System

Abstract

An object of the system according to the embodiment is to propose exercise and meal menus for improving lifestyle-related diseases and to provide continuous support.SOLUTION: A system includes a basic information input part, an analysis part, a proposal part, and a support part. The basic information input unit inputs basic information. The analysis unit analyzes the basic information input by the basic information input unit. The proposal unit proposes an exercise menu or a meal menu for one week on the basis of a result analyzed by the analysis unit. The support unit supports improvement based on the menu proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not providing sufficient support by proposing specific exercise and dietary menus aimed at improving lifestyle-related diseases.

[0005] The system according to the embodiment aims to propose exercise and meal menus aimed at improving lifestyle-related diseases and provide continuous support. [Means for solving the problem]

[0006] The system according to the embodiment includes a basic information input unit, an analysis unit, a proposal unit, and a support unit. The basic information input unit inputs basic information. The analysis unit analyzes the basic information input by the basic information input unit. The proposal unit proposes a one-week exercise menu and meal menu based on the results of the analysis by the analysis unit. The support unit supports improvement based on the menu proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose exercise and meal menus aimed at improving lifestyle-related diseases and provide continuous support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health support system according to an embodiment of the present invention is a system in which a user inputs basic information, and a generation AI analyzes the information, proposes exercise and meal menus, and provides support for improvement. As a result, the health support system can improve the user's health and support the prevention and improvement of lifestyle-related diseases.

[0029] A health support system according to an embodiment includes a basic information input unit, an analysis unit, a suggestion unit, and a support unit. The basic information input unit allows a user to input basic information. For example, the user can input information such as height, weight, body fat percentage, blood pressure, dietary content, and alcohol intake. The basic information input unit can also store the input information as digital data. The analysis unit analyzes the basic information input by the basic information input unit. For example, the generation AI analyzes the user's health status and assesses the risk of lifestyle-related diseases. The generation AI can also predict changes in the user's health status based on the user's basic information. The suggestion unit suggests a weekly exercise menu and meal menu based on the results of the analysis by the analysis unit. For example, the generation AI suggests an optimal exercise menu for the user. The exercise menu may include walking, yoga, strength training, etc. The generation AI also suggests an optimal meal menu for the user. The meal menu may include oatmeal, salad, fish, etc. The support unit supports improvement based on the menu suggested by the suggestion unit. For example, the generation AI monitors the user's progress and provides advice as needed. Furthermore, the generation AI can set achievement points and provide rewards to maintain the user's motivation. As a result, the health support system according to the embodiment can improve the user's health condition and support the prevention and improvement of lifestyle-related diseases.

[0030] The basic information input unit allows the user to input their daily mood and stress level, and the generation AI can analyze this information to predict changes in their health condition. For example, the basic information input unit allows the user to input their mood and stress level every day, and the generation AI analyzes this data to predict changes in their health condition. For example, on days when they are feeling down, the generation AI may suggest light exercise. Based on the mood and stress level data, the generation AI may also predict changes in the user's health condition and provide appropriate advice. For example, on days when stress is high, the generation AI may suggest relaxation. The generation AI may also analyze the mood and stress level data input by the user to predict changes in their health condition. For example, on days when they are feeling good, the generation AI may suggest increasing the intensity of exercise. This makes it possible to predict changes in their health condition taking into account the user's daily mood and stress level.

[0031] The basic information input unit takes into account the user's past medical history and family health history, allowing the generation AI to more accurately grasp their health condition. The basic information input unit, for example, allows the user to input their past medical history, and the generation AI analyzes it to grasp their health condition. For example, if a user has been diagnosed with high blood pressure in the past, suggestions will be made that focus on blood pressure management. The user also inputs their family's health history, and the generation AI takes this into consideration to grasp their health condition. For example, if a family member has a history of diabetes, suggestions will be made that focus on dietary management. The generation AI also integrates the user's past medical history and family health history to more accurately grasp their health condition. For example, if there is a high risk of heart disease, a heart-friendly exercise menu will be suggested. This allows for a more accurate understanding of the health condition by taking into account the user's past medical history and family health history.

[0032] The basic information input unit can use voice recognition technology to enable the user to input basic information. The basic information input unit, for example, allows the user to input basic information by voice, and the generation AI analyzes the data using voice recognition technology. For example, the user inputs "weight 70 kg, blood pressure 140 / 90" by voice. Voice recognition technology can also be used to enable the user to easily input basic information. For example, voice input can be performed using a smartphone microphone. The user can also input basic information by voice, and the generation AI analyzes the data using voice recognition technology. For example, the user can input "today's meals are bread and coffee for breakfast, and a sandwich for lunch" by voice. This allows the user to easily input basic information.

[0033] The basic information input unit can gamify the input of basic information, allowing the user to input information while having fun. The basic information input unit, for example, gamifies the input of basic information, allowing the user to input information while having fun. For example, a system is introduced whereby points are accumulated each time an input is made. Furthermore, a basic information input system incorporating game elements is developed, allowing the user to input information while having fun. For example, a system is introduced whereby an avatar grows according to the input content. Furthermore, the input of basic information is gamified, allowing the user to input information while having fun. For example, a system is introduced whereby a mini-game is played each time an input is made. This allows the user to input basic information while having fun.

[0034] The suggestion unit allows the generation AI to propose an individually customized meal menu taking into account the user's preferences and allergy information. For example, the suggestion unit allows the user to input preferences and allergy information, and the generation AI proposes an individually customized meal menu taking this into account. For example, it proposes a menu that excludes ingredients that the user is allergic to. The generation AI also proposes an individually customized meal menu based on the user's preferences and allergy information. For example, it proposes a menu that includes many of the user's favorite ingredients. The generation AI also proposes an individually customized meal menu taking into account the user's preferences and allergy information. For example, it proposes a menu that avoids ingredients that the user is allergic to. In this way, it is possible to propose a customized meal menu taking into account the user's preferences and allergy information.

[0035] The suggestion unit can analyze the user's past exercise history and performance data to suggest the optimal exercise intensity and type. For example, the suggestion unit allows the user to input their past exercise history, and the generation AI analyzes it to suggest the optimal exercise intensity and type. For example, it suggests a new menu based on data from past exercises. The generation AI also suggests the optimal exercise intensity and type for the user based on performance data. For example, it analyzes heart rate and calorie consumption data to suggest an exercise menu. The generation AI also analyzes the user's past exercise history and performance data to suggest the optimal exercise intensity and type. For example, it suggests a new menu based on past exercise data. This makes it possible to suggest the optimal exercise menu taking into account the user's past exercise history and performance data.

[0036] The suggestion unit adds a social networking service (SNS) function that allows users to easily share suggested exercise menus and meal menus, thereby utilizing the power of the community. The suggestion unit, for example, adds a function that allows users to share suggested exercise menus and meal menus on SNS, thereby enabling users to utilize the power of the community. For example, sharing menus with friends. Furthermore, the SNS function allows users to share suggested menus and receive feedback from the community. For example, receiving advice from other users. Furthermore, the suggestion unit adds a function that allows users to share suggested exercise menus and meal menus on SNS, thereby enabling users to utilize the power of the community. For example, holding a group challenge. This allows users to easily share suggested menus and utilize the power of the community.

[0037] The suggestion unit can provide specific recipes and exercise videos for carrying out the proposed exercise menu and meal menu, making it easier for the user to carry out. The suggestion unit, for example, provides specific recipes for the proposed meal menu, making it easier for the user to carry out. For example, it provides detailed explanations of cooking procedures and necessary ingredients. It also provides specific exercise videos for the proposed exercise menu, making it easier for the user to carry out. For example, it shows exercise procedures in videos. It also provides specific recipes and exercise videos for carrying out the proposed menu, making it easier for the user to carry out. For example, it provides recipe videos and exercise tutorials. This makes it easier for the user to carry out the proposed menu.

[0038] The support unit has an AI concierge that monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, the support unit has an AI concierge that constantly monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, an alert is issued if blood pressure suddenly rises. The AI ​​concierge also monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, a notification is issued if the heart rate is abnormally high. The AI ​​concierge also monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, an alert is issued if weight suddenly increases. In this way, the user's health condition can be monitored and that a visit to a medical institution is recommended if an abnormality is detected.

[0039] The support unit allows the AI ​​concierge to analyze the user's progress and provide praise and encouraging messages according to the level of achievement. For example, the support unit allows the AI ​​concierge to analyze the user's progress and provide praise and encouraging messages according to the level of achievement. For example, a praise message is displayed when the target weight is reached. The AI ​​concierge also analyzes the user's progress and provides praise and encouraging messages according to the level of achievement. For example, an encouraging message is sent when the user completes all of the exercise menu. The AI ​​concierge also analyzes the user's progress and provides praise and encouraging messages according to the level of achievement. For example, a praise message is displayed when the user sticks to the meal menu. In this way, praise and encouraging messages can be provided according to the user's progress.

[0040] The support unit allows the AI ​​concierge to analyze the user's health condition and suggest appropriate supplements and health foods. For example, the support unit allows the AI ​​concierge to analyze the user's health condition and suggest appropriate supplements. For example, if a vitamin deficiency is detected, vitamin supplements will be suggested. The AI ​​concierge also analyzes the user's health condition and suggest appropriate health foods. For example, if an iron deficiency is detected, foods high in iron will be suggested. The AI ​​concierge also analyzes the user's health condition and suggest appropriate supplements and health foods. For example, if a protein deficiency is detected, protein supplements will be suggested. This makes it possible to suggest appropriate supplements and health foods according to the user's health condition.

[0041] The support unit's AI concierge can analyze the user's health condition and suggest appropriate relaxation and stress relief methods. For example, the support unit's AI concierge can analyze the user's health condition and suggest appropriate relaxation methods. For example, if stress is high, it can suggest meditation or deep breathing. The AI ​​concierge can also analyze the user's health condition and suggest appropriate stress relief methods. For example, if stress is high, it can suggest taking a walk or spending time on a hobby. The AI ​​concierge can also analyze the user's health condition and suggest appropriate relaxation and stress relief methods. For example, if stress is high, it can suggest aromatherapy. This makes it possible to suggest appropriate relaxation and stress relief methods according to the user's health condition.

[0042] The support unit allows the generation AI to propose individually customized benefits according to the points achieved by the user. For example, the support unit allows the generation AI to propose individually customized benefits according to the points achieved by the user. For example, fitness goods may be suggested if the entire exercise menu is completed. The generation AI may also propose individually customized benefits based on the point achievement status. For example, health foods may be suggested if weight is lost. The generation AI may also propose individually customized benefits according to the points achieved by the user. For example, a restaurant discount coupon may be provided if the meal menu is followed. In this way, individually customized benefits may be proposed according to the points achieved by the user.

[0043] The support unit can display the point achievement status in real time to maintain the user's motivation. The support unit, for example, builds a system that displays the point achievement status of the user in real time to maintain motivation. For example, the point progress status is displayed in a graph within the app. The point achievement status is also displayed in real time to maintain the user's motivation. For example, the number of points remaining until the goal is achieved is displayed. The support unit also builds a system that displays the point achievement status of the user in real time to maintain motivation. For example, an animation is displayed when points are achieved. This allows the point achievement status to be displayed in real time to maintain the user's motivation.

[0044] The support unit can stimulate the competitive spirit by adding a ranking function that allows users to compete for points with other users. For example, the support unit can add a ranking function that allows users to compete for points with other users to stimulate the competitive spirit. For example, it can display weekly rankings or monthly rankings. It can also use the ranking function to introduce a system in which users compete for points with other users. For example, it can provide special benefits to top rankers. It can also add a ranking function that allows users to compete for points with other users to stimulate the competitive spirit. For example, it can provide a function to share rankings with friends. This can stimulate the competitive spirit of users and increase motivation.

[0045] The support department can add a function that allows points to be shared with family and friends, and introduce a system whereby users can work together to win rewards. For example, the support department can add a function that allows points to be shared with family and friends, and introduce a system whereby users can work together to win rewards. For example, the whole family can collect points to win rewards. A system can also be introduced whereby users can share points with family and friends, and introduce a system whereby users can work together to win rewards. For example, the whole family can collect points to win rewards. A system can also be introduced whereby users can share points with family and friends, and introduce a system whereby users can work together to win rewards. For example, the whole family can collect points to win rewards. This can increase motivation by allowing users to work together to win rewards with family and friends.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The basic information input unit can use voice recognition technology to assist users in entering their basic information. For example, if a user speaks, "Weight 70 kg, Blood pressure 140 / 90," the generation AI analyzes the voice data and saves it as digital data. Voice recognition technology can also be used to make it easier for users to enter basic information. For example, voice input can be performed using a smartphone microphone. Alternatively, a user can enter basic information by voice, and the generation AI analyzes the data using voice recognition technology. For example, a user can speak, "Today's meals are bread and coffee for breakfast, and a sandwich for lunch." This allows users to easily enter basic information.

[0048] The basic information input unit allows the user to input their daily mood and stress level, and the generation AI can analyze this information to predict changes in their health condition. For example, if the user inputs their mood and stress level every day, the generation AI can analyze this data and predict changes in their health condition. For example, on days when they are feeling down, it can suggest light exercise. The generation AI can also predict changes in the user's health condition based on the mood and stress level data and provide appropriate advice. For example, it can suggest relaxation on days when stress is high. The generation AI can also analyze the mood and stress level data input by the user and predict changes in their health condition. For example, it can suggest increasing the intensity of exercise on days when they are feeling good. This makes it possible to predict changes in their health condition taking into account the user's daily mood and stress level.

[0049] The basic information input unit takes into account the user's past medical history and family health history, allowing the generation AI to more accurately grasp their health condition. For example, the user is asked to input their past medical history, and the generation AI analyzes it to grasp their health condition. For example, if they have been diagnosed with high blood pressure in the past, suggestions will be made that focus on blood pressure management. The generation AI is also asked to input their family's health history, and this will be taken into consideration when grasping their health condition. For example, if a family member has a history of diabetes, suggestions will be made that focus on dietary management. The generation AI also integrates the user's past medical history with their family's health history to more accurately grasp their health condition. For example, if they are at high risk of heart disease, it will suggest a heart-friendly exercise menu. This allows for a more accurate understanding of their health condition by taking into account their past medical history and family's health history.

[0050] The basic information input unit can gamify the input of basic information, allowing the user to input information while having fun. For example, the input of basic information can be gamified, allowing the user to input information while having fun. For example, a system can be introduced whereby points are accumulated each time information is input. Furthermore, a basic information input system incorporating game elements can be developed, allowing the user to input information while having fun. For example, a system can be introduced whereby an avatar grows according to the input content. Furthermore, the input of basic information can be gamified, allowing the user to input information while having fun. For example, a system can be introduced whereby a mini-game can be played each time information is input. This allows the user to input basic information while having fun.

[0051] The suggestion unit allows the generation AI to propose individually customized meal menus taking into account the user's preferences and allergy information. For example, the user can input their preferences and allergy information, and the generation AI can propose individually customized meal menus taking this into account. For example, it can propose a menu that excludes ingredients that the user is allergic to. The generation AI can also propose individually customized meal menus based on the user's preferences and allergy information. For example, it can propose a menu that includes many of the user's favorite ingredients. The generation AI can also propose individually customized meal menus taking into account the user's preferences and allergy information. For example, it can propose a menu that avoids ingredients that the user is allergic to. In this way, it is possible to propose customized meal menus taking into account the user's preferences and allergy information.

[0052] The suggestion unit adds a social networking service (SNS) function that allows users to easily share suggested exercise menus and meal menus, thereby utilizing the power of the community. For example, a function that allows users to share suggested exercise menus and meal menus on SNS can be added, allowing users to utilize the power of the community. For example, sharing menus with friends. Furthermore, the SNS function can be used to allow users to share suggested menus and receive feedback from the community. For example, receiving advice from other users. Furthermore, a function that allows users to share suggested exercise menus and meal menus on SNS can be added, allowing users to utilize the power of the community. For example, holding a group challenge. This allows users to easily share suggested menus and utilize the power of the community.

[0053] The suggestion unit can provide specific recipes and exercise videos for carrying out the proposed exercise menu and meal menu, making it easier for the user to carry out. For example, a specific recipe can be provided for the proposed meal menu, making it easier for the user to carry out. For example, cooking procedures and necessary ingredients can be explained in detail. Also, a specific exercise video can be provided for the proposed exercise menu, making it easier for the user to carry out. For example, exercise procedures can be shown in video. Also, a specific recipe or exercise video can be provided for carrying out the proposed menu, making it easier for the user to carry out. For example, a recipe video or exercise tutorial can be provided. This makes it easier for the user to carry out the proposed menu.

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

[0055] Step 1: The basic information input unit allows the user to input basic information. For example, the user can input information such as height, weight, body fat percentage, blood pressure, dietary details, and alcohol intake. The basic information input unit can also save the input information as digital data. Step 2: The analysis unit analyzes the basic information input by the basic information input unit. For example, the generation AI analyzes the user's health condition and evaluates the risk of lifestyle-related diseases. The generation AI can also predict changes in the user's health condition based on the user's basic information. Step 3: The suggestion unit proposes a weekly exercise menu and meal menu based on the results of the analysis by the analysis unit. For example, the generation AI proposes the optimal exercise menu for the user. The exercise menu may include walking, yoga, strength training, etc. The generation AI also proposes the optimal meal menu for the user. The meal menu may include oatmeal, salad, fish, etc. Step 4: The support department supports improvements based on the menu proposed by the suggestion department. For example, the generation AI monitors the user's progress and provides advice as needed. The generation AI can also set achievement points and offer rewards to maintain the user's motivation.

[0056] (Example 2) The health support system according to an embodiment of the present invention is a system in which a user inputs basic information, and a generation AI analyzes the information, proposes exercise and meal menus, and provides support for improvement. As a result, the health support system can improve the user's health and support the prevention and improvement of lifestyle-related diseases.

[0057] A health support system according to an embodiment includes a basic information input unit, an analysis unit, a suggestion unit, and a support unit. The basic information input unit allows a user to input basic information. For example, the user can input information such as height, weight, body fat percentage, blood pressure, dietary content, and alcohol intake. The basic information input unit can also store the input information as digital data. The analysis unit analyzes the basic information input by the basic information input unit. For example, the generation AI analyzes the user's health status and assesses the risk of lifestyle-related diseases. The generation AI can also predict changes in the user's health status based on the user's basic information. The suggestion unit suggests a weekly exercise menu and meal menu based on the results of the analysis by the analysis unit. For example, the generation AI suggests an optimal exercise menu for the user. The exercise menu may include walking, yoga, strength training, etc. The generation AI also suggests an optimal meal menu for the user. The meal menu may include oatmeal, salad, fish, etc. The support unit supports improvement based on the menu suggested by the suggestion unit. For example, the generation AI monitors the user's progress and provides advice as needed. Furthermore, the generation AI can set achievement points and provide rewards to maintain the user's motivation. As a result, the health support system according to the embodiment can improve the user's health condition and support the prevention and improvement of lifestyle-related diseases.

[0058] The basic information input unit allows the user to input their daily mood and stress level, and the generation AI can analyze this information to predict changes in their health condition. For example, the basic information input unit allows the user to input their mood and stress level every day, and the generation AI analyzes this data to predict changes in their health condition. For example, on days when they are feeling down, the generation AI may suggest light exercise. Based on the mood and stress level data, the generation AI may also predict changes in the user's health condition and provide appropriate advice. For example, on days when stress is high, the generation AI may suggest relaxation. The generation AI may also analyze the mood and stress level data input by the user to predict changes in their health condition. For example, on days when they are feeling good, the generation AI may suggest increasing the intensity of exercise. This makes it possible to predict changes in their health condition taking into account the user's daily mood and stress level.

[0059] The basic information input unit takes into account the user's past medical history and family health history, allowing the generation AI to more accurately grasp their health condition. The basic information input unit, for example, allows the user to input their past medical history, and the generation AI analyzes it to grasp their health condition. For example, if a user has been diagnosed with high blood pressure in the past, suggestions will be made that focus on blood pressure management. The user also inputs their family's health history, and the generation AI takes this into consideration to grasp their health condition. For example, if a family member has a history of diabetes, suggestions will be made that focus on dietary management. The generation AI also integrates the user's past medical history and family health history to more accurately grasp their health condition. For example, if there is a high risk of heart disease, a heart-friendly exercise menu will be suggested. This allows for a more accurate understanding of the health condition by taking into account the user's past medical history and family health history.

[0060] The basic information input unit can use the emotion estimation function to analyze the emotion of the user when inputting information and evaluate the reliability of the input content. For example, when the user inputs basic information, the basic information input unit uses the emotion estimation function to analyze the emotion and evaluate the reliability of the input content. For example, the emotion estimation function analyzes facial expressions and tone of voice when inputting information. The emotion estimation function also analyzes the emotion of the user when inputting information in real time and evaluates the reliability of the input content. For example, if the user is under high stress, the emotion estimation function also analyzes the emotion and evaluates the reliability of the input content. For example, if the user has strong negative emotions, the input content is recommended to be reconfirmed. In this way, the user's emotion can be analyzed and the reliability of the input content can be evaluated.

[0061] The basic information input unit can use voice recognition technology to enable the user to input basic information. The basic information input unit, for example, allows the user to input basic information by voice, and the generation AI analyzes the data using voice recognition technology. For example, the user inputs "weight 70 kg, blood pressure 140 / 90" by voice. Voice recognition technology can also be used to enable the user to easily input basic information. For example, voice input can be performed using a smartphone microphone. The user can also input basic information by voice, and the generation AI analyzes the data using voice recognition technology. For example, the user can input "today's meals are bread and coffee for breakfast, and a sandwich for lunch" by voice. This allows the user to easily input basic information.

[0062] The basic information input unit can gamify the input of basic information, allowing the user to input information while having fun. The basic information input unit, for example, gamifies the input of basic information, allowing the user to input information while having fun. For example, a system is introduced whereby points are accumulated each time an input is made. Furthermore, a basic information input system incorporating game elements is developed, allowing the user to input information while having fun. For example, a system is introduced whereby an avatar grows according to the input content. Furthermore, the input of basic information is gamified, allowing the user to input information while having fun. For example, a system is introduced whereby a mini-game is played each time an input is made. This allows the user to input basic information while having fun.

[0063] The basic information input unit uses the emotion estimation function to provide real-time feedback on the emotions of the user when entering information, thereby eliciting positive emotions. The basic information input unit, for example, uses the emotion estimation function to analyze the emotions of the user when entering basic information in real time and provide feedback that elicits positive emotions. For example, it displays an encouraging message. Furthermore, it uses the emotion estimation function to analyze the emotions of the user when entering basic information and provides feedback that elicits positive emotions. For example, it displays a compliment according to the input content. Furthermore, it uses the emotion estimation function to analyze the emotions of the user when entering basic information in real time and provides feedback that elicits positive emotions. For example, it plays music that elicits positive emotions. This makes it possible to enter basic information while eliciting positive emotions from the user.

[0064] The suggestion unit allows the generation AI to propose an individually customized meal menu taking into account the user's preferences and allergy information. For example, the suggestion unit allows the user to input preferences and allergy information, and the generation AI proposes an individually customized meal menu taking this into account. For example, it proposes a menu that excludes ingredients that the user is allergic to. The generation AI also proposes an individually customized meal menu based on the user's preferences and allergy information. For example, it proposes a menu that includes many of the user's favorite ingredients. The generation AI also proposes an individually customized meal menu taking into account the user's preferences and allergy information. For example, it proposes a menu that avoids ingredients that the user is allergic to. In this way, it is possible to propose a customized meal menu taking into account the user's preferences and allergy information.

[0065] The suggestion unit can analyze the user's past exercise history and performance data to suggest the optimal exercise intensity and type. For example, the suggestion unit allows the user to input their past exercise history, and the generation AI analyzes it to suggest the optimal exercise intensity and type. For example, it suggests a new menu based on data from past exercises. The generation AI also suggests the optimal exercise intensity and type for the user based on performance data. For example, it analyzes heart rate and calorie consumption data to suggest an exercise menu. The generation AI also analyzes the user's past exercise history and performance data to suggest the optimal exercise intensity and type. For example, it suggests a new menu based on past exercise data. This makes it possible to suggest the optimal exercise menu taking into account the user's past exercise history and performance data.

[0066] The suggestion unit uses the emotion estimation function to suggest an exercise menu according to the user's emotional state, thereby maintaining motivation. The suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional state and suggest an exercise menu according to the emotion. For example, on days when the user is feeling down, it suggests light exercise. The suggestion unit also analyzes the user's emotional state in real time and suggests an exercise menu according to the emotion. For example, on days when the user is feeling high, it suggests relaxation. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest an exercise menu according to the emotion. For example, on days when the user is feeling good, it suggests increasing the exercise intensity. In this way, it is possible to suggest an exercise menu according to the user's emotional state and maintain motivation.

[0067] The suggestion unit adds a social networking service (SNS) function that allows users to easily share suggested exercise menus and meal menus, thereby utilizing the power of the community. The suggestion unit, for example, adds a function that allows users to share suggested exercise menus and meal menus on SNS, thereby enabling users to utilize the power of the community. For example, sharing menus with friends. Furthermore, the SNS function allows users to share suggested menus and receive feedback from the community. For example, receiving advice from other users. Furthermore, the suggestion unit adds a function that allows users to share suggested exercise menus and meal menus on SNS, thereby enabling users to utilize the power of the community. For example, holding a group challenge. This allows users to easily share suggested menus and utilize the power of the community.

[0068] The suggestion unit can provide specific recipes and exercise videos for carrying out the proposed exercise menu and meal menu, making it easier for the user to carry out. The suggestion unit, for example, provides specific recipes for the proposed meal menu, making it easier for the user to carry out. For example, it provides detailed explanations of cooking procedures and necessary ingredients. It also provides specific exercise videos for the proposed exercise menu, making it easier for the user to carry out. For example, it shows exercise procedures in videos. It also provides specific recipes and exercise videos for carrying out the proposed menu, making it easier for the user to carry out. For example, it provides recipe videos and exercise tutorials. This makes it easier for the user to carry out the proposed menu.

[0069] The suggestion unit can use the emotion estimation function to analyze how the user feels about the proposed menu and adjust the menu based on the feedback. For example, the suggestion unit can use the emotion estimation function to analyze how the user feels about the proposed menu and adjust the menu based on the feedback. For example, it can change a menu that has a lot of negative emotions. It can also analyze the user's emotional response in real time and adjust the menu based on feedback about the proposed menu. For example, it can prioritize menus that have a lot of positive emotions. It can also use the emotion estimation function to analyze how the user feels about the proposed menu and adjust the menu based on the feedback. For example, it can improve a menu that has a low emotion score. This makes it possible to adjust the menu according to the user's emotions.

[0070] The support unit has an AI concierge that monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, the support unit has an AI concierge that constantly monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, an alert is issued if blood pressure suddenly rises. The AI ​​concierge also monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, a notification is issued if the heart rate is abnormally high. The AI ​​concierge also monitors the user's health condition and recommends that the user visit a medical institution if an abnormality is detected. For example, an alert is issued if weight suddenly increases. In this way, the user's health condition can be monitored and that a visit to a medical institution is recommended if an abnormality is detected.

[0071] The support unit allows the AI ​​concierge to analyze the user's progress and provide praise and encouraging messages according to the level of achievement. For example, the support unit allows the AI ​​concierge to analyze the user's progress and provide praise and encouraging messages according to the level of achievement. For example, a praise message is displayed when the target weight is reached. The AI ​​concierge also analyzes the user's progress and provides praise and encouraging messages according to the level of achievement. For example, an encouraging message is sent when the user completes all of the exercise menu. The AI ​​concierge also analyzes the user's progress and provides praise and encouraging messages according to the level of achievement. For example, a praise message is displayed when the user sticks to the meal menu. In this way, praise and encouraging messages can be provided according to the user's progress.

[0072] The support unit uses the emotion estimation function to provide a support message that corresponds to the user's emotional state, thereby maintaining motivation. The support unit, for example, uses the emotion estimation function to analyze the user's emotional state and provide a support message that corresponds to the emotion. For example, on days when the user is feeling down, it sends an encouraging message. The support unit also analyzes the user's emotional state in real time and provides a support message that corresponds to the emotion. For example, on days when the user is feeling high in stress, it suggests relaxation. The support unit also uses the emotion estimation function to analyze the user's emotional state and provide a support message that corresponds to the emotion. For example, on days when the user is feeling good, it displays a compliment. In this way, it is possible to provide a support message that corresponds to the user's emotional state and maintain motivation.

[0073] The support unit allows the AI ​​concierge to analyze the user's health condition and suggest appropriate supplements and health foods. For example, the support unit allows the AI ​​concierge to analyze the user's health condition and suggest appropriate supplements. For example, if a vitamin deficiency is detected, vitamin supplements will be suggested. The AI ​​concierge also analyzes the user's health condition and suggest appropriate health foods. For example, if an iron deficiency is detected, foods high in iron will be suggested. The AI ​​concierge also analyzes the user's health condition and suggest appropriate supplements and health foods. For example, if a protein deficiency is detected, protein supplements will be suggested. This makes it possible to suggest appropriate supplements and health foods according to the user's health condition.

[0074] The support unit's AI concierge can analyze the user's health condition and suggest appropriate relaxation and stress relief methods. For example, the support unit's AI concierge can analyze the user's health condition and suggest appropriate relaxation methods. For example, if stress is high, it can suggest meditation or deep breathing. The AI ​​concierge can also analyze the user's health condition and suggest appropriate stress relief methods. For example, if stress is high, it can suggest taking a walk or spending time on a hobby. The AI ​​concierge can also analyze the user's health condition and suggest appropriate relaxation and stress relief methods. For example, if stress is high, it can suggest aromatherapy. This makes it possible to suggest appropriate relaxation and stress relief methods according to the user's health condition.

[0075] The support unit can use the emotion estimation function to suggest relaxation methods and stress relief methods according to the user's emotional state. For example, the support unit uses the emotion estimation function to analyze the user's emotional state and suggest relaxation methods according to the emotion. For example, on days when the user is feeling down, it may suggest meditation. The support unit can also analyze the user's emotional state in real time and suggest stress relief methods according to the emotion. For example, on days when the user is feeling high, it may suggest taking a walk. The support unit can also use the emotion estimation function to analyze the user's emotional state and suggest relaxation methods and stress relief methods according to the emotion. For example, on days when the user is feeling good, it may suggest time for a hobby. In this way, it can suggest appropriate relaxation methods and stress relief methods according to the user's emotional state.

[0076] The support unit allows the generation AI to propose individually customized benefits according to the points achieved by the user. For example, the support unit allows the generation AI to propose individually customized benefits according to the points achieved by the user. For example, fitness goods may be suggested if the entire exercise menu is completed. The generation AI may also propose individually customized benefits based on the point achievement status. For example, health foods may be suggested if weight is lost. The generation AI may also propose individually customized benefits according to the points achieved by the user. For example, a restaurant discount coupon may be provided if the meal menu is followed. In this way, individually customized benefits may be proposed according to the points achieved by the user.

[0077] The support unit can display the point achievement status in real time to maintain the user's motivation. The support unit, for example, builds a system that displays the point achievement status of the user in real time to maintain motivation. For example, the point progress status is displayed in a graph within the app. The point achievement status is also displayed in real time to maintain the user's motivation. For example, the number of points remaining until the goal is achieved is displayed. The support unit also builds a system that displays the point achievement status of the user in real time to maintain motivation. For example, an animation is displayed when points are achieved. This allows the point achievement status to be displayed in real time to maintain the user's motivation.

[0078] The support unit can stimulate the competitive spirit by adding a ranking function that allows users to compete for points with other users. For example, the support unit can add a ranking function that allows users to compete for points with other users to stimulate the competitive spirit. For example, it can display weekly rankings or monthly rankings. It can also use the ranking function to introduce a system in which users compete for points with other users. For example, it can provide special benefits to top rankers. It can also add a ranking function that allows users to compete for points with other users to stimulate the competitive spirit. For example, it can provide a function to share rankings with friends. This can stimulate the competitive spirit of users and increase motivation.

[0079] The support department can add a function that allows points to be shared with family and friends, and introduce a system whereby users can work together to win rewards. For example, the support department can add a function that allows points to be shared with family and friends, and introduce a system whereby users can work together to win rewards. For example, the whole family can collect points to win rewards. A system can also be introduced whereby users can share points with family and friends, and introduce a system whereby users can work together to win rewards. For example, the whole family can collect points to win rewards. A system can also be introduced whereby users can share points with family and friends, and introduce a system whereby users can work together to win rewards. For example, the whole family can collect points to win rewards. This can increase motivation by allowing users to work together to win rewards with family and friends.

[0080] The support unit can use the emotion estimation function to share the emotions the user feels when receiving a reward with family and friends, thereby sharing joy. The support unit, for example, uses the emotion estimation function to share the emotions the user feels when receiving a reward with family and friends, thereby sharing joy. For example, the support unit shares an emotion score when a reward is acquired. The support unit also analyzes the user's emotional state in real time and shares the emotions the user feels when receiving a reward with family and friends. For example, it sends a joyful message when a reward is acquired. The support unit also uses the emotion estimation function to share the emotions the user feels when receiving a reward with family and friends, thereby sharing joy. For example, it displays an emotion score when a reward is acquired. This allows the user to share the joy the user feels when receiving a reward with family and friends.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The basic information input unit can use voice recognition technology to assist users in entering their basic information. For example, if a user speaks, "Weight 70 kg, Blood pressure 140 / 90," the generation AI analyzes the voice data and saves it as digital data. Voice recognition technology can also be used to make it easier for users to enter basic information. For example, voice input can be performed using a smartphone microphone. Alternatively, a user can enter basic information by voice, and the generation AI analyzes the data using voice recognition technology. For example, a user can speak, "Today's meals are bread and coffee for breakfast, and a sandwich for lunch." This allows users to easily enter basic information.

[0083] The basic information input unit allows the user to input their daily mood and stress level, and the generation AI can analyze this information to predict changes in their health condition. For example, if the user inputs their mood and stress level every day, the generation AI can analyze this data and predict changes in their health condition. For example, on days when they are feeling down, it can suggest light exercise. The generation AI can also predict changes in the user's health condition based on the mood and stress level data and provide appropriate advice. For example, it can suggest relaxation on days when stress is high. The generation AI can also analyze the mood and stress level data input by the user and predict changes in their health condition. For example, it can suggest increasing the intensity of exercise on days when they are feeling good. This makes it possible to predict changes in their health condition taking into account the user's daily mood and stress level.

[0084] The basic information input unit takes into account the user's past medical history and family health history, allowing the generation AI to more accurately grasp their health condition. For example, the user is asked to input their past medical history, and the generation AI analyzes it to grasp their health condition. For example, if they have been diagnosed with high blood pressure in the past, suggestions will be made that focus on blood pressure management. The generation AI is also asked to input their family's health history, and this will be taken into consideration when grasping their health condition. For example, if a family member has a history of diabetes, suggestions will be made that focus on dietary management. The generation AI also integrates the user's past medical history with their family's health history to more accurately grasp their health condition. For example, if they are at high risk of heart disease, it will suggest a heart-friendly exercise menu. This allows for a more accurate understanding of their health condition by taking into account their past medical history and family's health history.

[0085] The basic information input unit can use the emotion estimation function to analyze the emotion of the user when inputting information and evaluate the reliability of the input content. For example, when the user inputs basic information, the emotion estimation function is used to analyze the emotion and evaluate the reliability of the input content. For example, facial expressions and tone of voice when inputting are analyzed. The emotion estimation function is also used to analyze the emotion of the user when inputting information in real time and evaluate the reliability of the input content. For example, if the user is under high stress, the input content is prompted to be reconfirmed. The emotion estimation function is also used to analyze the emotion of the user when inputting basic information and evaluate the reliability of the input content. For example, if the user has strong negative emotions, the input content is prompted to be reconfirmed. In this way, the user's emotion can be analyzed and the reliability of the input content can be evaluated.

[0086] The basic information input unit can gamify the input of basic information, allowing the user to input information while having fun. For example, the input of basic information can be gamified, allowing the user to input information while having fun. For example, a system can be introduced whereby points are accumulated each time information is input. Furthermore, a basic information input system incorporating game elements can be developed, allowing the user to input information while having fun. For example, a system can be introduced whereby an avatar grows according to the input content. Furthermore, the input of basic information can be gamified, allowing the user to input information while having fun. For example, a system can be introduced whereby a mini-game can be played each time information is input. This allows the user to input basic information while having fun.

[0087] The suggestion unit allows the generation AI to propose individually customized meal menus taking into account the user's preferences and allergy information. For example, the user can input their preferences and allergy information, and the generation AI can propose individually customized meal menus taking this into account. For example, it can propose a menu that excludes ingredients that the user is allergic to. The generation AI can also propose individually customized meal menus based on the user's preferences and allergy information. For example, it can propose a menu that includes many of the user's favorite ingredients. The generation AI can also propose individually customized meal menus taking into account the user's preferences and allergy information. For example, it can propose a menu that avoids ingredients that the user is allergic to. In this way, it is possible to propose customized meal menus taking into account the user's preferences and allergy information.

[0088] The suggestion unit uses the emotion estimation function to suggest an exercise menu that corresponds to the user's emotional state, thereby maintaining motivation. For example, the emotion estimation function is used to analyze the user's emotional state and suggest an exercise menu that corresponds to the emotion. For example, on days when the user is feeling down, light exercise is suggested. The suggestion unit also analyzes the user's emotional state in real time and suggests an exercise menu that corresponds to the emotion. For example, on days when the user is feeling high, relaxation is suggested. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest an exercise menu that corresponds to the emotion. For example, on days when the user is feeling good, it suggests increasing the exercise intensity. In this way, an exercise menu that corresponds to the user's emotional state is suggested, thereby maintaining motivation.

[0089] The suggestion unit adds a social networking service (SNS) function that allows users to easily share suggested exercise menus and meal menus, thereby utilizing the power of the community. For example, a function that allows users to share suggested exercise menus and meal menus on SNS can be added, allowing users to utilize the power of the community. For example, sharing menus with friends. Furthermore, the SNS function can be used to allow users to share suggested menus and receive feedback from the community. For example, receiving advice from other users. Furthermore, a function that allows users to share suggested exercise menus and meal menus on SNS can be added, allowing users to utilize the power of the community. For example, holding a group challenge. This allows users to easily share suggested menus and utilize the power of the community.

[0090] The suggestion unit can provide specific recipes and exercise videos for carrying out the proposed exercise menu and meal menu, making it easier for the user to carry out. For example, a specific recipe can be provided for the proposed meal menu, making it easier for the user to carry out. For example, cooking procedures and necessary ingredients can be explained in detail. Also, a specific exercise video can be provided for the proposed exercise menu, making it easier for the user to carry out. For example, exercise procedures can be shown in video. Also, a specific recipe or exercise video can be provided for carrying out the proposed menu, making it easier for the user to carry out. For example, a recipe video or exercise tutorial can be provided. This makes it easier for the user to carry out the proposed menu.

[0091] The support unit can use the emotion estimation function to provide support messages that correspond to the user's emotional state, thereby maintaining motivation. For example, the emotion estimation function can be used to analyze the user's emotional state and provide support messages that correspond to the emotion. For example, on days when the user is feeling down, an encouraging message can be sent. The support unit can also analyze the user's emotional state in real time and provide support messages that correspond to the emotion. For example, on days when the user is feeling high in stress, it can suggest relaxation techniques. The support unit can also use the emotion estimation function to analyze the user's emotional state and provide support messages that correspond to the emotion. For example, on days when the user is feeling good, it can display words of praise. In this way, support messages that correspond to the user's emotional state can be provided, thereby maintaining motivation.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The basic information input unit allows the user to input basic information. For example, the user can input information such as height, weight, body fat percentage, blood pressure, dietary details, and alcohol intake. The basic information input unit can also save the input information as digital data. Step 2: The analysis unit analyzes the basic information input by the basic information input unit. For example, the generation AI analyzes the user's health condition and evaluates the risk of lifestyle-related diseases. The generation AI can also predict changes in the user's health condition based on the user's basic information. Step 3: The suggestion unit proposes a weekly exercise menu and meal menu based on the results of the analysis by the analysis unit. For example, the generation AI proposes the optimal exercise menu for the user. The exercise menu may include walking, yoga, strength training, etc. The generation AI also proposes the optimal meal menu for the user. The meal menu may include oatmeal, salad, fish, etc. Step 4: The support department supports improvements based on the menu proposed by the suggestion department. For example, the generation AI monitors the user's progress and provides advice as needed. The generation AI can also set achievement points and offer rewards to maintain the user's motivation.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 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 basic information input unit for inputting basic information; an analysis unit that analyzes the basic information input by the basic information input unit; a suggestion unit that suggests a weekly exercise menu and a meal menu based on the results of the analysis by the analysis unit; a support unit that provides support up to improvement based on the menu proposed by the proposal unit. A system characterized by:

2. The basic information input unit Users input their daily mood and stress levels, and the generative AI analyzes this information to predict fluctuations in their health.

2. The system of claim 1.

3. The basic information input unit Taking into account the user's past medical history and the health history of their family, the generative AI can grasp their health condition more accurately.

2. The system of claim 1.

4. The basic information input unit Analyze user sentiment when typing and evaluate the reliability of the input 2. The system of claim 1.

5. The basic information input unit Use voice recognition technology to allow users to enter basic information 2. The system of claim 1.

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