system

Abstract

The system according to the embodiment aims to provide an initial diagnosis based on the user's symptoms, suggest appropriate medical departments, and provide dietary suggestions based on the user's family structure and lifestyle in an integrated manner. [Solution] A system according to an embodiment includes a reception unit, a diagnosis unit, a proposal unit, and a reservation unit. The reception unit accepts input of symptoms from a user. The diagnosis unit performs an initial diagnosis based on the symptoms accepted by the reception unit. The proposal unit proposes a medical department based on the diagnosis results obtained by the diagnosis unit. The reservation unit searches for a hospital with the medical department proposed by the proposal unit and makes a reservation. The proposal unit makes specific meal proposals based on the user's family composition and lifestyle.

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 not providing a unified initial diagnosis based on the user's symptoms, suggesting appropriate medical departments, or suggesting dietary recommendations based on family composition and lifestyle.

[0005] The system according to the embodiment aims to provide an initial diagnosis based on the user's symptoms, suggest appropriate medical departments, and provide dietary suggestions based on the user's family structure and lifestyle in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a diagnosis unit, a suggestion unit, and a reservation unit. The reception unit accepts input of symptoms from a user. The diagnosis unit performs an initial diagnosis based on the symptoms accepted by the reception unit. The suggestion unit suggests a medical department based on the diagnosis results obtained by the diagnosis unit. The reservation unit searches for a hospital with the medical department suggested by the suggestion unit and makes a reservation. The suggestion unit makes specific meal suggestions based on the user's family structure and lifestyle. [Effects of the Invention]

[0007] The system according to the embodiment can centrally perform an initial diagnosis based on the user's symptoms, suggest appropriate medical departments, and even suggest dietary recommendations based on family structure and lifestyle. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management support system according to an embodiment of the present invention is a system equipped with functions for initial diagnosis of illness, searching for hospitals with appropriate medical specialties, and daily dietary management from the perspective of preventive nutrition. When a user feels unwell, this system allows AI to analyze and perform an initial diagnosis. For example, specific symptoms such as headache, fever, or abdominal pain are input. This information is analyzed by AI to determine possible illnesses and their severity. This allows the user to understand the severity of their symptoms. Next, based on the results of the initial diagnosis, the system suggests an appropriate medical specialty to the user. For example, specific medical specialties are suggested, such as neurology for headaches, internal medicine for fevers, and gastroenterology for abdominal pain. The system also searches for and suggests the nearest hospital based on the user's current location and desired consultation hours. This allows the user to quickly visit an appropriate medical institution. Furthermore, a function for daily dietary management from the perspective of preventive nutrition is described below. The system suggests meals based on the user's family structure and lifestyle. For example, families are suggested menus that provide balanced nutrition for the entire family, while single individuals are suggested easy-to-prepare, healthy meals. By eating meals according to the menus suggested by the system, users can maintain a balanced diet. Thus, the present invention is a system that comprehensively supports the user's health through early diagnosis of illness, hospital search for appropriate medical departments, and daily dietary management from the perspective of preventive nutrition. This allows the health management support system to grasp the user's health condition, allowing them to visit appropriate medical institutions, and maintaining health through daily meals.

[0029] A health management support system according to an embodiment includes a reception unit, a diagnosis unit, a suggestion unit, a reservation unit, and a suggestion unit. The reception unit accepts input of symptoms from a user. When a user feels unwell, the user inputs specific symptoms. For example, symptoms such as headache, fever, and abdominal pain can be input. The diagnosis unit performs an initial diagnosis based on the symptoms accepted by the reception unit. The diagnosis unit analyzes the input symptoms using AI and determines possible illnesses and their severity. For example, the AI ​​may indicate the possibility of a migraine or tension headache if the user has a headache based on the input symptoms. The suggestion unit suggests an appropriate medical department based on the diagnosis results obtained by the diagnosis unit. The suggestion unit analyzes the diagnosis results using AI and suggests an appropriate medical department to the user. For example, a neurology department may be suggested for a headache, an internal medicine department for a fever, and a gastroenterology department for abdominal pain. The reservation unit searches for a hospital with the medical department suggested by the suggestion unit and makes a reservation. The reservation unit searches for the nearest hospital based on the user's current location and desired consultation hours and makes a reservation. For example, when a user inputs their current location and specifies their desired consultation time, the reservation unit searches for the nearest hospital and makes a reservation. The suggestion unit makes specific meal suggestions based on the user's family structure and lifestyle. The suggestion unit uses AI to analyze the user's family structure and lifestyle and makes appropriate meal suggestions. For example, it suggests menus that allow all family members to consume balanced nutrition to families, and suggests easy-to-prepare healthy menus to single people. As a result, the health management support system according to the embodiment can support comprehensive health management by making initial diagnoses, suggesting medical departments, making hospital reservations, and suggesting meals based on the user's symptoms.

[0030] The diagnostic unit can make a diagnosis based on past medical data and a case database. The diagnostic unit, for example, makes a diagnosis by referring to past medical data. For example, the diagnostic unit improves the accuracy of the diagnosis by referring to past medical data such as electronic medical records and medical records. The diagnostic unit can also make a diagnosis based on a case database. For example, the diagnostic unit refers to a case database such as a public database or an in-hospital database and makes a diagnosis based on similar cases. In this way, the diagnostic unit can improve the accuracy of the diagnosis by referring to past medical data and the case database. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input past medical data and a case database into AI, which can output a diagnosis result.

[0031] The suggestion unit can search for and suggest hospitals based on the user's current location and desired consultation hours. The suggestion unit, for example, acquires the user's current location and searches for the nearest hospital. For example, the suggestion unit can acquire the user's current location using GPS data, an IP address, or the like, and search for the nearest hospital. The suggestion unit can also acquire the user's desired consultation hours and search for hospitals that are open during those hours. For example, the suggestion unit searches for hospitals that are open based on the desired consultation hours entered by the user. This allows the suggestion unit to suggest the optimal hospital based on the user's current location and desired consultation hours. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's current location and desired consultation hours into AI, which can output the optimal hospital.

[0032] The reservation unit can make a reservation at the proposed hospital. The reservation unit, for example, makes an online reservation at the proposed hospital. For example, the reservation unit accesses the online reservation system of the proposed hospital and makes the reservation. The reservation unit can also make a telephone reservation at the proposed hospital. For example, the reservation unit obtains the phone number of the proposed hospital and makes a telephone reservation on behalf of the user. In this way, the reservation unit makes a reservation at the proposed hospital, enabling the user to be examined promptly. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input reservation information for the proposed hospital into AI, which can complete the reservation.

[0033] The suggestion unit can suggest nutritionally balanced menus for all family members to family members. The suggestion unit, for example, suggests balanced meals for family members. For example, the suggestion unit suggests menus that allow all family members to consume balanced nutrition. The suggestion unit suggests balanced menus by taking into consideration the ratio of nutrients and the type of ingredients. For example, the suggestion unit suggests menus that include a balanced amount of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids. The suggestion unit can also suggest appropriate menus based on the age and health condition of family members. For example, the suggestion unit suggests menus suitable for children and the elderly. In this way, the suggestion unit can support the health of all family members by suggesting balanced meals to family members. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on family composition and health condition into AI, which can output an optimal menu.

[0034] The suggestion unit can suggest healthy menus that are easy to prepare to single people. The suggestion unit, for example, suggests healthy menus that are easy to prepare to single people. For example, the suggestion unit suggests menus that require a short cooking time and are easy to prepare. The suggestion unit suggests menus that are easy to prepare by taking into consideration the ingredients used and the cooking method. For example, the suggestion unit suggests menus that include a lot of vegetables and fruits, or menus that can be easily prepared in a microwave. The suggestion unit can also suggest appropriate menus according to the lifestyle of single people. For example, the suggestion unit suggests menus that can be easily prepared in between busy work schedules. In this way, the suggestion unit can support a healthy diet by suggesting healthy menus that are easy to prepare to single people. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the lifestyle and food preferences of single people into AI, which can output the optimal menu.

[0035] The suggestion unit can collect and analyze data related to the user's dietary history and health condition. The suggestion unit, for example, collects and analyzes the user's dietary history. For example, the suggestion unit collects the user's dietary history using a diet record app or manual input. The suggestion unit can also collect and analyze data related to the user's health condition. For example, the suggestion unit collects data related to the user's health condition using health checkup results or self-reporting. This allows the suggestion unit to make more appropriate diet suggestions based on the user's dietary history and health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's dietary history and health condition data into AI, which can output optimal diet suggestions.

[0036] The reception unit can analyze the user's past symptom input history and provide an optimal input interface. The reception unit, for example, analyzes the user's past symptom input history and provides an optimal 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. Furthermore, the reception unit can 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 reception unit can provide an optimal input interface for the user by analyzing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past symptom input history into AI, which can output an optimal input interface.

[0037] When inputting symptoms, the reception unit can filter the input content based on the user's current health condition and lifestyle habits. The reception unit filters the input content based on, for example, the user's current health condition and lifestyle habits. For example, the reception unit can prioritize and display highly relevant symptoms based on the user's current health condition. Specific symptoms can also be emphasized based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, general symptoms can be filtered and displayed based on the user's age and gender. Thus, the reception unit can input more relevant symptoms by filtering the input content based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's health condition and lifestyle habits into AI, which can then output optimal input content.

[0038] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms by taking into account the user's geographical location information. The reception unit can, for example, prioritize inputting highly relevant symptoms by taking into account the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data, an IP address, or the like, and prioritize displaying symptoms of diseases that are prevalent in the area. Also, if the user is traveling, the reception unit can prioritize displaying symptoms common in the travel destination. Furthermore, if the user is in a specific climate, the reception unit can prioritize displaying symptoms related to that climate. In this way, the reception unit can prioritize inputting symptoms specific to the region by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location information to AI, which can then output the optimal symptoms.

[0039] When a symptom is input, the reception unit can analyze the user's social media activity and input related symptoms. The reception unit, for example, analyzes the user's social media activity and inputs related symptoms. For example, the reception unit can suggest related symptoms based on health information shared by the user on social media. The reception unit can also predict specific symptoms from the user's social media activity and prompt the user to input them. Furthermore, the reception unit can suggest symptoms based on information about health-related accounts the user follows on social media. In this way, the reception unit can input related symptoms by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can then output optimal symptoms.

[0040] The diagnostic unit can improve the accuracy of diagnosis by referring to past medical data and a case database during diagnosis. The diagnostic unit, for example, makes a diagnosis by referring to past medical data. For example, the diagnostic unit improves the accuracy of diagnosis by referring to past medical data such as electronic medical records and medical records. The diagnostic unit can also make a diagnosis based on a case database. For example, the diagnostic unit refers to a case database such as a public database or an in-hospital database and makes a diagnosis based on similar cases. In this way, the diagnostic unit can improve the accuracy of diagnosis by referring to past medical data and a case database. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input past medical data and a case database into AI, which can output a diagnosis result.

[0041] The diagnostic unit can make a diagnosis taking into consideration the user's lifestyle habits and genetic information. The diagnostic unit makes a diagnosis taking into consideration, for example, the user's lifestyle habits. For example, the diagnostic unit makes a diagnosis taking into consideration the user's lifestyle habits, exercise habits, smoking, drinking, etc. The diagnostic unit can also make a diagnosis based on the user's genetic information. For example, the diagnostic unit makes a diagnosis taking into consideration genetic risk based on genetic information such as genetic test results and family history. This enables the diagnostic unit to make a more appropriate diagnosis by taking into consideration the user's lifestyle habits and genetic information. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input data on the user's lifestyle habits and genetic information into AI, which can output a diagnosis result.

[0042] The diagnostic unit can provide diagnostic results by taking into account the user's geographical location information during diagnosis. The diagnostic unit provides diagnostic results by taking into account, for example, the user's geographical location information. For example, the diagnostic unit acquires the user's geographical location information using GPS data, an IP address, or the like, and provides diagnostic results by taking into account diseases prevalent in the area. Furthermore, if the user is traveling, the diagnostic result can be provided by taking into account diseases common in the user's destination. Furthermore, if the user is in a specific climate, the diagnostic unit can provide diagnostic results by taking into account diseases related to that climate. This enables the diagnostic unit to diagnose diseases specific to the region by taking into account the user's geographical location information. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnostic unit can input the user's geographical location information into AI, which then outputs an optimal diagnostic result.

[0043] The diagnostic unit can analyze the user's social media activity during diagnosis and provide relevant diagnostic results. The diagnostic unit can, for example, analyze the user's social media activity and provide relevant diagnostic results. For example, the diagnostic unit can provide relevant diagnostic results based on health information shared by the user on social media. The diagnostic unit can also predict specific diseases from the user's social media activity and provide diagnostic results. Furthermore, the diagnostic unit can provide diagnostic results based on information about health-related accounts the user follows on social media. In this way, the diagnostic unit can provide relevant diagnostic results by analyzing the user's social media activity. Some or all of the above-mentioned processing in the diagnostic unit can be performed, for example, using AI, or can be performed without using AI. For example, the diagnostic unit can input data on the user's social media activity into AI, which can output optimal diagnostic results.

[0044] When making a proposal, the suggestion unit can suggest the most appropriate medical department based on the diagnosis result. The suggestion unit, for example, suggests the most appropriate medical department based on the diagnosis result. For example, in the case of a headache, the suggestion unit can suggest a neurology department. In the case of a fever, the suggestion unit can also suggest an internal medicine department. Furthermore, in the case of abdominal pain, the suggestion unit can suggest a gastroenterology department. In this way, the suggestion unit can suggest the most appropriate medical department based on the diagnosis result, enabling a prompt consultation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the diagnosis result into AI, which can output the most appropriate medical department.

[0045] When making a suggestion, the suggestion unit can suggest the most appropriate medical department by referring to the user's past medical history. The suggestion unit, for example, can suggest the most appropriate medical department by referring to the user's past medical history. For example, the suggestion unit can suggest the most appropriate medical department based on the medical department the user has visited in the past. The suggestion unit can also suggest a related medical department by referring to the user's past medical records. Furthermore, the suggestion unit can suggest an appropriate medical department by taking the user's past medical history into consideration. In this way, the suggestion unit can suggest a more appropriate medical department by referring to the past medical history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past medical history into AI, which can output the most appropriate medical department.

[0046] When making a suggestion, the suggestion unit can suggest the most appropriate medical department by taking into account the user's geographical location information. The suggestion unit, for example, suggests the most appropriate medical department by taking into account the user's geographical location information. For example, the suggestion unit acquires the user's geographical location information using GPS data, an IP address, or the like, and suggests a medical department with a good reputation in the area. If the user is traveling, the suggestion unit can also suggest a reliable medical department at the user's destination. Furthermore, if the user is in a specific climate, the suggestion unit can suggest a medical department that is suitable for that climate. In this way, the suggestion unit can suggest a medical department specific to the area by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI, which then outputs the most appropriate medical department.

[0047] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest a relevant medical department. The suggestion unit, for example, analyzes the user's social media activity and suggests a relevant medical department. For example, the suggestion unit can suggest a relevant medical department based on health information shared by the user on social media. The suggestion unit can also predict and suggest a specific medical department from the user's social media activity. Furthermore, the suggestion unit can suggest a medical department based on information about health-related accounts the user follows on social media. In this way, the suggestion unit can suggest a relevant medical department by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's social media activity into AI, which can then output the optimal medical department.

[0048] The reservation unit can provide the optimal reservation method by referring to the user's past reservation history when making a reservation. The reservation unit can, for example, provide the optimal reservation method by referring to the user's past reservation history. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. The reservation unit can also prioritize reservations for specific time periods based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, the reservation unit can provide the optimal reservation method for the user by referring to the past reservation history. Some or all of the above-described processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the user's past reservation history into AI, which can output the optimal reservation method.

[0049] The reservation unit can make a reservation taking into account the user's current schedule when making a reservation. The reservation unit, for example, makes a reservation taking into account the user's current schedule. For example, the reservation unit references the user's calendar information and suggests a reservation for an available time slot. The reservation unit can also suggest an optimal reservation time based on the user's schedule. Furthermore, the reservation unit can set a reservation reminder according to the user's schedule. This allows the reservation unit to make a more appropriate reservation by taking the user's schedule into consideration. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into AI, which can output the optimal reservation time.

[0050] The reservation unit can provide the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit provides the optimal reservation method by taking into account, for example, the user's geographical location information. For example, the reservation unit can acquire the user's geographical location information using GPS data, an IP address, or the like, and suggest reservation methods available in that area. Also, if the user is traveling, the reservation unit can suggest reservation methods available at the travel destination. Furthermore, if the user is in specific weather conditions, the reservation unit can suggest a reservation method that is suitable for that weather. In this way, the reservation unit can provide a region-specific reservation method by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information into AI, which can then output the optimal reservation method.

[0051] The reservation unit can analyze the user's social media activity at the time of reservation and provide a relevant reservation method. The reservation unit, for example, analyzes the user's social media activity and provides a relevant reservation method. For example, the reservation unit can suggest a relevant reservation method based on information shared by the user on social media. The reservation unit can also predict and suggest a specific reservation method from the user's social media activity. Furthermore, the reservation unit can suggest a reservation method based on information about accounts the user follows on social media. In this way, the reservation unit can provide a relevant reservation method by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input data on the user's social media activity into AI, which can output the optimal reservation method.

[0052] When making a proposal, the suggestion unit can suggest an optimal menu based on the user's family structure and lifestyle. The suggestion unit, for example, suggests an optimal menu based on the user's family structure and lifestyle. For example, the suggestion unit suggests a menu for families that allows all family members to consume balanced nutrition. The suggestion unit can also suggest healthy menus that are easy to prepare for single people. Furthermore, the suggestion unit can suggest an optimal menu based on the user's lifestyle (such as how busy they are at work or their exercise habits). This allows the suggestion unit to suggest an optimal menu based on the user's family structure and lifestyle, enabling more appropriate dietary management. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's family structure and lifestyle into AI, which can then output an optimal menu.

[0053] When making a suggestion, the suggestion unit can suggest an optimal menu by referring to the user's past meal history. The suggestion unit, for example, suggests an optimal menu by referring to the user's past meal history. For example, the suggestion unit suggests a balanced menu based on the user's past meal history. Furthermore, if the user's past meal history indicates a deficiency of a specific nutrient, the suggestion unit can suggest a menu that supplements that nutrient. Furthermore, the suggestion unit can analyze the user's past meal history and suggest a menu tailored to the user's health condition. In this way, the suggestion unit can suggest a more appropriate menu by referring to the user's past meal history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past meal history into AI, which can output an optimal menu.

[0054] When making a proposal, the proposal unit can propose an optimal menu taking into consideration the user's geographical location information. The proposal unit proposes an optimal menu taking into consideration, for example, the user's geographical location information. For example, the proposal unit can acquire the user's geographical location information using GPS data, an IP address, or the like, and propose a menu using ingredients available in the area. Also, if the user is traveling, the proposal unit can propose a menu using ingredients available at the travel destination. Furthermore, if the user is in a specific climate, the proposal unit can propose a menu suitable for that climate. In this way, the proposal unit can propose a region-specific menu by taking into consideration the user's geographical location information. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the user's geographical location information into AI, which can output an optimal menu.

[0055] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related menus. The suggestion unit, for example, analyzes the user's social media activity and suggests related menus. For example, the suggestion unit can suggest related menus based on food information shared by the user on social media. The suggestion unit can also predict and suggest specific menus from the user's social media activity. Furthermore, the suggestion unit can suggest menus based on information about cooking-related accounts the user follows on social media. In this way, the suggestion unit can suggest related menus by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's social media activity into AI, which can then output an optimal menu.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The health management support system can also acquire the user's exercise history and suggest an appropriate exercise program if it detects a lack of exercise. For example, if the user has not exercised for more than a week, the system can suggest a program of light stretching and walking. Furthermore, if the user has set a specific health goal (e.g., weight loss or muscle building), the system can also provide an exercise program tailored to that goal. Furthermore, based on the user's exercise history, it can also re-suggest exercise programs that have been effective in the past. This allows users to maintain their daily exercise habits and improve their health.

[0058] The diagnostic unit can make a diagnosis that takes into account genetic risk based on the user's genetic information. For example, if the user has a specific gene mutation, it can evaluate the risk of a disease associated with that mutation and suggest appropriate preventive measures. It can also recommend tests for early detection of diseases with a high genetic risk based on family history. It can also suggest personalized treatments based on the user's genetic information. As a result, the diagnostic unit can provide more accurate diagnoses and personalized medical care by taking the user's genetic information into account.

[0059] The reservation unit can analyze the user's past reservation history and provide the optimal reservation method. For example, it can prioritize suggesting reservation methods that the user has frequently used in the past. It can also suggest the optimal reservation time based on the time slots the user has made reservations in the past. It can also prioritize suggesting specific medical departments or doctors based on the user's past reservation history. In this way, the reservation unit can provide the user with the most efficient and comfortable reservation experience by taking past reservation history into consideration.

[0060] The suggestion unit can suggest supplements to compensate for specific nutrients based on the user's dietary history and health condition. For example, if the user is deficient in vitamin D, a vitamin D supplement can be suggested. Also, if the user is deficient in iron, an iron supplement can be suggested. Furthermore, it is possible to suggest supplements that are effective in preventing specific diseases according to the user's health condition. In this way, the suggestion unit can complement the user's nutritional balance and provide support for maintaining health.

[0061] The suggestion unit can suggest preventive measures to address health risks specific to a region, taking into account the user's geographical location information. For example, if the user lives in an area where hay fever is common, the suggestion unit can suggest preventive measures to combat hay fever. If the user lives in a tropical region, the suggestion unit can also suggest measures to prevent heatstroke. Furthermore, if the user is traveling, the suggestion unit can also suggest preventive measures to address health risks at the user's destination. In this way, the suggestion unit can provide appropriate preventive measures to address health risks specific to a region, taking into account the user's geographical location information.

[0062] The diagnostic unit can make a diagnosis taking into account the user's lifestyle habits and genetic information. For example, the diagnosis is made taking into account the user's lifestyle habits, exercise habits, smoking, drinking, etc. The diagnosis can also be made taking into account genetic risks based on the user's genetic information. This allows the diagnostic unit to make a more appropriate diagnosis by taking into account the user's lifestyle habits and genetic information.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives input of symptoms from the user. When the user feels unwell, the user inputs specific symptoms. For example, the user can input symptoms such as headache, fever, stomachache, etc. Step 2: The diagnosis unit performs an initial diagnosis based on the symptoms received by the reception unit. The diagnosis unit uses AI to analyze the input symptoms and determine possible illnesses and their severity. For example, if the patient has a headache, the AI ​​may indicate the possibility of a migraine or tension headache based on the input symptoms. Step 3: The suggestion unit proposes an appropriate medical department based on the diagnosis results obtained by the diagnosis unit. The suggestion unit uses AI to analyze the diagnosis results and propose an appropriate medical department to the user. For example, if the patient has a headache, it will suggest a neurology department, if the patient has a fever, it will suggest an internal medicine department, and if the patient has abdominal pain, it will suggest a gastroenterology department. Step 4: The reservation unit searches for hospitals with the medical departments suggested by the suggestion unit and makes a reservation. The reservation unit searches for the nearest hospital based on the user's current location and desired consultation time and makes a reservation. For example, when the user inputs their current location and specifies the desired consultation time, the reservation unit searches for the nearest hospital and makes a reservation. Step 5: The suggestion unit makes specific meal suggestions based on the user's family structure and lifestyle. The suggestion unit uses AI to analyze the user's family structure and lifestyle and make appropriate meal suggestions. For example, it suggests menus that allow the whole family to consume balanced nutrition to families, and suggests easy-to-prepare healthy menus to single people.

[0065] (Example 2) A health management support system according to an embodiment of the present invention is a system equipped with functions for initial diagnosis of illness, searching for hospitals with appropriate medical specialties, and daily dietary management from the perspective of preventive nutrition. When a user feels unwell, this system allows AI to analyze and perform an initial diagnosis. For example, specific symptoms such as headache, fever, or abdominal pain are input. This information is analyzed by AI to determine possible illnesses and their severity. This allows the user to understand the severity of their symptoms. Next, based on the results of the initial diagnosis, the system suggests an appropriate medical specialty to the user. For example, specific medical specialties are suggested, such as neurology for headaches, internal medicine for fevers, and gastroenterology for abdominal pain. The system also searches for and suggests the nearest hospital based on the user's current location and desired consultation hours. This allows the user to quickly visit an appropriate medical institution. Furthermore, a function for daily dietary management from the perspective of preventive nutrition is described below. The system suggests meals based on the user's family structure and lifestyle. For example, families are suggested menus that provide balanced nutrition for the entire family, while single individuals are suggested easy-to-prepare, healthy meals. By eating meals according to the menus suggested by the system, users can maintain a balanced diet. Thus, the present invention is a system that comprehensively supports the user's health through early diagnosis of illness, hospital search for appropriate medical departments, and daily dietary management from the perspective of preventive nutrition. This allows the health management support system to grasp the user's health condition, allowing them to visit appropriate medical institutions, and maintaining health through daily meals.

[0066] A health management support system according to an embodiment includes a reception unit, a diagnosis unit, a suggestion unit, a reservation unit, and a suggestion unit. The reception unit accepts input of symptoms from a user. When a user feels unwell, the user inputs specific symptoms. For example, symptoms such as headache, fever, and abdominal pain can be input. The diagnosis unit performs an initial diagnosis based on the symptoms accepted by the reception unit. The diagnosis unit analyzes the input symptoms using AI and determines possible illnesses and their severity. For example, the AI ​​may indicate the possibility of a migraine or tension headache if the user has a headache based on the input symptoms. The suggestion unit suggests an appropriate medical department based on the diagnosis results obtained by the diagnosis unit. The suggestion unit analyzes the diagnosis results using AI and suggests an appropriate medical department to the user. For example, a neurology department may be suggested for a headache, an internal medicine department for a fever, and a gastroenterology department for abdominal pain. The reservation unit searches for a hospital with the medical department suggested by the suggestion unit and makes a reservation. The reservation unit searches for the nearest hospital based on the user's current location and desired consultation hours and makes a reservation. For example, when a user inputs their current location and specifies their desired consultation time, the reservation unit searches for the nearest hospital and makes a reservation. The suggestion unit makes specific meal suggestions based on the user's family structure and lifestyle. The suggestion unit uses AI to analyze the user's family structure and lifestyle and makes appropriate meal suggestions. For example, it suggests menus that allow all family members to consume balanced nutrition to families, and suggests easy-to-prepare healthy menus to single people. As a result, the health management support system according to the embodiment can support comprehensive health management by making initial diagnoses, suggesting medical departments, making hospital reservations, and suggesting meals based on the user's symptoms.

[0067] The diagnostic unit can make a diagnosis based on past medical data and a case database. The diagnostic unit, for example, makes a diagnosis by referring to past medical data. For example, the diagnostic unit improves the accuracy of the diagnosis by referring to past medical data such as electronic medical records and medical records. The diagnostic unit can also make a diagnosis based on a case database. For example, the diagnostic unit refers to a case database such as a public database or an in-hospital database and makes a diagnosis based on similar cases. In this way, the diagnostic unit can improve the accuracy of the diagnosis by referring to past medical data and the case database. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input past medical data and a case database into AI, which can output a diagnosis result.

[0068] The suggestion unit can search for and suggest hospitals based on the user's current location and desired consultation hours. The suggestion unit, for example, acquires the user's current location and searches for the nearest hospital. For example, the suggestion unit can acquire the user's current location using GPS data, an IP address, or the like, and search for the nearest hospital. The suggestion unit can also acquire the user's desired consultation hours and search for hospitals that are open during those hours. For example, the suggestion unit searches for hospitals that are open based on the desired consultation hours entered by the user. This allows the suggestion unit to suggest the optimal hospital based on the user's current location and desired consultation hours. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's current location and desired consultation hours into AI, which can output the optimal hospital.

[0069] The reservation unit can make a reservation at the proposed hospital. The reservation unit, for example, makes an online reservation at the proposed hospital. For example, the reservation unit accesses the online reservation system of the proposed hospital and makes the reservation. The reservation unit can also make a telephone reservation at the proposed hospital. For example, the reservation unit obtains the phone number of the proposed hospital and makes a telephone reservation on behalf of the user. In this way, the reservation unit makes a reservation at the proposed hospital, enabling the user to be examined promptly. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input reservation information for the proposed hospital into AI, which can complete the reservation.

[0070] The suggestion unit can suggest nutritionally balanced menus for all family members to family members. The suggestion unit, for example, suggests balanced meals for family members. For example, the suggestion unit suggests menus that allow all family members to consume balanced nutrition. The suggestion unit suggests balanced menus by taking into consideration the ratio of nutrients and the type of ingredients. For example, the suggestion unit suggests menus that include a balanced amount of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids. The suggestion unit can also suggest appropriate menus based on the age and health condition of family members. For example, the suggestion unit suggests menus suitable for children and the elderly. In this way, the suggestion unit can support the health of all family members by suggesting balanced meals to family members. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on family composition and health condition into AI, which can output an optimal menu.

[0071] The suggestion unit can suggest healthy menus that are easy to prepare to single people. The suggestion unit, for example, suggests healthy menus that are easy to prepare to single people. For example, the suggestion unit suggests menus that require a short cooking time and are easy to prepare. The suggestion unit suggests menus that are easy to prepare by taking into consideration the ingredients used and the cooking method. For example, the suggestion unit suggests menus that include a lot of vegetables and fruits, or menus that can be easily prepared in a microwave. The suggestion unit can also suggest appropriate menus according to the lifestyle of single people. For example, the suggestion unit suggests menus that can be easily prepared in between busy work schedules. In this way, the suggestion unit can support a healthy diet by suggesting healthy menus that are easy to prepare to single people. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the lifestyle and food preferences of single people into AI, which can output the optimal menu.

[0072] The suggestion unit can collect and analyze data related to the user's dietary history and health condition. The suggestion unit, for example, collects and analyzes the user's dietary history. For example, the suggestion unit collects the user's dietary history using a diet record app or manual input. The suggestion unit can also collect and analyze data related to the user's health condition. For example, the suggestion unit collects data related to the user's health condition using health checkup results or self-reporting. This allows the suggestion unit to make more appropriate diet suggestions based on the user's dietary history and health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's dietary history and health condition data into AI, which can output optimal diet suggestions.

[0073] The reception unit can estimate the user's emotions and adjust the symptom input method based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the input method. For example, the reception unit estimates the user's emotions using facial expression recognition or voice analysis. If the user is stressed, a simple interface is provided to minimize input steps. If the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input symptoms. This allows the reception unit to adjust the input method according to the user's emotions, enabling more appropriate symptom input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into AI, which then outputs the optimal input method.

[0074] The reception unit can analyze the user's past symptom input history and provide an optimal input interface. The reception unit, for example, analyzes the user's past symptom input history and provides an optimal 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. Furthermore, the reception unit can 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 reception unit can provide an optimal input interface for the user by analyzing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past symptom input history into AI, which can output an optimal input interface.

[0075] When inputting symptoms, the reception unit can filter the input content based on the user's current health condition and lifestyle habits. The reception unit filters the input content based on, for example, the user's current health condition and lifestyle habits. For example, the reception unit can prioritize and display highly relevant symptoms based on the user's current health condition. Specific symptoms can also be emphasized based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, general symptoms can be filtered and displayed based on the user's age and gender. Thus, the reception unit can input more relevant symptoms by filtering the input content based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's health condition and lifestyle habits into AI, which can then output optimal input content.

[0076] The reception unit can estimate the user's emotions and prioritize the input symptoms based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes the input symptoms. For example, the reception unit estimates the user's emotions using facial expression recognition or voice analysis. If the user is feeling anxious, the reception unit can prioritize displaying serious symptoms. If the user is relaxed, the reception unit can prioritize displaying general symptoms. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying symptoms that require prompt attention. This enables the reception unit to prioritize symptoms based on the user's emotions, enabling a more accurate diagnosis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into an AI, which can then output an optimal priority.

[0077] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms by taking into account the user's geographical location information. The reception unit can, for example, prioritize inputting highly relevant symptoms by taking into account the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data, an IP address, or the like, and prioritize displaying symptoms of diseases that are prevalent in the area. Also, if the user is traveling, the reception unit can prioritize displaying symptoms common in the travel destination. Furthermore, if the user is in a specific climate, the reception unit can prioritize displaying symptoms related to that climate. In this way, the reception unit can prioritize inputting symptoms specific to the region by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location information to AI, which can then output the optimal symptoms.

[0078] When a symptom is input, the reception unit can analyze the user's social media activity and input related symptoms. The reception unit, for example, analyzes the user's social media activity and inputs related symptoms. For example, the reception unit can suggest related symptoms based on health information shared by the user on social media. The reception unit can also predict specific symptoms from the user's social media activity and prompt the user to input them. Furthermore, the reception unit can suggest symptoms based on information about health-related accounts the user follows on social media. In this way, the reception unit can input related symptoms by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can then output optimal symptoms.

[0079] The diagnosis unit can estimate the user's emotions and adjust the way the diagnosis result is presented based on the estimated user's emotions. The diagnosis unit, for example, estimates the user's emotions and adjusts the way the diagnosis result is presented. For example, the diagnosis unit estimates the user's emotions using facial expression recognition or voice analysis. If the user is feeling anxious, the diagnosis result can be provided in an expression that gives a sense of security. If the user is relaxed, the diagnosis result can be provided in an expression that includes detailed information. Furthermore, if the user is in a hurry, the diagnosis result can be provided in a concise and to-the-point expression. This allows the diagnosis unit to adjust the way the diagnosis result is presented based on the user's emotions, thereby providing a more appropriate diagnosis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the diagnosis unit may be performed using an AI, or may be performed without an AI. For example, the diagnosis unit can input the user's emotion data into an AI, which then outputs the optimal expression.

[0080] The diagnostic unit can improve the accuracy of diagnosis by referring to past medical data and a case database during diagnosis. The diagnostic unit, for example, makes a diagnosis by referring to past medical data. For example, the diagnostic unit improves the accuracy of diagnosis by referring to past medical data such as electronic medical records and medical records. The diagnostic unit can also make a diagnosis based on a case database. For example, the diagnostic unit refers to a case database such as a public database or an in-hospital database and makes a diagnosis based on similar cases. In this way, the diagnostic unit can improve the accuracy of diagnosis by referring to past medical data and a case database. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input past medical data and a case database into AI, which can output a diagnosis result.

[0081] The diagnostic unit can make a diagnosis taking into consideration the user's lifestyle habits and genetic information. The diagnostic unit makes a diagnosis taking into consideration, for example, the user's lifestyle habits. For example, the diagnostic unit makes a diagnosis taking into consideration the user's lifestyle habits, exercise habits, smoking, drinking, etc. The diagnostic unit can also make a diagnosis based on the user's genetic information. For example, the diagnostic unit makes a diagnosis taking into consideration genetic risk based on genetic information such as genetic test results and family history. This enables the diagnostic unit to make a more appropriate diagnosis by taking into consideration the user's lifestyle habits and genetic information. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input data on the user's lifestyle habits and genetic information into AI, which can output a diagnosis result.

[0082] The diagnostic unit can estimate the user's emotions and prioritize the diagnostic results based on the estimated user emotions. The diagnostic unit, for example, estimates the user's emotions and prioritizes the diagnostic results. For example, the diagnostic unit estimates the user's emotions using facial expression recognition or voice analysis. If the user is feeling anxious, serious symptoms can be prioritized as diagnostic results. If the user is relaxed, general symptoms can be prioritized as diagnostic results. Furthermore, if the user is in a hurry, symptoms requiring prompt attention can be prioritized as diagnostic results. This allows the diagnostic unit to prioritize the diagnostic results based on the user's emotions, enabling a more accurate diagnosis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the diagnostic unit may be performed using an AI, or may be performed without an AI. For example, the diagnostic unit can input the user's emotion data into an AI, which then outputs an optimal priority.

[0083] The diagnostic unit can provide diagnostic results by taking into account the user's geographical location information during diagnosis. The diagnostic unit provides diagnostic results by taking into account, for example, the user's geographical location information. For example, the diagnostic unit acquires the user's geographical location information using GPS data, an IP address, or the like, and provides diagnostic results by taking into account diseases prevalent in the area. Furthermore, if the user is traveling, the diagnostic result can be provided by taking into account diseases common in the user's destination. Furthermore, if the user is in a specific climate, the diagnostic unit can provide diagnostic results by taking into account diseases related to that climate. This enables the diagnostic unit to diagnose diseases specific to the region by taking into account the user's geographical location information. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, AI, or may be performed without AI. For example, the diagnostic unit can input the user's geographical location information into AI, which then outputs an optimal diagnostic result.

[0084] The diagnostic unit can analyze the user's social media activity during diagnosis and provide relevant diagnostic results. The diagnostic unit can, for example, analyze the user's social media activity and provide relevant diagnostic results. For example, the diagnostic unit can provide relevant diagnostic results based on health information shared by the user on social media. The diagnostic unit can also predict specific diseases from the user's social media activity and provide diagnostic results. Furthermore, the diagnostic unit can provide diagnostic results based on information about health-related accounts the user follows on social media. In this way, the diagnostic unit can provide relevant diagnostic results by analyzing the user's social media activity. Some or all of the above-mentioned processing in the diagnostic unit can be performed, for example, using AI, or can be performed without using AI. For example, the diagnostic unit can input data on the user's social media activity into AI, which can output optimal diagnostic results.

[0085] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way the suggestion is expressed. For example, the suggestion unit estimates the user's emotion using facial expression recognition or voice analysis. If the user is feeling anxious, the suggestion unit can make a suggestion using an expression that gives a sense of security. If the user is relaxed, the suggestion unit can make a suggestion using an expression that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion using a concise and to-the-point expression. This enables the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion, thereby enabling more appropriate suggestions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI, which then outputs an optimal expression.

[0086] When making a proposal, the suggestion unit can suggest the most appropriate medical department based on the diagnosis result. The suggestion unit, for example, suggests the most appropriate medical department based on the diagnosis result. For example, in the case of a headache, the suggestion unit can suggest a neurology department. In the case of a fever, the suggestion unit can also suggest an internal medicine department. Furthermore, in the case of abdominal pain, the suggestion unit can suggest a gastroenterology department. In this way, the suggestion unit can suggest the most appropriate medical department based on the diagnosis result, enabling a prompt consultation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the diagnosis result into AI, which can output the most appropriate medical department.

[0087] When making a suggestion, the suggestion unit can suggest the most appropriate medical department by referring to the user's past medical history. The suggestion unit, for example, can suggest the most appropriate medical department by referring to the user's past medical history. For example, the suggestion unit can suggest the most appropriate medical department based on the medical department the user has visited in the past. The suggestion unit can also suggest a related medical department by referring to the user's past medical records. Furthermore, the suggestion unit can suggest an appropriate medical department by taking the user's past medical history into consideration. In this way, the suggestion unit can suggest a more appropriate medical department by referring to the past medical history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past medical history into AI, which can output the most appropriate medical department.

[0088] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and determines the priority of suggestions. For example, the suggestion unit estimates the user's emotions using facial expression recognition or voice analysis. If the user is feeling anxious, the suggestion unit can prioritize suggesting medical departments that deal with serious symptoms. If the user is relaxed, the suggestion unit can prioritize suggesting medical departments that deal with general symptoms. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting medical departments that deal with symptoms that require prompt attention. This enables the suggestion unit to determine the priority of suggestions based on the user's emotions, thereby enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without an AI. For example, the suggestion unit can input the user's emotion data into an AI, which then outputs an optimal priority.

[0089] When making a suggestion, the suggestion unit can suggest the most appropriate medical department by taking into account the user's geographical location information. The suggestion unit, for example, suggests the most appropriate medical department by taking into account the user's geographical location information. For example, the suggestion unit acquires the user's geographical location information using GPS data, an IP address, or the like, and suggests a medical department with a good reputation in the area. If the user is traveling, the suggestion unit can also suggest a reliable medical department at the user's destination. Furthermore, if the user is in a specific climate, the suggestion unit can suggest a medical department that is suitable for that climate. In this way, the suggestion unit can suggest a medical department specific to the area by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI, which then outputs the most appropriate medical department.

[0090] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest a relevant medical department. The suggestion unit, for example, analyzes the user's social media activity and suggests a relevant medical department. For example, the suggestion unit can suggest a relevant medical department based on health information shared by the user on social media. The suggestion unit can also predict and suggest a specific medical department from the user's social media activity. Furthermore, the suggestion unit can suggest a medical department based on information about health-related accounts the user follows on social media. In this way, the suggestion unit can suggest a relevant medical department by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's social media activity into AI, which can then output the optimal medical department.

[0091] The reservation unit can estimate a user's emotions and adjust the reservation method based on the estimated user emotions. The reservation unit, for example, estimates a user's emotions and adjusts the reservation method. For example, the reservation unit estimates a user's emotions using facial expression recognition or voice analysis. If the user is feeling anxious, the reservation unit can provide a simple and easy-to-understand reservation method. If the user is relaxed, the reservation unit can provide detailed reservation options. Furthermore, if the user is in a hurry, the reservation unit can provide a method for quickly completing the reservation. This allows the reservation unit to adjust the reservation method according to the user's emotions, enabling more appropriate reservations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit may be performed using an AI, for example, or without an AI. For example, the reservation unit can input user emotion data into an AI, which then outputs the optimal reservation method.

[0092] The reservation unit can provide the optimal reservation method by referring to the user's past reservation history when making a reservation. The reservation unit can, for example, provide the optimal reservation method by referring to the user's past reservation history. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. The reservation unit can also prioritize reservations for specific time periods based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, the reservation unit can provide the optimal reservation method for the user by referring to the past reservation history. Some or all of the above-described processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the user's past reservation history into AI, which can output the optimal reservation method.

[0093] The reservation unit can make a reservation taking into account the user's current schedule when making a reservation. The reservation unit, for example, makes a reservation taking into account the user's current schedule. For example, the reservation unit references the user's calendar information and suggests a reservation for an available time slot. The reservation unit can also suggest an optimal reservation time based on the user's schedule. Furthermore, the reservation unit can set a reservation reminder according to the user's schedule. This allows the reservation unit to make a more appropriate reservation by taking the user's schedule into consideration. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into AI, which can output the optimal reservation time.

[0094] The reservation unit can estimate a user's emotions and determine the priority of reservations based on the estimated user emotions. The reservation unit, for example, estimates a user's emotions and determines the priority of reservations. For example, the reservation unit estimates a user's emotions using facial expression recognition or voice analysis. If a user feels anxious, reservations requiring immediate attention can be prioritized. If a user feels relaxed, general reservations can be prioritized. Furthermore, if a user is in a hurry, reservations requiring immediate attention can be prioritized. This allows the reservation unit to determine the priority of reservations based on the user's emotions, enabling more appropriate reservations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit may be performed using an AI, for example, or without an AI. For example, the reservation unit can input user emotion data into an AI, which then outputs an optimal priority.

[0095] The reservation unit can provide the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit provides the optimal reservation method by taking into account, for example, the user's geographical location information. For example, the reservation unit can acquire the user's geographical location information using GPS data, an IP address, or the like, and suggest reservation methods available in that area. Also, if the user is traveling, the reservation unit can suggest reservation methods available at the travel destination. Furthermore, if the user is in specific weather conditions, the reservation unit can suggest a reservation method that is suitable for that weather. In this way, the reservation unit can provide a region-specific reservation method by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information into AI, which can then output the optimal reservation method.

[0096] The reservation unit can analyze the user's social media activity at the time of reservation and provide a relevant reservation method. The reservation unit, for example, analyzes the user's social media activity and provides a relevant reservation method. For example, the reservation unit can suggest a relevant reservation method based on information shared by the user on social media. The reservation unit can also predict and suggest a specific reservation method from the user's social media activity. Furthermore, the reservation unit can suggest a reservation method based on information about accounts the user follows on social media. In this way, the reservation unit can provide a relevant reservation method by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input data on the user's social media activity into AI, which can output the optimal reservation method.

[0097] The suggestion unit can estimate the user's emotions and adjust the way meal suggestions are expressed based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the way meal suggestions are expressed. For example, the suggestion unit estimates the user's emotions using facial expression recognition or voice analysis. If the user is feeling anxious, meal suggestions can be made using an expression that gives a sense of security. If the user is relaxed, meal suggestions can be made using an expression that includes detailed information. Furthermore, if the user is in a hurry, meal suggestions can be made using an expression that is concise and to the point. This allows the suggestion unit to adjust the way meal suggestions are expressed according to the user's emotions, enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI, which then outputs an optimal expression.

[0098] When making a proposal, the suggestion unit can suggest an optimal menu based on the user's family structure and lifestyle. The suggestion unit, for example, suggests an optimal menu based on the user's family structure and lifestyle. For example, the suggestion unit suggests a menu for families that allows all family members to consume balanced nutrition. The suggestion unit can also suggest healthy menus that are easy to prepare for single people. Furthermore, the suggestion unit can suggest an optimal menu based on the user's lifestyle (such as how busy they are at work or their exercise habits). This allows the suggestion unit to suggest an optimal menu based on the user's family structure and lifestyle, enabling more appropriate dietary management. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's family structure and lifestyle into AI, which can then output an optimal menu.

[0099] When making a suggestion, the suggestion unit can suggest an optimal menu by referring to the user's past meal history. The suggestion unit, for example, suggests an optimal menu by referring to the user's past meal history. For example, the suggestion unit suggests a balanced menu based on the user's past meal history. Furthermore, if the user's past meal history indicates a deficiency of a specific nutrient, the suggestion unit can suggest a menu that supplements that nutrient. Furthermore, the suggestion unit can analyze the user's past meal history and suggest a menu tailored to the user's health condition. In this way, the suggestion unit can suggest a more appropriate menu by referring to the user's past meal history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past meal history into AI, which can output an optimal menu.

[0100] The suggestion unit can estimate the user's emotions and prioritize meal suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and prioritizes meal suggestions. For example, the suggestion unit estimates the user's emotions using facial expression recognition or voice analysis. If the user is feeling anxious, the suggestion unit can prioritize menus that provide a sense of security. If the user is relaxed, the suggestion unit can prioritize menus that include detailed information. If the user is in a hurry, the suggestion unit can prioritize menus that are concise and to the point. This enables the suggestion unit to prioritize meal suggestions based on the user's emotions, thereby enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without an AI. For example, the suggestion unit can input the user's emotion data into an AI, which then outputs an optimal priority.

[0101] When making a proposal, the proposal unit can propose an optimal menu taking into consideration the user's geographical location information. The proposal unit proposes an optimal menu taking into consideration, for example, the user's geographical location information. For example, the proposal unit can acquire the user's geographical location information using GPS data, an IP address, or the like, and propose a menu using ingredients available in the area. Also, if the user is traveling, the proposal unit can propose a menu using ingredients available at the travel destination. Furthermore, if the user is in a specific climate, the proposal unit can propose a menu suitable for that climate. In this way, the proposal unit can propose a region-specific menu by taking into consideration the user's geographical location information. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the user's geographical location information into AI, which can output an optimal menu.

[0102] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related menus. The suggestion unit, for example, analyzes the user's social media activity and suggests related menus. For example, the suggestion unit can suggest related menus based on food information shared by the user on social media. The suggestion unit can also predict and suggest specific menus from the user's social media activity. Furthermore, the suggestion unit can suggest menus based on information about cooking-related accounts the user follows on social media. In this way, the suggestion unit can suggest related menus by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's social media activity into AI, which can then output an optimal menu. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, diagnosis unit, suggestion unit, reservation unit, and suggestion 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 reception device 38 of the smart device 14 and accepts input of symptoms from the user. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input symptoms using AI to determine possible illnesses and their severity. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate medical department based on the diagnosis results. The reservation unit is realized by the control unit 46A of the smart device 14 and searches for the nearest hospital based on the user's current location and desired consultation hours and makes a reservation. The suggestion unit is realized by the control unit 46A of the smart device 14 and makes specific meal suggestions based on the user's family composition and lifestyle. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, diagnosis unit, suggestion unit, reservation unit, and suggestion 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 microphone 238 of the smart glasses 214 and accepts input of symptoms from the user. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input symptoms using AI to determine possible illnesses and their severity. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate medical department based on the diagnosis results. The reservation unit is realized by the control unit 46A of the smart glasses 214 and searches for the nearest hospital based on the user's current location and desired consultation hours and makes a reservation. The suggestion unit is realized by the control unit 46A of the smart glasses 214 and makes specific meal suggestions based on the user's family composition and lifestyle. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, diagnosis unit, suggestion unit, reservation unit, and suggestion unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and accepts input of symptoms from the user. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input symptoms using AI to determine possible illnesses and their severity. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate medical department based on the diagnosis results. The reservation unit is realized by the control unit 46A of the headset terminal 314 and searches for the nearest hospital based on the user's current location and desired consultation hours and makes a reservation. The suggestion unit is realized by the control unit 46A of the headset terminal 314 and makes specific meal suggestions based on the user's family composition and lifestyle. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, diagnosis unit, suggestion unit, reservation unit, and suggestion 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 microphone 238 of the robot 414 and accepts input of symptoms from the user. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input symptoms using AI to determine possible illnesses and their severity. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate medical department based on the diagnosis results. The reservation unit is realized by the control unit 46A of the robot 414 and searches for the nearest hospital based on the user's current location and desired consultation hours and makes a reservation. The suggestion unit is realized by the control unit 46A of the robot 414 and makes specific meal suggestions based on the user's family composition and lifestyle.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The health management support system can also acquire the user's exercise history and suggest an appropriate exercise program if it detects a lack of exercise. For example, if the user has not exercised for more than a week, the system can suggest a program of light stretching and walking. Furthermore, if the user has set a specific health goal (e.g., weight loss or muscle building), the system can also provide an exercise program tailored to that goal. Furthermore, based on the user's exercise history, it can also re-suggest exercise programs that have been effective in the past. This allows users to maintain their daily exercise habits and improve their health.

[0105] The diagnostic unit can make a diagnosis that takes into account genetic risk based on the user's genetic information. For example, if the user has a specific gene mutation, it can evaluate the risk of a disease associated with that mutation and suggest appropriate preventive measures. It can also recommend tests for early detection of diseases with a high genetic risk based on family history. It can also suggest personalized treatments based on the user's genetic information. As a result, the diagnostic unit can provide more accurate diagnoses and personalized medical care by taking the user's genetic information into account.

[0106] The suggestion unit can estimate the user's emotions and adjust the suggested medical departments based on the estimated user's emotions. For example, if the user feels anxious, it can prioritize suggesting medical departments that give a sense of security. Also, if the user feels relaxed, it can suggest medical departments that include detailed information. Furthermore, if the user is in a hurry, it can prioritize suggesting medical departments that can respond quickly. In this way, the suggestion unit can support the user in selecting a more appropriate medical institution by adjusting the suggested medical departments according to the user's emotions.

[0107] The reservation unit can analyze the user's past reservation history and provide the optimal reservation method. For example, it can prioritize suggesting reservation methods that the user has frequently used in the past. It can also suggest the optimal reservation time based on the time slots the user has made reservations in the past. It can also prioritize suggesting specific medical departments or doctors based on the user's past reservation history. In this way, the reservation unit can provide the user with the most efficient and comfortable reservation experience by taking past reservation history into consideration.

[0108] The suggestion unit can suggest supplements to compensate for specific nutrients based on the user's dietary history and health condition. For example, if the user is deficient in vitamin D, a vitamin D supplement can be suggested. Also, if the user is deficient in iron, an iron supplement can be suggested. Furthermore, it is possible to suggest supplements that are effective in preventing specific diseases according to the user's health condition. In this way, the suggestion unit can complement the user's nutritional balance and provide support for maintaining health.

[0109] The diagnostic unit can estimate the user's emotions and adjust the way in which the diagnostic result is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the diagnostic result can be provided in an expression that gives a sense of security. If the user is relaxed, the diagnostic result can be provided in an expression that includes detailed information. Furthermore, if the user is in a hurry, the diagnostic result can be provided in a concise and to-the-point expression. In this way, the diagnostic unit can provide a more appropriate diagnostic result by adjusting the way in which the diagnostic result is expressed based on the user's emotions.

[0110] The suggestion unit can suggest preventive measures to address health risks specific to a region, taking into account the user's geographical location information. For example, if the user lives in an area where hay fever is common, the suggestion unit can suggest preventive measures to combat hay fever. If the user lives in a tropical region, the suggestion unit can also suggest measures to prevent heatstroke. Furthermore, if the user is traveling, the suggestion unit can also suggest preventive measures to address health risks at the user's destination. In this way, the suggestion unit can provide appropriate preventive measures to address health risks specific to a region, taking into account the user's geographical location information.

[0111] The suggestion unit can estimate the user's emotions and adjust the way meal suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling anxious, meal suggestions can be made in an expression that gives a sense of security. If the user is relaxed, meal suggestions can be made in an expression that includes detailed information. Furthermore, if the user is in a hurry, meal suggestions can be made in a concise and to-the-point expression. This allows the suggestion unit to make more appropriate suggestions by adjusting the way meal suggestions are expressed according to the user's emotions.

[0112] The diagnostic unit can make a diagnosis taking into account the user's lifestyle habits and genetic information. For example, the diagnosis is made taking into account the user's lifestyle habits, exercise habits, smoking, drinking, etc. The diagnosis can also be made taking into account genetic risks based on the user's genetic information. This allows the diagnostic unit to make a more appropriate diagnosis by taking into account the user's lifestyle habits and genetic information.

[0113] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is feeling anxious, it can prioritize suggesting medical departments that deal with serious symptoms. Also, if the user is relaxed, it can prioritize suggesting medical departments that deal with general symptoms. Furthermore, if the user is in a hurry, it can prioritize suggesting medical departments that deal with symptoms that require prompt attention. In this way, the suggestion unit can determine the priority of suggestions based on the user's emotions, thereby enabling more appropriate suggestions.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit receives input of symptoms from the user. When the user feels unwell, the user inputs specific symptoms. For example, the user can input symptoms such as headache, fever, stomachache, etc. Step 2: The diagnosis unit performs an initial diagnosis based on the symptoms received by the reception unit. The diagnosis unit uses AI to analyze the input symptoms and determine possible illnesses and their severity. For example, if the patient has a headache, the AI ​​may indicate the possibility of a migraine or tension headache based on the input symptoms. Step 3: The suggestion unit proposes an appropriate medical department based on the diagnosis results obtained by the diagnosis unit. The suggestion unit uses AI to analyze the diagnosis results and propose an appropriate medical department to the user. For example, if the patient has a headache, it will suggest a neurology department, if the patient has a fever, it will suggest an internal medicine department, and if the patient has abdominal pain, it will suggest a gastroenterology department. Step 4: The reservation unit searches for hospitals with the medical departments suggested by the suggestion unit and makes a reservation. The reservation unit searches for the nearest hospital based on the user's current location and desired consultation time and makes a reservation. For example, when the user inputs their current location and specifies the desired consultation time, the reservation unit searches for the nearest hospital and makes a reservation. Step 5: The suggestion unit makes specific meal suggestions based on the user's family structure and lifestyle. The suggestion unit uses AI to analyze the user's family structure and lifestyle and make appropriate meal suggestions. For example, it suggests menus that allow the whole family to consume balanced nutrition to families, and suggests easy-to-prepare healthy menus to single people.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 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 diagnosis unit that performs an initial diagnosis based on the symptoms received by the reception unit; a suggestion unit that suggests a medical department based on the diagnosis result obtained by the diagnosis unit; a reservation unit that searches for hospitals with the medical departments suggested by the suggestion unit and makes reservations; a suggestion unit that makes specific meal suggestions based on the user's family structure and lifestyle. A system characterized by:

2. The diagnostic unit Diagnosis is based on past medical data and case databases 2. The system of claim 1.

3. The proposal unit Search and suggest hospitals based on the user's current location and desired consultation hours 2. The system of claim 1.

4. The reservation unit Make a suggested hospital appointment 2. The system of claim 1.

5. The proposal unit For families, we offer nutritionally balanced menus for the whole family.

2. The system of claim 1.

6. The proposal unit Propose easy-to-prepare healthy menus to single people 2. The system of claim 1.

7. The proposal unit Collect and analyze data on users' dietary history and health status 2. The system of claim 1.

8. The reception unit Inferring the user's emotions and adjusting the symptom input method based on the estimated user emotions 2. The system of claim 1.

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

10. 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.

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