice unit can provide simple, highly visible advice when the user is nervous. Furthermore, the advice unit can provide detailed advice when the user is relaxed. Furthermore, the advice unit can provide advice that focuses on the main points when the user is in a hurry. In this way, the advice unit can provide more appropriate advice by adjusting the way in which advice is expressed based on the user's emotion.

[0114] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the question data. The advice unit adjusts the level of detail of the advice based on, for example, the importance of the question data. For example, the advice unit can provide detailed advice for question data with a high level of importance. The advice unit can also provide simplified advice for question data with a low level of importance. Furthermore, the advice unit can adjust the level of detail of the advice depending on the importance of the question data. In this way, the advice unit can efficiently provide advice by adjusting the level of detail of the advice based on the importance of the question data.

[0115] When providing advice, the advice unit can apply different advice algorithms depending on the category of the question. For example, the advice unit can apply different advice algorithms depending on the category of the question. For example, the advice unit can apply an advice algorithm specialized for health to a question about health. Furthermore, the advice unit can also apply an advice algorithm specialized for lifestyle habits to a question about lifestyle habits. Furthermore, the advice unit can also apply an advice algorithm specialized for diet to a question about diet. In this way, the advice unit can provide more appropriate advice by applying different advice algorithms depending on the category of the question.

[0116] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past advice results. The advice unit, for example, refers to the user's past advice results. For example, the advice unit can adjust the advice algorithm by referring to data such as the user's past advice content and the user's reaction. The advice unit can also set parameters for improving the accuracy of the advice from the user's past advice results. Furthermore, the advice unit can also improve the accuracy of the advice by referring to the user's past advice results. In this way, the advice unit can improve the accuracy of the advice by referring to the past advice results.

[0117] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The advice unit, for example, estimates the user's emotion. For example, the advice unit can estimate the user's emotion using facial expression recognition technology. The advice unit can also estimate the user's emotion using voice analysis technology. Furthermore, the advice unit can adjust the length of the advice based on the user's emotion. For example, the advice unit can provide short and to-the-point advice when the user is in a hurry. Furthermore, the advice unit can provide detailed advice when the user is relaxed. Furthermore, the advice unit can provide advice with visually stimulating effects when the user is excited. In this way, the advice unit can provide more appropriate advice by adjusting the length of the advice based on the user's emotion.

[0118] When providing advice, the advice unit can determine the priority of advice based on the time when the question was asked. The advice unit determines the priority of advice based, for example, on the time when the question was asked. For example, the advice unit can provide advice preferentially for questions that have recently arisen. The advice unit can also provide advice later for questions that have arisen in the past. Furthermore, the advice unit can adjust the priority of advice based on the time when the question was asked. In this way, the advice unit can provide advice efficiently by determining the priority of advice based on the time when the question was asked.

[0119] When providing advice, the advice unit can adjust the order of advice based on the relevance of the questions. The advice unit adjusts the order of advice based on, for example, the relevance of the questions. For example, the advice unit can provide advice preferentially for highly relevant questions. The advice unit can also provide advice later for less relevant questions. Furthermore, the advice unit can adjust the order of advice based on the relevance of the questions. In this way, the advice unit can provide advice efficiently by adjusting the order of advice based on the relevance of the questions.

[0120] When providing advice, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. The advice unit, for example, adjusts the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical knowledge, the advice unit can provide advice that uses a lot of technical terms. Also, if the user does not have technical knowledge, the advice unit can provide advice in simple language. Furthermore, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. In this way, the advice unit can provide advice that is easier to understand by adjusting the use of technical terms in the advice according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, question formation unit, provision unit, and advice unit, 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 data such as the user's daily activities, diet, and exercise habits using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The question formation unit is realized by the specific processing unit 290 of the data processing device 12 and forms a medical question based on the analyzed information. The provision unit is realized by the control unit 46A of the smart device 14 and provides the formed question to the user. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the provided question data and provides general medical advice. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, question formation unit, provision unit, and advice unit, described above, 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 data such as the user's daily activities, diet, and exercise habits using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The question formation unit is realized by the specific processing unit 290 of the data processing device 12 and forms a medical question based on the analyzed information. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the formed question to the user. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the provided question data and provides general medical advice. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, question formation unit, provision unit, and advice unit, described above, 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 data such as the user's daily activities, diet, and exercise habits using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The question formation unit is realized by the specific processing unit 290 of the data processing device 12 and forms a medical question based on the analyzed information. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the formed question to the user. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the provided question data and provides general medical advice. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, question formation unit, provision unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data such as the user's daily activities, diet, and exercise habits using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The question formation unit is realized by the specific processing unit 290 of the data processing device 12 and forms a medical question based on the analyzed information. The provision unit is realized by the control unit 46A of the robot 414 and provides the formed question to the user. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the provided question data and provides general medical advice.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The medical question answering system can further estimate the user's emotions and adjust the priority of questions based on the estimated emotions. For example, if the user is feeling anxious, it can provide urgent questions with priority. If the user is relaxed, it can provide detailed questions. If the user is in a hurry, it can provide questions that focus on the main points. In this way, by adjusting the priority of questions based on the user's emotions, it is possible to provide more appropriate questions.

[0123] The collection unit can analyze the user's social media activities and collect health-related data. For example, related data can be collected based on health information shared by the user on social media. It can also extract health-related topics from the user's social media activities and collect data. It can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data by analyzing social media activities.

[0124] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide more appropriate analysis results.

[0125] The question formulation unit can improve the accuracy of questions by referring to the user's past question results. For example, the question formulation unit can adjust the question algorithm by referring to data such as the user's past answers and diagnosis results. In addition, parameters for improving the accuracy of questions can be set based on the user's past question results. Furthermore, the accuracy of questions can also be improved by referring to the user's past question results. In this way, the accuracy of questions can be improved by referring to the past question results.

[0126] The providing unit can estimate the user's emotions and adjust the way questions are presented based on the estimated emotions. For example, if the user is nervous, a simple, highly visible presentation method can be provided. If the user is relaxed, a presentation method including detailed information can be provided. Furthermore, if the user is in a hurry, a presentation method that focuses on the main points can be provided. In this way, by adjusting the way questions are presented based on the user's emotions, more appropriate questions can be presented.

[0127] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the user is nervous, simple, highly visible advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, advice that focuses on the main points can be provided. In this way, by adjusting the way the advice is expressed based on the user's emotions, more appropriate advice can be provided.

[0128] The collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, if the user lives in a specific area, data related to health risks in that area can be prioritized. Also, if the user is traveling, data related to health risks at the travel destination can be prioritized. Furthermore, if the user frequently visits a specific area, data related to health risks in that area can be prioritized. In this way, highly relevant data can be prioritized by taking geographical location information into consideration.

[0129] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, an analysis algorithm specialized for health can be applied to health data. Also, an analysis algorithm specialized for lifestyle habit can be applied to lifestyle habit data. Furthermore, an analysis algorithm specialized for diet can be applied to diet data. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis can be performed.

[0130] The question formation unit can determine the priority of questions based on the time of symptom onset when forming questions. For example, questions can be provided preferentially for symptoms that have recently occurred. Also, questions can be provided later for symptoms that have occurred in the past. Furthermore, the priority of questions can be adjusted based on the time of symptom onset. In this way, by determining the priority of questions based on the time of symptom onset, questions can be provided efficiently.

[0131] When providing the information, the providing unit can select the optimal providing method by referring to the user's past question history. For example, the optimal providing method can be selected by referring to data such as the user's past question content and answer history. The providing method can also be customized based on the user's past question history. Furthermore, the providing method can also be improved by referring to the user's past question history. In this way, the optimal providing method can be selected by referring to the past question history.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects information about the user's lifestyle and health background. For example, it collects data about the user's daily activities, diet, exercise habits, etc. The collection unit can collect information such as how much time the user spends exercising each day and what kind of food they eat. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to understand the user's lifestyle habits and health background. The analysis unit provides data for formulating medical questions at a medical institution based on the user's lifestyle habits and health background. Step 3: The question generator generates medical questions based on the information analyzed by the analyzer. For example, if the user complains of a specific symptom, the generator generates questions related to that symptom. The question generator uses a generation AI to generate appropriate medical questions based on the user's lifestyle and health background. Step 4: The providing unit provides the questions formed by the question forming unit to the user. For example, before going to a medical institution, the user can check the formed question list and ask a doctor questions based on the list. Step 5: The advice unit analyzes the question data provided by the provision unit and provides general medical advice. For example, if the user asks about a specific symptom, the advice unit provides general advice for that symptom.

[0134] 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.

[0135] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 about the user's lifestyle and health background; an analysis unit that analyzes the information collected by the collection unit; a question formulation unit that formulates a medical question based on the information analyzed by the analysis unit; a providing unit that provides the question formed by the question forming unit to a user; an advice unit that analyzes the question data provided by the provision unit and provides general medical advice. A system characterized by:

2. The collecting unit Collect data on your daily activities, diet, and exercise habits 2. The system of claim 1.

3. The analysis unit Analyze the collected information to understand the user's lifestyle and health background 2. The system of claim 1.

4. The question formation unit Forming medical questions at medical institutions based on analysis results 2. The system of claim 1.

5. The providing unit Provide users with formulated questions to guide them in asking questions to doctors and pharmacists 2. The system of claim 1.

6. The advice unit Analyzes questionnaire data and provides general medical advice 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting lifestyle data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past health checkup results and select the type of data to collect 2. The system of claim 1.

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