system

Abstract

The system according to the embodiment aims to reduce the waiting time and psychological burden when a user visits a medical institution. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, a reservation unit, a medical questionnaire creation unit, a provision unit, and a support unit. The reception unit accepts input of symptoms from a user. The generation unit analyzes the symptoms accepted by the reception unit and performs a simple diagnosis. The provision unit provides the simple diagnosis results generated by the generation unit. The reservation unit makes an appointment with a nearby hospital or a family doctor when the generation unit determines that medical treatment by a doctor is necessary. The medical questionnaire creation unit creates a medical questionnaire in advance for the medical institution reserved by the reservation unit. The provision unit provides the medical questionnaire created by the medical questionnaire creation unit. The support unit provides health support based on the doctor's diagnosis results provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of long waiting times when users visit medical institutions, placing a heavy psychological burden on them.

[0005] The system according to the embodiment aims to reduce the waiting time and psychological burden when a user visits a medical institution. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, a reservation unit, a medical questionnaire creation unit, a provision unit, and a support unit. The reception unit accepts input of symptoms from a user. The generation unit analyzes the symptoms accepted by the reception unit and performs a simple diagnosis. The provision unit provides the simple diagnosis results generated by the generation unit. The reservation unit makes an appointment with a nearby hospital or a primary care doctor when the generation unit determines that medical treatment by a doctor is necessary. The medical questionnaire creation unit creates a medical questionnaire in advance for the medical institution reserved by the reservation unit. The provision unit provides the medical questionnaire created by the medical questionnaire creation unit. The support unit provides health support based on the doctor's diagnosis results provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the waiting time and psychological burden a user has when visiting a medical institution. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The medical support system according to an embodiment of the present invention uses a generative AI to perform simple AI medical consultations and create medical questionnaires. When a user inputs their symptoms, the generative AI analyzes the information and performs a simple diagnosis. If a doctor's consultation is deemed necessary, the system makes an appointment with a nearby hospital or family doctor and prepares a medical questionnaire in advance. Finally, health support is provided based on the doctor's diagnosis. This system aims to address the current situation in which few people seek medical attention immediately due to psychological challenges such as waiting times of over an hour and the inability to seek unnecessary advice. It aims to resolve users' health issues from both a time and psychological perspective, providing peace of mind. For example, a user inputs their symptoms, such as "I have a headache" or "I have a sore throat." This information is then input into the generative AI, which analyzes the symptoms. Based on the input symptoms, the generative AI then presents possible illnesses and treatments. For example, it provides a simple diagnosis such as "It's likely a cold" or "It's recommended to drink plenty of fluids." If the generative AI determines that a doctor's consultation is necessary, it can schedule an appointment with a nearby hospital or family doctor. Based on the user's current location, the system searches for the nearest medical institution and makes the appointment. The service also includes a function to create a medical questionnaire in advance, allowing users to enter necessary information before their consultation, ensuring a smooth medical treatment. For example, users can input basic information, details of their symptoms, and past medical history to help doctors with their treatment. Finally, the service offers health support based on the doctor's diagnosis. For example, the AI ​​provides health management advice based on the doctor's diagnosis. This advice includes dietary and exercise advice and recommendations for regular health checkups. This supports users in managing their health in their daily lives. This service aims to address the current situation where few people seek medical advice immediately due to psychological challenges such as waiting times of over an hour and the inability to discuss unnecessary questions. It aims to address users' health issues from a time and psychological perspective, providing peace of mind. For example, this service is extremely useful for people with time constraints, such as busy businessmen and housewives raising children. Users can easily enter their symptoms from home or work and receive a simple diagnosis from the AI.Even if a doctor's consultation is required, appointments and medical questionnaires can be completed in advance, ensuring a smooth consultation. Furthermore, health support based on diagnostic results makes it easier to manage your health in your daily life. This allows the medical support system to resolve users' health problems from both a time and psychological perspective, providing peace of mind.

[0029] The medical support system according to the embodiment includes a reception unit, a generation unit, a provision unit, a reservation unit, a medical questionnaire creation unit, and a support unit. The reception unit receives symptom input from a user. Symptom input from a user includes, but is not limited to, text input, voice input, and selection from options. The reception unit receives symptom input from a user using, for example, text input. The reception unit can also receive symptom input from a user using voice input. The reception unit can also receive symptom input from a user using selection from options. The generation unit uses a generation AI to analyze the symptoms received by the reception unit and perform a simple diagnosis. For example, the generation AI presents possible illnesses and treatments based on the input symptoms. For example, the generation AI provides a simple diagnosis result such as "high possibility of cold" or "recommended: drink plenty of fluids." The generation unit can also present other possible illnesses and treatments based on the input symptoms. The generation unit can provide a simple diagnostic result such as "There is a possibility of influenza" or "It is recommended that you see a doctor." The provision unit provides the simple diagnostic result generated by the generation unit. The provision unit, for example, displays the simple diagnostic result generated by the generation AI to the user. The provision unit, for example, displays the simple diagnostic result generated by the generation AI on the screen of the user's smartphone or computer. The provision unit can also provide the simple diagnostic result generated by the generation AI to the user by voice. The provision unit, for example, reads out the simple diagnostic result generated by the generation AI by voice. If the generation unit determines that medical treatment by a doctor is necessary, the reservation unit makes an appointment with a nearby hospital or family doctor. The reservation unit, for example, searches for the nearest medical institution based on the user's current location information and makes an appointment. The reservation unit, for example, obtains the user's current location information from GPS information, IP address, etc., and searches for the nearest medical institution. The reservation unit can also make an appointment at the nearest medical institution based on the user's current location information. The reservation unit makes a reservation with the nearest hospital or family doctor based on, for example, current location information of the user. The medical questionnaire creation unit creates a medical questionnaire in advance for the medical institution reserved by the reservation unit.The medical questionnaire creation unit creates a medical questionnaire that doctors can use as a reference when treating a user by inputting, for example, basic information about the user, details of the user's symptoms, and past medical history. The medical questionnaire creation unit inputs, for example, basic information about the user, such as the user's name, age, and gender. The medical questionnaire creation unit can also input details of the user's symptoms, such as the time of symptom onset, frequency, and intensity. The medical questionnaire creation unit can also input the user's past medical history, such as past diagnostic results and treatment history. The support unit provides health support based on the doctor's diagnosis results provided by the providing unit. For example, the support unit provides health management advice based on the doctor's diagnosis results using a generation AI. For example, the support unit provides advice on diet and exercise using a generation AI. The support unit can also recommend regular health checks using a generation AI. As a result, the medical consultation support system according to the embodiment can resolve the user's health problems from a time and psychological perspective and provide peace of mind.

[0030] The generation unit can present possible illnesses and treatments based on the input symptoms. For example, the generation AI presents possible illnesses and treatments based on the input symptoms. For example, the generation unit provides a simple diagnosis result such as "It is likely a cold" or "It is recommended that you drink plenty of fluids." The generation unit can also present possible other illnesses and treatments based on the input symptoms. For example, the generation AI can provide a simple diagnosis result such as "It is likely that you have the flu" or "It is recommended that you see a doctor." This makes it possible to provide appropriate information to the user by presenting possible illnesses and treatments based on the input symptoms.

[0031] The reservation unit can search for nearby medical institutions based on the user's current location information and make a reservation. The reservation unit, for example, searches for the nearest medical institution based on the user's current location information and makes a reservation. The reservation unit, for example, acquires the user's current location information from GPS information, an IP address, or the like, and searches for the nearest medical institution. The reservation unit can also make a reservation at the nearest medical institution based on the user's current location information. The reservation unit, for example, makes a reservation at the nearest hospital or family doctor based on the user's current location information. In this way, by searching for the nearest medical institution based on the user's current location information and making a reservation, it is possible to provide prompt medical services.

[0032] The medical questionnaire creation unit can create a medical questionnaire that will be useful to doctors when they provide medical care by inputting the user's basic information, details of symptoms, past medical history, etc. The medical questionnaire creation unit can create a medical questionnaire that will be useful to doctors when they provide medical care by inputting, for example, the user's basic information, details of symptoms, past medical history, etc. The medical questionnaire creation unit can input, for example, the user's basic information, details of symptoms, past medical history, etc. The medical questionnaire creation unit can also input the user's basic information, such as name, age, and gender. The medical questionnaire creation unit can also input the user's details of symptoms, such as the time of symptom onset, frequency, and intensity. Furthermore, the medical questionnaire creation unit can also input the user's past medical history, such as past diagnosis results and treatment history. In this way, by inputting the user's basic information, details of symptoms, past medical history, etc., it is possible to create a medical questionnaire that will be useful to doctors when they provide medical care.

[0033] The support unit can provide health management advice based on the doctor's diagnosis results. For example, the support unit can have the generating AI provide health management advice based on the doctor's diagnosis results. For example, the support unit can have the generating AI provide advice on diet and exercise. The support unit can also have the generating AI recommend regular health checks. This makes it possible to support the user's health management by providing health management advice based on the doctor's diagnosis results.

[0034] The reception unit can analyze the user's past symptom input history and provide an appropriate input interface. For example, the reception unit can automatically display symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms that will be input during a specific time period based on the user's past input history. In this way, the optimal input interface can be provided by analyzing the user's past symptom input history.

[0035] When inputting symptoms, the reception unit can filter the input content based on the user's current health condition and lifestyle habits. For example, the reception unit allows the user to input related symptoms preferentially based on the user's medical history that has been diagnosed in the past. The reception unit can also allow the user to input related symptoms taking into account the user's current lifestyle habits (smoking, drinking, etc.). The reception unit can also filter the input content based on the user's current health condition (body temperature, blood pressure, etc.). This allows for more appropriate symptom input by filtering the input content based on the user's current health condition and lifestyle habits.

[0036] When inputting symptoms, the reception unit can prioritize input of highly relevant symptoms based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize input of symptoms related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the reception unit can also prioritize input of symptoms related to health risks at the user's travel destination. Furthermore, when the user is at home, the reception unit can also prioritize input of symptoms related to health risks at home. In this way, highly relevant symptoms can be prioritized by taking the user's geographical location information into consideration.

[0037] When a symptom is input, the reception unit can analyze the user's social media activity and input related symptoms. The reception unit can input related symptoms based on, for example, health information shared by the user on social media. The reception unit can also input related symptoms based on information about health-related accounts the user follows on social media. The reception unit can also input related symptoms based on information about health-related groups the user participates in on social media. In this way, related symptoms can be input by analyzing the user's social media activity.

[0038] The generation unit can adjust the level of detail of the diagnosis based on the severity of the symptom during the simplified diagnosis. For example, the generation unit provides detailed diagnostic information in the case of a serious symptom. The generation unit can also provide concise diagnostic information in the case of a minor symptom. The generation unit can also adjust the level of detail of the diagnosis in stages according to the severity of the symptom. This allows appropriate diagnostic information to be provided by adjusting the level of detail of the diagnosis based on the severity of the symptom.

[0039] The generation unit can apply different diagnostic algorithms depending on the symptom category during the simplified diagnosis. For example, in the case of a respiratory symptom, the generation unit applies a diagnostic algorithm specialized for the respiratory system. In addition, in the case of a digestive symptom, the generation unit can also apply a diagnostic algorithm specialized for the digestive system. In addition, in the case of a nervous system symptom, the generation unit can also apply a diagnostic algorithm specialized for the nervous system. In this way, by applying different diagnostic algorithms depending on the symptom category, more accurate diagnostic results can be provided.

[0040] During the simplified diagnosis, the generation unit can determine the priority of the diagnosis based on the time when the symptoms occurred. For example, the generation unit prioritizes diagnosing symptoms that have occurred recently. The generation unit can also prioritize diagnosing symptoms that have continued for a long period of time. The generation unit can also gradually adjust the priority of the diagnosis depending on the time when the symptoms occurred. In this way, by determining the priority of the diagnosis based on the time when the symptoms occurred, it is possible to provide appropriate diagnostic results.

[0041] The generation unit can adjust the order of diagnoses based on the relevance of symptoms during the simplified diagnosis. For example, the generation unit prioritizes diagnosing highly relevant symptoms. The generation unit can also postpone diagnosing less relevant symptoms. The generation unit can also adjust the order of diagnoses in stages according to the relevance of symptoms. In this way, by adjusting the order of diagnoses based on the relevance of symptoms, it is possible to provide appropriate diagnostic results.

[0042] When providing a diagnostic result, the providing unit can select an appropriate display method by referring to the user's past diagnostic history. The providing unit provides the diagnostic result based on, for example, a display method that the user has preferred in the past. The providing unit can also select the most understandable display method from the user's past diagnostic history. The providing unit can also analyze the user's past diagnostic history and suggest the optimal display method. In this way, the optimal display method can be selected by referring to the user's past diagnostic history.

[0043] The providing unit can customize the display content based on the user's current health condition when providing the diagnostic result. For example, the providing unit can prioritize displaying related information based on the user's current health condition. The providing unit can also filter the display content according to the user's current health condition. The providing unit can also customize the display content taking the user's current health condition into consideration. In this way, by customizing the display content based on the user's current health condition, more appropriate diagnostic results can be provided.

[0044] When providing a diagnostic result, the providing unit can select an appropriate display method based on the user's geographical location information. For example, when the user is in a specific area, the providing unit can prioritize displaying information related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the providing unit can also prioritize displaying information related to health risks at the user's travel destination. Furthermore, when the user is at home, the providing unit can also prioritize displaying information related to health risks within the home. In this way, the optimal display method can be selected by taking the user's geographical location information into consideration.

[0045] When providing a diagnostic result, the providing unit can analyze the user's social media activity and adjust the display content. For example, the providing unit can display related diagnostic results based on health information shared by the user on social media. The providing unit can also display related diagnostic results based on information about health-related accounts the user follows on social media. The providing unit can also display related diagnostic results based on information about health-related groups the user participates in on social media. In this way, related diagnostic results can be provided by analyzing the user's social media activity.

[0046] When making a reservation, the reservation unit can select an appropriate reservation method by referring to the user's past reservation history. The reservation unit can, for example, suggest the optimal reservation method based on the reservation methods the user has used in the past. The reservation unit can also select the most efficient reservation method from the user's past reservation history. The reservation unit can also analyze the user's past reservation history and suggest the optimal reservation time slot. In this way, the optimal reservation method can be selected by referring to the user's past reservation history.

[0047] The reservation unit can customize the reservation contents based on the user's current health condition when making a reservation. For example, the reservation unit can suggest an optimal reservation time based on the user's current health condition. The reservation unit can also filter the reservation contents according to the user's current health condition. The reservation unit can also customize the reservation contents taking the user's current health condition into consideration. This allows for more appropriate reservations by customizing the reservation contents based on the user's current health condition.

[0048] When making a reservation, the reservation unit can select an appropriate reservation method based on the user's geographical location information. For example, if the user is in a specific area, the reservation unit will prioritize reserving medical institutions available in that area. Furthermore, if the user is traveling, the reservation unit can also prioritize reserving medical institutions at the user's travel destination. Furthermore, if the user is at home, the reservation unit can also prioritize reserving nearby medical institutions. In this way, the optimal reservation method can be selected by taking the user's geographical location information into consideration.

[0049] The reservation unit can adjust the reservation details by analyzing the user's social media activity when making a reservation. For example, the reservation unit reserves a relevant medical institution based on health information shared by the user on social media. The reservation unit can also adjust the reservation details based on information about medical institutions that the user follows on social media. The reservation unit can also adjust the reservation details based on information about health-related groups that the user joins on social media. In this way, by analyzing the user's social media activity, it is possible to provide relevant reservation details.

[0050] When creating a medical questionnaire, the medical questionnaire creation unit can create an appropriate medical questionnaire by referring to the user's past medical history. The medical questionnaire creation unit, for example, preferentially displays relevant questions based on the user's past medical history. The medical questionnaire creation unit can also create the most efficient medical questionnaire from the user's past medical history. The medical questionnaire creation unit can also analyze the user's past medical history and suggest optimal questions. In this way, the optimal medical questionnaire can be created by referring to the user's past medical history.

[0051] The medical questionnaire creation unit can customize the contents of the medical questionnaire based on the user's current health condition when creating the medical questionnaire. For example, the medical questionnaire creation unit can preferentially display related questions based on the user's current health condition. The medical questionnaire creation unit can also filter the contents of the medical questionnaire according to the user's current health condition. The medical questionnaire creation unit can also customize the contents of the medical questionnaire taking the user's current health condition into consideration. In this way, by customizing the contents of the medical questionnaire based on the user's current health condition, a more appropriate medical questionnaire can be provided.

[0052] When creating a medical questionnaire, the medical questionnaire creation unit can create an appropriate medical questionnaire based on the user's geographical location information. For example, when the user is in a specific area, the medical questionnaire creation unit can prioritize displaying questions related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the medical questionnaire creation unit can also prioritize displaying questions related to health risks at the user's travel destination. Furthermore, when the user is at home, the medical questionnaire creation unit can also prioritize displaying questions related to health risks at home. In this way, an optimal medical questionnaire can be created by taking the user's geographical location information into consideration.

[0053] When creating the questionnaire, the questionnaire creation unit can analyze the user's social media activity and adjust the content of the questionnaire. The questionnaire creation unit can display related questions based on, for example, health information shared by the user on social media. The questionnaire creation unit can also display related questions based on information about health-related accounts the user follows on social media. The questionnaire creation unit can also display related questions based on information about health-related groups the user participates in on social media. In this way, related questions can be displayed by analyzing the user's social media activity.

[0054] When providing health support, the support unit can select an appropriate support method by referring to the user's past health management history. For example, the support unit can suggest an optimal support method based on the user's past health management history. The support unit can also select the most effective support method from the user's past health management history. The support unit can also analyze the user's past health management history and suggest an optimal support method. This makes it possible to select an optimal support method by referring to the user's past health management history.

[0055] The support unit can customize the support content based on the user's current living situation during health support. For example, the support unit provides relevant health support based on the user's current living situation. The support unit can also filter the support content according to the user's current living situation. The support unit can also customize the support content taking the user's current living situation into consideration. This allows the support content to be customized based on the user's current living situation, making it possible to provide more appropriate support.

[0056] The support unit can select an appropriate support method based on the user's geographical location information when providing health support. For example, if the user is in a specific area, the support unit can provide health support available in that area. If the user is traveling, the support unit can also provide health support at the user's travel destination. If the user is at home, the support unit can also provide nearby health support. This allows the optimal support method to be selected by taking the user's geographical location information into consideration.

[0057] The support unit can analyze the user's social media activity to adjust the support content when providing health support. For example, the support unit can provide relevant health support based on health information shared by the user on social media. The support unit can also provide relevant health support based on information about health-related accounts the user follows on social media. The support unit can also provide relevant health support based on information about health-related groups the user participates in on social media. In this way, relevant support content can be provided by analyzing the user's social media activity.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The medical assistance system may further include a lifestyle habit monitoring unit that monitors the user's lifestyle habits. The lifestyle habit monitoring unit collects data on the user's diet, exercise, sleep, etc., and provides it to the generation unit. The generation unit can perform a more accurate simplified diagnosis based on this data. For example, if the user has not been exercising much recently, the generation unit can take that information into consideration and provide a diagnosis result such as "Fatigue may be due to lack of exercise." Also, if the user has an irregular diet, the generation unit can provide a diagnosis result such as "Poor physical condition may be due to lack of nutrition." Furthermore, based on the user's sleep data, the generation unit can provide a diagnosis result such as "Headache may be due to lack of sleep." This makes it possible to provide a more appropriate diagnosis result based on the user's lifestyle habits.

[0060] The medical assistance system may further include a health tracking unit that tracks the user's health data over a long period of time. The health tracking unit periodically collects the user's health data and provides it to the generation unit. The generation unit can detect changes in the user's health condition based on this data and provide appropriate advice. For example, if the user's weight is increasing, the generation unit can provide advice such as "recommend exercising to manage weight." If the user's blood pressure is high, the generation unit can provide advice such as "reduce salt intake." If the user's blood sugar level is rising, the generation unit can provide advice such as "reduce sugar intake." This makes it possible to track the user's health condition over a long period of time and provide appropriate advice.

[0061] The medical assistance system may further include a family health management unit that manages health data of the user's family. The family health management unit collects health data of the user's family and provides it to the generation unit. The generation unit can provide advice that takes into account the health status of the entire family based on this data. For example, if there is a family member with allergies, the generation unit can provide advice such as "take measures to prevent allergies." If there is a family member with high blood pressure, the generation unit can provide advice such as "cut down on salt intake." If there is a family member with diabetes, the generation unit can provide advice such as "cut down on sugar intake." This makes it possible to provide appropriate advice that takes into account the health status of the entire family.

[0062] The medical support system can further include a workplace health management unit that manages health data from the user's workplace. The workplace health management unit collects health data from the user's workplace and provides it to the generation unit. The generation unit can provide advice that takes into account the health status of the entire workplace based on this data. For example, if influenza is prevalent in the workplace, the generation unit can provide advice such as "get vaccinated." If stress is rising in the workplace, the generation unit can provide advice such as "recommend relaxation to manage stress." Furthermore, if long working hours are a problem in the workplace, the generation unit can provide advice such as "take appropriate rest." This makes it possible to provide appropriate advice that takes into account the health status of the entire workplace.

[0063] The medical assistance system may further include an exercise data collection unit that collects the user's exercise data. The exercise data collection unit collects the user's exercise data and provides it to the generation unit. The generation unit can provide advice that takes into account the user's exercise habits based on this data. For example, if the user is not getting enough exercise, the generation unit can provide advice such as "We recommend walking 30 minutes every day." If the user is exercising too much, the generation unit can also provide advice such as "Try to exercise moderately." Furthermore, if the user plays a particular sport, the generation unit can also provide training methods suitable for that sport. This makes it possible to provide appropriate advice that takes into account the user's exercise habits.

[0064] The medical assistance system may further include a dietary data collection unit that collects dietary data of the user. The dietary data collection unit collects the user's dietary data and provides it to the generation unit. The generation unit can provide advice that takes into account the user's eating habits based on this data. For example, if the user is undernourished, the generation unit can provide advice such as "try to eat a balanced diet." If the user is overeating, the generation unit can provide advice such as "maintain a moderate amount of food." Furthermore, if the user is allergic to a specific food ingredient, the generation unit can provide advice to avoid that ingredient. This makes it possible to provide appropriate advice that takes into account the user's eating habits.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives symptom input from the user. Symptom input from the user includes text input, voice input, selection from options, etc. For example, the reception unit receives symptom input from the user using text input. The reception unit also receives symptom input using voice input or selection from options. Step 2: The generation unit analyzes the symptoms received by the reception unit and performs a simple diagnosis. Using the generation AI, possible illnesses and treatments are presented based on the input symptoms. For example, it can provide simple diagnostic results such as "It's likely a cold" or "It's recommended that you drink plenty of fluids." It can also provide simple diagnostic results such as "It's likely that you have the flu" or "It's recommended that you see a doctor." Step 3: The provider provides the simple diagnostic results generated by the generator. The result generated by the AI ​​is displayed to the user. For example, it is displayed on a smartphone or computer screen. The result can also be read aloud. Step 4: If the generation unit determines that medical treatment by a doctor is necessary, the reservation unit makes a reservation at a nearby hospital or family doctor. The unit searches for the nearest medical institution based on the user's current location information and makes the reservation. For example, the unit obtains current location information from GPS information or IP address, searches for the nearest medical institution, and makes the reservation. Step 5: The medical questionnaire creation unit creates a medical questionnaire in advance for the medical institution for which the reservation has been made by the reservation unit. By inputting the user's basic information, details of symptoms, past medical history, etc., a medical questionnaire is created that will be useful for the doctor when treating the patient. For example, basic information such as name, age, and gender, details such as the time of symptom onset, frequency, and intensity, past diagnostic results, treatment history, etc. are input. Step 6: The support department provides health support based on the doctor's diagnosis provided by the provider department. The generation AI provides health management advice, such as advice on diet and exercise and recommendations for regular health checks.

[0067] (Example 2) The medical support system according to an embodiment of the present invention uses a generative AI to perform simple AI medical consultations and create medical questionnaires. When a user inputs their symptoms, the generative AI analyzes the information and performs a simple diagnosis. If a doctor's consultation is deemed necessary, the system makes an appointment with a nearby hospital or family doctor and prepares a medical questionnaire in advance. Finally, health support is provided based on the doctor's diagnosis. This system aims to address the current situation in which few people seek medical attention immediately due to psychological challenges such as waiting times of over an hour and the inability to seek unnecessary advice. It aims to resolve users' health issues from both a time and psychological perspective, providing peace of mind. For example, a user inputs their symptoms, such as "I have a headache" or "I have a sore throat." This information is then input into the generative AI, which analyzes the symptoms. Based on the input symptoms, the generative AI then presents possible illnesses and treatments. For example, it provides a simple diagnosis such as "It's likely a cold" or "It's recommended to drink plenty of fluids." If the generative AI determines that a doctor's consultation is necessary, it can schedule an appointment with a nearby hospital or family doctor. Based on the user's current location, the system searches for the nearest medical institution and makes the appointment. The service also includes a function to create a medical questionnaire in advance, allowing users to enter necessary information before their consultation, ensuring a smooth medical treatment. For example, users can input basic information, details of their symptoms, and past medical history to help doctors with their treatment. Finally, the service offers health support based on the doctor's diagnosis. For example, the AI ​​provides health management advice based on the doctor's diagnosis. This advice includes dietary and exercise advice and recommendations for regular health checkups. This supports users in managing their health in their daily lives. This service aims to address the current situation where few people seek medical advice immediately due to psychological challenges such as waiting times of over an hour and the inability to discuss unnecessary questions. It aims to address users' health issues from a time and psychological perspective, providing peace of mind. For example, this service is extremely useful for people with time constraints, such as busy businessmen and housewives raising children. Users can easily enter their symptoms from home or work and receive a simple diagnosis from the AI.Even if a doctor's consultation is required, appointments and medical questionnaires can be completed in advance, ensuring a smooth consultation. Furthermore, health support based on diagnostic results makes it easier to manage your health in your daily life. This allows the medical support system to resolve users' health problems from both a time and psychological perspective, providing peace of mind.

[0068] The medical support system according to the embodiment includes a reception unit, a generation unit, a provision unit, a reservation unit, a medical questionnaire creation unit, and a support unit. The reception unit receives symptom input from a user. Symptom input from a user includes, but is not limited to, text input, voice input, and selection from options. The reception unit receives symptom input from a user using, for example, text input. The reception unit can also receive symptom input from a user using voice input. The reception unit can also receive symptom input from a user using selection from options. The generation unit uses a generation AI to analyze the symptoms received by the reception unit and perform a simple diagnosis. For example, the generation AI presents possible illnesses and treatments based on the input symptoms. For example, the generation AI provides a simple diagnosis result such as "high possibility of cold" or "recommended: drink plenty of fluids." The generation unit can also present other possible illnesses and treatments based on the input symptoms. The generation unit can provide a simple diagnostic result such as "There is a possibility of influenza" or "It is recommended that you see a doctor." The provision unit provides the simple diagnostic result generated by the generation unit. The provision unit, for example, displays the simple diagnostic result generated by the generation AI to the user. The provision unit, for example, displays the simple diagnostic result generated by the generation AI on the screen of the user's smartphone or computer. The provision unit can also provide the simple diagnostic result generated by the generation AI to the user by voice. The provision unit, for example, reads out the simple diagnostic result generated by the generation AI by voice. If the generation unit determines that medical treatment by a doctor is necessary, the reservation unit makes an appointment with a nearby hospital or family doctor. The reservation unit, for example, searches for the nearest medical institution based on the user's current location information and makes an appointment. The reservation unit, for example, obtains the user's current location information from GPS information, IP address, etc., and searches for the nearest medical institution. The reservation unit can also make an appointment at the nearest medical institution based on the user's current location information. The reservation unit makes a reservation with the nearest hospital or family doctor based on, for example, current location information of the user. The medical questionnaire creation unit creates a medical questionnaire in advance for the medical institution reserved by the reservation unit.The medical questionnaire creation unit creates a medical questionnaire that doctors can use as a reference when treating a user by inputting, for example, basic information about the user, details of the user's symptoms, and past medical history. The medical questionnaire creation unit inputs, for example, basic information about the user, such as the user's name, age, and gender. The medical questionnaire creation unit can also input details of the user's symptoms, such as the time of symptom onset, frequency, and intensity. The medical questionnaire creation unit can also input the user's past medical history, such as past diagnostic results and treatment history. The support unit provides health support based on the doctor's diagnosis results provided by the providing unit. For example, the support unit provides health management advice based on the doctor's diagnosis results using a generation AI. For example, the support unit provides advice on diet and exercise using a generation AI. The support unit can also recommend regular health checks using a generation AI. As a result, the medical consultation support system according to the embodiment can resolve the user's health problems from a time and psychological perspective and provide peace of mind.

[0069] The generation unit can present possible illnesses and treatments based on the input symptoms. For example, the generation AI presents possible illnesses and treatments based on the input symptoms. For example, the generation unit provides a simple diagnosis result such as "It is likely a cold" or "It is recommended that you drink plenty of fluids." The generation unit can also present possible other illnesses and treatments based on the input symptoms. For example, the generation AI can provide a simple diagnosis result such as "It is likely that you have the flu" or "It is recommended that you see a doctor." This makes it possible to provide appropriate information to the user by presenting possible illnesses and treatments based on the input symptoms.

[0070] The reservation unit can search for nearby medical institutions based on the user's current location information and make a reservation. The reservation unit, for example, searches for the nearest medical institution based on the user's current location information and makes a reservation. The reservation unit, for example, acquires the user's current location information from GPS information, an IP address, or the like, and searches for the nearest medical institution. The reservation unit can also make a reservation at the nearest medical institution based on the user's current location information. The reservation unit, for example, makes a reservation at the nearest hospital or family doctor based on the user's current location information. In this way, by searching for the nearest medical institution based on the user's current location information and making a reservation, it is possible to provide prompt medical services.

[0071] The medical questionnaire creation unit can create a medical questionnaire that will be useful to doctors when they provide medical care by inputting the user's basic information, details of symptoms, past medical history, etc. The medical questionnaire creation unit can create a medical questionnaire that will be useful to doctors when they provide medical care by inputting, for example, the user's basic information, details of symptoms, past medical history, etc. The medical questionnaire creation unit can input, for example, the user's basic information, details of symptoms, past medical history, etc. The medical questionnaire creation unit can also input the user's basic information, such as name, age, and gender. The medical questionnaire creation unit can also input the user's details of symptoms, such as the time of symptom onset, frequency, and intensity. Furthermore, the medical questionnaire creation unit can also input the user's past medical history, such as past diagnosis results and treatment history. In this way, by inputting the user's basic information, details of symptoms, past medical history, etc., it is possible to create a medical questionnaire that will be useful to doctors when they provide medical care.

[0072] The support unit can provide health management advice based on the doctor's diagnosis results. For example, the support unit can have the generating AI provide health management advice based on the doctor's diagnosis results. For example, the support unit can have the generating AI provide advice on diet and exercise. The support unit can also have the generating AI recommend regular health checks. This makes it possible to support the user's health management by providing health management advice based on the doctor's diagnosis results.

[0073] The reception unit can estimate the user's emotions and adjust the symptom input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Alternatively, if the user is in a hurry, the reception unit can prioritize voice input to enable quick symptom input. In this way, by adjusting the symptom input method according to the user's emotions, a more appropriate input method can be provided.

[0074] The reception unit can analyze the user's past symptom input history and provide an appropriate input interface. For example, the reception unit can automatically display symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms that will be input during a specific time period based on the user's past input history. In this way, the optimal input interface can be provided by analyzing the user's past symptom input history.

[0075] When inputting symptoms, the reception unit can filter the input content based on the user's current health condition and lifestyle habits. For example, the reception unit allows the user to input related symptoms preferentially based on the user's medical history that has been diagnosed in the past. The reception unit can also allow the user to input related symptoms taking into account the user's current lifestyle habits (smoking, drinking, etc.). The reception unit can also filter the input content based on the user's current health condition (body temperature, blood pressure, etc.). This allows for more appropriate symptom input by filtering the input content based on the user's current health condition and lifestyle habits.

[0076] The reception unit can estimate the user's emotions and determine the priority of input symptoms based on the estimated user emotions. For example, when the user is feeling anxious, the reception unit allows the user to input serious symptoms with priority. Furthermore, when the user is relaxed, the reception unit can also allow the user to input minor symptoms with priority. Furthermore, when the user is in a hurry, the reception unit can also allow the user to input serious symptoms with priority. In this way, by determining the priority of symptoms according to the user's emotions, serious symptoms can be input with priority.

[0077] When inputting symptoms, the reception unit can prioritize input of highly relevant symptoms based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize input of symptoms related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the reception unit can also prioritize input of symptoms related to health risks at the user's travel destination. Furthermore, when the user is at home, the reception unit can also prioritize input of symptoms related to health risks at home. In this way, highly relevant symptoms can be prioritized by taking the user's geographical location information into consideration.

[0078] When a symptom is input, the reception unit can analyze the user's social media activity and input related symptoms. The reception unit can input related symptoms based on, for example, health information shared by the user on social media. The reception unit can also input related symptoms based on information about health-related accounts the user follows on social media. The reception unit can also input related symptoms based on information about health-related groups the user participates in on social media. In this way, related symptoms can be input by analyzing the user's social media activity.

[0079] The generation unit can estimate the user's emotions and adjust the presentation method of the simple diagnosis based on the estimated user's emotions. For example, if the user is feeling anxious, the generation unit can provide the simple diagnosis using a presentation method that gives a sense of security. Furthermore, if the user is relaxed, the generation unit can provide the simple diagnosis using a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide the simple diagnosis using a concise and to-the-point presentation method. In this way, by adjusting the presentation method of the simple diagnosis according to the user's emotions, more appropriate diagnosis results can be provided.

[0080] The generation unit can adjust the level of detail of the diagnosis based on the severity of the symptom during the simplified diagnosis. For example, the generation unit provides detailed diagnostic information in the case of a serious symptom. The generation unit can also provide concise diagnostic information in the case of a minor symptom. The generation unit can also adjust the level of detail of the diagnosis in stages according to the severity of the symptom. This allows appropriate diagnostic information to be provided by adjusting the level of detail of the diagnosis based on the severity of the symptom.

[0081] The generation unit can apply different diagnostic algorithms depending on the symptom category during the simplified diagnosis. For example, in the case of a respiratory symptom, the generation unit applies a diagnostic algorithm specialized for the respiratory system. In addition, in the case of a digestive symptom, the generation unit can also apply a diagnostic algorithm specialized for the digestive system. In addition, in the case of a nervous system symptom, the generation unit can also apply a diagnostic algorithm specialized for the nervous system. In this way, by applying different diagnostic algorithms depending on the symptom category, more accurate diagnostic results can be provided.

[0082] The generation unit can estimate the user's emotions and adjust the length of the simple diagnosis based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can provide a short, to-the-point simple diagnosis. If the user is relaxed, the generation unit can also provide a longer simple diagnosis including detailed information. If the user is feeling anxious, the generation unit can also provide a simple diagnosis of an appropriate length to give the user a sense of security. In this way, by adjusting the length of the simple diagnosis according to the user's emotions, an appropriate diagnosis result can be provided.

[0083] During the simplified diagnosis, the generation unit can determine the priority of the diagnosis based on the time when the symptoms occurred. For example, the generation unit prioritizes diagnosing symptoms that have occurred recently. The generation unit can also prioritize diagnosing symptoms that have continued for a long period of time. The generation unit can also gradually adjust the priority of the diagnosis depending on the time when the symptoms occurred. In this way, by determining the priority of the diagnosis based on the time when the symptoms occurred, it is possible to provide appropriate diagnostic results.

[0084] The generation unit can adjust the order of diagnoses based on the relevance of symptoms during the simplified diagnosis. For example, the generation unit prioritizes diagnosing highly relevant symptoms. The generation unit can also postpone diagnosing less relevant symptoms. The generation unit can also adjust the order of diagnoses in stages according to the relevance of symptoms. In this way, by adjusting the order of diagnoses based on the relevance of symptoms, it is possible to provide appropriate diagnostic results.

[0085] The providing unit can estimate the user's emotions and adjust the display method of the diagnostic result based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide the diagnostic result in a display method that gives a sense of security. Furthermore, if the user is relaxed, the providing unit can also provide the diagnostic result in a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide the diagnostic result in a concise and to-the-point display method. In this way, by adjusting the display method of the diagnostic result according to the user's emotions, more appropriate diagnostic results can be provided.

[0086] When providing a diagnostic result, the providing unit can select an appropriate display method by referring to the user's past diagnostic history. The providing unit provides the diagnostic result based on, for example, a display method that the user has preferred in the past. The providing unit can also select the most understandable display method from the user's past diagnostic history. The providing unit can also analyze the user's past diagnostic history and suggest the optimal display method. In this way, the optimal display method can be selected by referring to the user's past diagnostic history.

[0087] The providing unit can customize the display content based on the user's current health condition when providing the diagnostic result. For example, the providing unit can prioritize displaying related information based on the user's current health condition. The providing unit can also filter the display content according to the user's current health condition. The providing unit can also customize the display content taking the user's current health condition into consideration. In this way, by customizing the display content based on the user's current health condition, more appropriate diagnostic results can be provided.

[0088] The providing unit can estimate the user's emotions and determine the priority of diagnostic results based on the estimated user's emotions. For example, when the user feels anxious, the providing unit can prioritize displaying serious diagnostic results. Furthermore, when the user feels relaxed, the providing unit can also prioritize displaying detailed diagnostic results. Furthermore, when the user is in a hurry, the providing unit can also prioritize displaying important diagnostic results. In this way, by determining the priority of diagnostic results according to the user's emotions, important diagnostic results can be provided preferentially.

[0089] When providing a diagnostic result, the providing unit can select an appropriate display method based on the user's geographical location information. For example, when the user is in a specific area, the providing unit can prioritize displaying information related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the providing unit can also prioritize displaying information related to health risks at the user's travel destination. Furthermore, when the user is at home, the providing unit can also prioritize displaying information related to health risks within the home. In this way, the optimal display method can be selected by taking the user's geographical location information into consideration.

[0090] When providing a diagnostic result, the providing unit can analyze the user's social media activity and adjust the display content. For example, the providing unit can display related diagnostic results based on health information shared by the user on social media. The providing unit can also display related diagnostic results based on information about health-related accounts the user follows on social media. The providing unit can also display related diagnostic results based on information about health-related groups the user participates in on social media. In this way, related diagnostic results can be provided by analyzing the user's social media activity.

[0091] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated user's emotions. For example, if the user feels anxious, the reservation unit can provide a simple reservation method and minimize steps. If the user feels relaxed, the reservation unit can provide detailed reservation options and suggest a customizable reservation method. If the user is in a hurry, the reservation unit can prioritize voice input to enable quick reservations. In this way, a more appropriate reservation method can be provided by adjusting the reservation method according to the user's emotions.

[0092] When making a reservation, the reservation unit can select an appropriate reservation method by referring to the user's past reservation history. The reservation unit can, for example, suggest the optimal reservation method based on the reservation methods the user has used in the past. The reservation unit can also select the most efficient reservation method from the user's past reservation history. The reservation unit can also analyze the user's past reservation history and suggest the optimal reservation time slot. In this way, the optimal reservation method can be selected by referring to the user's past reservation history.

[0093] The reservation unit can customize the reservation contents based on the user's current health condition when making a reservation. For example, the reservation unit can suggest an optimal reservation time based on the user's current health condition. The reservation unit can also filter the reservation contents according to the user's current health condition. The reservation unit can also customize the reservation contents taking the user's current health condition into consideration. This allows for more appropriate reservations by customizing the reservation contents based on the user's current health condition.

[0094] The reservation unit can estimate the user's emotions and determine the priority of reservations based on the estimated user's emotions. For example, if the user feels anxious, the reservation unit can quickly confirm the reservation. If the user feels relaxed, the reservation unit can also provide detailed reservation options. If the user is in a hurry, the reservation unit can quickly confirm the reservation. In this way, by determining the priority of reservations according to the user's emotions, important reservations can be given priority.

[0095] When making a reservation, the reservation unit can select an appropriate reservation method based on the user's geographical location information. For example, if the user is in a specific area, the reservation unit will prioritize reserving medical institutions available in that area. Furthermore, if the user is traveling, the reservation unit can also prioritize reserving medical institutions at the user's travel destination. Furthermore, if the user is at home, the reservation unit can also prioritize reserving nearby medical institutions. In this way, the optimal reservation method can be selected by taking the user's geographical location information into consideration.

[0096] The reservation unit can adjust the reservation details by analyzing the user's social media activity when making a reservation. For example, the reservation unit reserves a relevant medical institution based on health information shared by the user on social media. The reservation unit can also adjust the reservation details based on information about medical institutions that the user follows on social media. The reservation unit can also adjust the reservation details based on information about health-related groups that the user joins on social media. In this way, by analyzing the user's social media activity, it is possible to provide relevant reservation details.

[0097] The medical questionnaire creation unit can estimate the user's emotions and adjust the method for creating the medical questionnaire based on the estimated user's emotions. For example, if the user is feeling anxious, the medical questionnaire creation unit can provide a simple medical questionnaire and minimize input steps. Furthermore, if the user is relaxed, the medical questionnaire creation unit can provide detailed input options and suggest a customizable medical questionnaire. Furthermore, if the user is in a hurry, the medical questionnaire creation unit can prioritize voice input and enable the medical questionnaire to be created quickly. In this way, by adjusting the method for creating the medical questionnaire according to the user's emotions, a more appropriate medical questionnaire can be provided.

[0098] When creating a medical questionnaire, the medical questionnaire creation unit can create an appropriate medical questionnaire by referring to the user's past medical history. The medical questionnaire creation unit, for example, preferentially displays relevant questions based on the user's past medical history. The medical questionnaire creation unit can also create the most efficient medical questionnaire from the user's past medical history. The medical questionnaire creation unit can also analyze the user's past medical history and suggest optimal questions. In this way, the optimal medical questionnaire can be created by referring to the user's past medical history.

[0099] The medical questionnaire creation unit can customize the contents of the medical questionnaire based on the user's current health condition when creating the medical questionnaire. For example, the medical questionnaire creation unit can preferentially display related questions based on the user's current health condition. The medical questionnaire creation unit can also filter the contents of the medical questionnaire according to the user's current health condition. The medical questionnaire creation unit can also customize the contents of the medical questionnaire taking the user's current health condition into consideration. In this way, by customizing the contents of the medical questionnaire based on the user's current health condition, a more appropriate medical questionnaire can be provided.

[0100] The medical questionnaire creation unit can estimate the user's emotions and determine the priority of the medical questionnaire based on the estimated user's emotions. For example, when the user is feeling anxious, the medical questionnaire creation unit can prioritize displaying important questions. Furthermore, when the user is relaxed, the medical questionnaire creation unit can also prioritize displaying detailed questions. Furthermore, when the user is in a hurry, the medical questionnaire creation unit can also prioritize displaying important questions. In this way, by determining the priority of the medical questionnaire according to the user's emotions, important questions can be displayed preferentially.

[0101] When creating a medical questionnaire, the medical questionnaire creation unit can create an appropriate medical questionnaire based on the user's geographical location information. For example, when the user is in a specific area, the medical questionnaire creation unit can prioritize displaying questions related to diseases that are prevalent in that area. Furthermore, when the user is traveling, the medical questionnaire creation unit can also prioritize displaying questions related to health risks at the user's travel destination. Furthermore, when the user is at home, the medical questionnaire creation unit can also prioritize displaying questions related to health risks at home. In this way, an optimal medical questionnaire can be created by taking the user's geographical location information into consideration.

[0102] When creating the questionnaire, the questionnaire creation unit can analyze the user's social media activity and adjust the content of the questionnaire. The questionnaire creation unit can display related questions based on, for example, health information shared by the user on social media. The questionnaire creation unit can also display related questions based on information about health-related accounts the user follows on social media. The questionnaire creation unit can also display related questions based on information about health-related groups the user participates in on social media. In this way, related questions can be displayed by analyzing the user's social media activity.

[0103] The support unit can estimate the user's emotions and adjust the health support method based on the estimated user's emotions. For example, if the user feels anxious, the support unit can provide health support that gives a sense of security. Furthermore, if the user is relaxed, the support unit can provide detailed health support. Furthermore, if the user is in a hurry, the support unit can provide concise health support that focuses on the main points. In this way, more appropriate support can be provided by adjusting the health support method according to the user's emotions.

[0104] When providing health support, the support unit can select an appropriate support method by referring to the user's past health management history. For example, the support unit can suggest an optimal support method based on the user's past health management history. The support unit can also select the most effective support method from the user's past health management history. The support unit can also analyze the user's past health management history and suggest an optimal support method. This makes it possible to select an optimal support method by referring to the user's past health management history.

[0105] The support unit can customize the support content based on the user's current living situation during health support. For example, the support unit provides relevant health support based on the user's current living situation. The support unit can also filter the support content according to the user's current living situation. The support unit can also customize the support content taking the user's current living situation into consideration. This allows the support content to be customized based on the user's current living situation, making it possible to provide more appropriate support.

[0106] The support unit can estimate the user's emotions and determine the priority of health support based on the estimated user's emotions. For example, if the user feels anxious, the support unit can provide important health support with priority. Furthermore, if the user feels relaxed, the support unit can also provide detailed health support with priority. Furthermore, if the user is in a hurry, the support unit can also provide important health support with priority. In this way, by determining the priority of health support according to the user's emotions, important support can be provided with priority.

[0107] The support unit can select an appropriate support method based on the user's geographical location information when providing health support. For example, if the user is in a specific area, the support unit can provide health support available in that area. If the user is traveling, the support unit can also provide health support at the user's travel destination. If the user is at home, the support unit can also provide nearby health support. This allows the optimal support method to be selected by taking the user's geographical location information into consideration.

[0108] The support unit can analyze the user's social media activity to adjust the support content when providing health support. For example, the support unit can provide relevant health support based on health information shared by the user on social media. The support unit can also provide relevant health support based on information about health-related accounts the user follows on social media. The support unit can also provide relevant health support based on information about health-related groups the user participates in on social media. In this way, relevant support content can be provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, provision unit, reservation unit, medical questionnaire creation unit, and support unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives symptom input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes symptoms using a generation AI and performs a simple diagnosis. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated simple diagnosis result to the user. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an appointment with a nearby hospital or a primary care doctor if it is determined that medical treatment by a doctor is necessary. The medical questionnaire creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a medical questionnaire in advance for the medical institution for which a reservation has been made. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health support based on the doctor's diagnosis results. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, provision unit, reservation unit, medical questionnaire creation unit, and support 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 reception unit is realized by the control unit 46A of the smart glasses 214 and receives symptom input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes symptoms using a generation AI and performs a simple diagnosis. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated simple diagnosis results to the user. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an appointment with a nearby hospital or a primary care physician if it is determined that medical treatment by a doctor is necessary. The medical questionnaire creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a medical questionnaire in advance for the medical institution for which a reservation has been made. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health support based on the doctor's diagnosis results. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, generation unit, provision unit, reservation unit, medical questionnaire creation unit, and support unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives symptom input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes symptoms using a generation AI and performs a simple diagnosis. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated simple diagnosis results to the user. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an appointment with a nearby hospital or a primary care doctor if it is determined that medical treatment by a doctor is necessary. The medical questionnaire creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a medical questionnaire in advance for the medical institution for which a reservation has been made. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health support based on the doctor's diagnosis results. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, generation unit, provision unit, reservation unit, medical questionnaire creation unit, and support unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives symptom input from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes symptoms using a generation AI and performs a simple diagnosis. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated simple diagnosis results to the user. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes an appointment with a nearby hospital or a primary care doctor if it is determined that medical treatment by a doctor is necessary. The medical questionnaire creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a medical questionnaire in advance for the medical institution for which a reservation has been made. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health support based on the doctor's diagnosis results.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The medical assistance system may further include a lifestyle habit monitoring unit that monitors the user's lifestyle habits. The lifestyle habit monitoring unit collects data on the user's diet, exercise, sleep, etc., and provides it to the generation unit. The generation unit can perform a more accurate simplified diagnosis based on this data. For example, if the user has not been exercising much recently, the generation unit can take that information into consideration and provide a diagnosis result such as "Fatigue may be due to lack of exercise." Also, if the user has an irregular diet, the generation unit can provide a diagnosis result such as "Poor physical condition may be due to lack of nutrition." Furthermore, based on the user's sleep data, the generation unit can provide a diagnosis result such as "Headache may be due to lack of sleep." This makes it possible to provide a more appropriate diagnosis result based on the user's lifestyle habits.

[0111] The generation unit can estimate the user's emotions and adjust the presentation method of the simple diagnosis based on the estimated user's emotions. For example, if the user is feeling anxious, the generation unit can provide the simple diagnosis using a presentation method that gives a sense of security. If the user is relaxed, the generation unit can provide the simple diagnosis using a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide the simple diagnosis using a concise and to-the-point presentation method. In this way, by adjusting the presentation method of the simple diagnosis according to the user's emotions, more appropriate diagnosis results can be provided.

[0112] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated user's emotions. For example, if the user feels anxious, the reservation unit can provide a simple reservation method and minimize the steps. Alternatively, if the user feels relaxed, the reservation unit can provide detailed reservation options and suggest a customizable reservation method. Furthermore, if the user is in a hurry, the reservation unit can prioritize voice input to enable quick reservations. In this way, a more appropriate reservation method can be provided by adjusting the reservation method according to the user's emotions.

[0113] The medical questionnaire creation unit can estimate the user's emotions and adjust the method for creating the medical questionnaire based on the estimated user's emotions. For example, if the user is feeling anxious, the medical questionnaire creation unit can provide a simple medical questionnaire and minimize input steps. If the user is relaxed, the medical questionnaire creation unit can provide detailed input options and suggest a customizable medical questionnaire. Furthermore, if the user is in a hurry, the medical questionnaire creation unit can prioritize voice input so that the medical questionnaire can be created quickly. In this way, by adjusting the method for creating the medical questionnaire according to the user's emotions, a more appropriate medical questionnaire can be provided.

[0114] The support unit can estimate the user's emotions and adjust the health support method based on the estimated user's emotions. For example, if the user feels anxious, the support unit can provide health support that gives a sense of security. If the user feels relaxed, the support unit can provide detailed health support. Furthermore, if the user is in a hurry, the support unit can provide concise health support that focuses on the main points. In this way, more appropriate support can be provided by adjusting the health support method according to the user's emotions.

[0115] The medical assistance system may further include a health tracking unit that tracks the user's health data over a long period of time. The health tracking unit periodically collects the user's health data and provides it to the generation unit. The generation unit can detect changes in the user's health condition based on this data and provide appropriate advice. For example, if the user's weight is increasing, the generation unit can provide advice such as "recommend exercising to manage weight." If the user's blood pressure is high, the generation unit can provide advice such as "reduce salt intake." If the user's blood sugar level is rising, the generation unit can provide advice such as "reduce sugar intake." This makes it possible to track the user's health condition over a long period of time and provide appropriate advice.

[0116] The medical assistance system may further include a family health management unit that manages health data of the user's family. The family health management unit collects health data of the user's family and provides it to the generation unit. The generation unit can provide advice that takes into account the health status of the entire family based on this data. For example, if there is a family member with allergies, the generation unit can provide advice such as "take measures to prevent allergies." If there is a family member with high blood pressure, the generation unit can provide advice such as "cut down on salt intake." If there is a family member with diabetes, the generation unit can provide advice such as "cut down on sugar intake." This makes it possible to provide appropriate advice that takes into account the health status of the entire family.

[0117] The medical support system can further include a workplace health management unit that manages health data from the user's workplace. The workplace health management unit collects health data from the user's workplace and provides it to the generation unit. The generation unit can provide advice that takes into account the health status of the entire workplace based on this data. For example, if influenza is prevalent in the workplace, the generation unit can provide advice such as "get vaccinated." If stress is rising in the workplace, the generation unit can provide advice such as "recommend relaxation to manage stress." Furthermore, if long working hours are a problem in the workplace, the generation unit can provide advice such as "take appropriate rest." This makes it possible to provide appropriate advice that takes into account the health status of the entire workplace.

[0118] The medical assistance system may further include an exercise data collection unit that collects the user's exercise data. The exercise data collection unit collects the user's exercise data and provides it to the generation unit. The generation unit can provide advice that takes into account the user's exercise habits based on this data. For example, if the user is not getting enough exercise, the generation unit can provide advice such as "We recommend walking 30 minutes every day." If the user is exercising too much, the generation unit can also provide advice such as "Try to exercise moderately." Furthermore, if the user plays a particular sport, the generation unit can also provide training methods suitable for that sport. This makes it possible to provide appropriate advice that takes into account the user's exercise habits.

[0119] The medical assistance system may further include a dietary data collection unit that collects dietary data of the user. The dietary data collection unit collects the user's dietary data and provides it to the generation unit. The generation unit can provide advice that takes into account the user's eating habits based on this data. For example, if the user is undernourished, the generation unit can provide advice such as "try to eat a balanced diet." If the user is overeating, the generation unit can provide advice such as "maintain a moderate amount of food." Furthermore, if the user is allergic to a specific food ingredient, the generation unit can provide advice to avoid that ingredient. This makes it possible to provide appropriate advice that takes into account the user's eating habits.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit receives symptom input from the user. Symptom input from the user includes text input, voice input, selection from options, etc. For example, the reception unit receives symptom input from the user using text input. The reception unit also receives symptom input using voice input or selection from options. Step 2: The generation unit analyzes the symptoms received by the reception unit and performs a simple diagnosis. Using the generation AI, possible illnesses and treatments are presented based on the input symptoms. For example, it can provide simple diagnostic results such as "It's likely a cold" or "It's recommended that you drink plenty of fluids." It can also provide simple diagnostic results such as "It's likely that you have the flu" or "It's recommended that you see a doctor." Step 3: The provider provides the simple diagnostic results generated by the generator. The result generated by the AI ​​is displayed to the user. For example, it is displayed on a smartphone or computer screen. The result can also be read aloud. Step 4: If the generation unit determines that medical treatment by a doctor is necessary, the reservation unit makes a reservation at a nearby hospital or family doctor. The unit searches for the nearest medical institution based on the user's current location information and makes the reservation. For example, the unit obtains current location information from GPS information or IP address, searches for the nearest medical institution, and makes the reservation. Step 5: The medical questionnaire creation unit creates a medical questionnaire in advance for the medical institution for which the reservation has been made by the reservation unit. By inputting the user's basic information, details of symptoms, past medical history, etc., a medical questionnaire is created that will be useful for the doctor when treating the patient. For example, basic information such as name, age, and gender, details such as the time of symptom onset, frequency, and intensity, past diagnostic results, treatment history, etc. are input. Step 6: The support department provides health support based on the doctor's diagnosis provided by the provider department. The generation AI provides health management advice, such as advice on diet and exercise and recommendations for regular health checks.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an input of symptoms from a user; a generation unit that analyzes the symptoms received by the reception unit and performs a simple diagnosis; a providing unit that provides the simplified diagnostic result generated by the generating unit; a reservation unit that makes a reservation at a nearby hospital or a family doctor when the generation unit determines that medical treatment by a doctor is necessary; a medical questionnaire creation unit that creates a medical questionnaire in advance for the medical institution reserved by the reservation unit; a section for providing the medical questionnaire created by the medical questionnaire creation section; a support unit that provides health support based on the doctor's diagnosis results provided by the providing unit. A system characterized by:

2. The generation unit Based on the symptoms entered, it suggests possible illnesses and treatments.

2. The system of claim 1.

3. The reservation unit Search for nearby medical institutions based on the user's current location and make a reservation 2. The system of claim 1.

4. The medical questionnaire creation unit By inputting the user's basic information, details of symptoms, past medical history, etc., a medical questionnaire will be created that doctors can use as a reference when examining the patient.

2. The system of claim 1.

5. The support portion is Providing health management advice based on doctor's diagnosis results 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the symptom input method based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's symptom input history and provides an appropriate input interface 2. The system of claim 1.

8. The reception unit When entering symptoms, filter the input based on the user's current health and lifestyle habits.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize the input symptoms based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When entering symptoms, prioritize relevant symptoms based on the user's geographic location.

2. The system of claim 1.

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