System

Abstract

To provide a system capable of providing advice and proposals on appropriate diet and exercise based on health data of a user.SOLUTION: A system in an embodiment includes a first reception unit, a guidance unit, a second reception unit, and a proposal unit. The first reception unit receives one or more pieces of information from the health check result and the health check data of a user. The guidance unit provides dietary or exercise guidance to the user based on the information received by the first reception unit. The second reception unit receives the registered exercise amount or dietary details of the user. The proposal unit proposes to the user based on the information received by the second reception 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide dietary and exercise advice based on a user's health data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide and suggest appropriate dietary and exercise habits based on the user's health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a first reception unit, an instruction unit, a second reception unit, and a suggestion unit. The first reception unit receives one or more pieces of information from the results of a medical checkup of the user and the health checkup data of the user. The instruction unit provides the user with instruction regarding diet or exercise based on the information received by the first reception unit. The second reception unit receives registration of the amount of exercise or dietary content of the user. The suggestion unit suggests dietary content to the user based on the information received by the second reception unit. Effect of the Invention

[0007] The system according to the embodiment can provide and suggest appropriate dietary and exercise habits based on the user's health data. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management support system according to an embodiment of the present invention is a system that receives the results of a medical checkup and medical examination data of a user, and provides guidance on diet and exercise based on the results. In this health management support system, a first reception unit receives the results of a medical checkup and medical examination data of the user. Next, based on the information received by the first reception unit, an instruction unit provides guidance on diet and exercise to the user. Furthermore, a second reception unit receives registration of the user's amount of exercise and dietary content. For example, the number of steps the user takes during commuting and photos of what he or she eats can be registered every hour. Based on the information received by the second reception unit, a suggestion unit suggests dietary content to the user. For example, it is possible to suggest the next day's dietary content and approximate dietary content for each week. This system allows the user to understand his or her health condition and to take appropriate diet and exercise. For example, if a user wants to lose weight, the system provides guidance on appropriate diet and exercise based on the user's current weight and blood data. In addition, by registering photos of what the user has eaten, the system analyzes the information and suggests the next day's dietary content. This allows the user to continue eating a healthy diet, and is expected to improve weight and blood data. This enables the health management support system to efficiently manage the user's health condition and provide appropriate dietary and exercise advice.

[0029] The health management support system according to the embodiment includes a first reception unit, an instruction unit, a second reception unit, and a suggestion unit. The first reception unit receives one or more pieces of information among the results of a medical checkup and medical examination data of the user. For example, the first reception unit can receive data such as the user's blood test results and electrocardiogram results. The first reception unit can also receive medical examination data such as the user's height, weight, and blood pressure. The instruction unit provides the user with instruction regarding diet and exercise based on the information received by the first reception unit. For example, the instruction unit can provide instruction regarding the calorie restriction of the user's diet and the type and frequency of exercise. The instruction unit can also provide appropriate diet and exercise advice according to the user's health condition. The second reception unit receives registration of the user's exercise amount and dietary content. For example, the second reception unit can register the number of steps the user walked during commuting and photos of what the user ate every hour. The second reception unit can also receive information such as the user's exercise time and intensity, and the calories and nutrients of the diet. The suggestion unit suggests meal contents to the user based on the information accepted by the second acceptance unit. For example, the suggestion unit can suggest meal contents for the next day and approximate meal contents for each week. The suggestion unit can also suggest an appropriate meal plan according to the user's health condition and goals. As a result, the health management support system according to the embodiment can efficiently manage the user's health condition and provide appropriate diet and exercise guidance. For example, the suggestion unit can receive the user's meal contents as input and suggest the next day's meal contents using the generation AI. The generation AI generates an appropriate meal plan based on the user's health condition and goals. For example, the generation AI can analyze the user's meal contents and suggest a meal plan based on nutritional balance and calorie restriction. As a result, the user can continue to eat a healthy diet, and improvement in weight and blood data can be expected.

[0030] The first reception unit receives one or more pieces of information among the results of the medical checkup and the health checkup data of the user. For example, the first reception unit can receive data such as the user's blood test results and electrocardiogram results. Specifically, the blood test results include blood glucose levels, cholesterol levels, and liver function indicators, and the electrocardiogram results include heart rate and the presence or absence of arrhythmia. The first reception unit can also receive health checkup data such as the user's height, weight, and blood pressure. These data are collected by the user inputting the results of tests taken at a medical institution in digital format or by acquiring data directly from the medical institution. In addition, the first reception unit can also receive the user's past health checkup data and medical history, which makes it possible to grasp long-term changes in the user's health condition. These data are securely stored in a cloud server and used for subsequent analysis and guidance. The first reception unit provides an interface that allows the user to easily input data and also has a check function to ensure the accuracy and completeness of the data. For example, if the input data contains abnormal values, an alert is displayed to prompt the user to recheck. In addition, when data input is completed, a confirmation message is sent to the user to notify the user that the data has been correctly accepted. This enables the first accepting unit to efficiently and accurately collect the user's health data and improve the reliability of the entire system.

[0031] The instruction unit provides the user with instruction on diet and exercise based on the information received by the first reception unit. For example, the instruction unit can provide instruction on calorie restrictions and the type and frequency of exercise for the user. Specifically, the instruction unit analyzes data such as the user's blood test results, electrocardiogram results, height, weight, and blood pressure, and proposes optimal meal plans and exercise programs for each user. For example, a carbohydrate-restricted diet is proposed for a user with high blood sugar levels, and mild aerobic exercise is recommended for a user with an unstable heart rate. The instruction unit can also provide appropriate diet and exercise advice according to the user's health condition. For example, a low-calorie, high-protein diet is recommended for a user who tends to be obese, and specific guidance is provided on the frequency and method of muscle training for a user who wants to increase muscle strength. The instruction unit uses AI to analyze the user's data and generate optimal instruction content. The AI ​​can provide optimal instruction content for each user based on past data and success stories of similar users. Furthermore, the instruction unit collects user feedback and evaluates the effectiveness of the instruction content. For example, the system monitors how weight and blood pressure change as a result of the user following the advice and modifies the advice content as necessary. This allows the advice unit to continuously improve the user's health condition and support optimal health management.

[0032] The second reception unit accepts the registration of the user's amount of exercise and dietary content. For example, the second reception unit can register the number of steps the user took during commuting and photos of what they ate every hour. Specifically, the user can automatically record data such as the number of steps, exercise time, and exercise intensity using a smartphone or wearable device. In addition, the calorie and nutrient content of the meal can be automatically analyzed by taking a photo of what the user ate and uploading it through the app. The second reception unit stores these data in a cloud server and uses them for subsequent analysis and proposals. In addition, the second reception unit also provides an interface for the user to manually input data, allowing the user to specifically record the details of the meal and the content of the exercise. For example, the user can input the calorie and nutrient content of the meal, the type, time, and intensity of the exercise, etc. in detail. The second reception unit can centrally manage these data and link with other systems and departments as necessary. For example, the exercise data and dietary data of the user can be made accessible to the guidance department and proposal department, and used as basic data for providing optimal guidance and proposals to each individual user. This enables the second reception unit to efficiently and accurately collect information on the amount of exercise and dietary details of the user, thereby improving the performance of the entire system.

[0033] The suggestion unit suggests meal contents to the user based on the information received by the second reception unit. For example, the suggestion unit can suggest the next day's meal contents and the approximate meal contents for each week. Specifically, the suggestion unit analyzes the user's exercise amount, meal contents, and health condition, and generates an optimal meal plan based on nutritional balance and calorie restriction. The suggestion unit analyzes these data using the generation AI and suggests a meal plan according to the user's health condition and goals. For example, the generation AI can analyze the user's meal contents and suggest a meal plan based on nutritional balance and calorie restriction. Specifically, the generation AI suggests the next day's meal contents based on the user's past meal data and exercise data, and provides a menu that takes nutritional balance into consideration. In addition, the generation AI can customize the meal plan according to the user's health condition and goals. For example, it suggests a low-calorie, high-protein meal to a user who is aiming to lose weight, and suggests a meal containing appropriate nutrients to a user who wants to increase muscle strength. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the suggestion unit monitors how the user's weight and blood data change as a result of following the proposed meal plan, and modifies the meal plan as necessary. This allows the suggestion unit to provide the user with an optimal meal plan and support the improvement of the user's health condition.

[0034] The suggestion unit can suggest at least one of the next day's meal contents or the weekly meal contents. The suggestion unit can suggest, for example, the menus for the next day's breakfast, lunch, and dinner. For example, the suggestion unit can suggest balanced meal contents based on the user's health condition and goals. The suggestion unit can also suggest a weekly meal plan and menu. For example, the suggestion unit can suggest a weekly meal plan based on the user's health condition and goals. This allows the user to continue a healthy diet from a long-term perspective. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can receive the user's meal contents as input, input a prompt to the generation AI, and the generation AI can generate a meal plan. The generation AI generates an appropriate meal plan based on the user's health condition and goals. For example, the generation AI can analyze the user's meal contents and suggest a meal plan based on nutritional balance and calorie restriction. This allows the user to continue a healthy diet, and improvement in weight and blood data can be expected.

[0035] The first reception unit can analyze the user's past health check data and select the optimal reception method. For example, the first reception unit can preferentially suggest a reception method that the user has frequently used in the past. The first reception unit can also select the most efficient reception method from the user's past health check data. Furthermore, the first reception unit can also suggest the optimal reception method for a specific time period based on the user's past health check data. This allows efficient data reception by selecting the optimal reception method based on the user's past data. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the first reception unit can input the user's past health check data to the generation AI and cause the generation AI to select the optimal reception method.

[0036] The first reception unit can perform filtering based on the user's current health condition and lifestyle habits when receiving the health checkup data. The first reception unit can preferentially receive important data based on the user's current health condition, for example. The first reception unit can also filter highly relevant data based on the user's lifestyle habits. Furthermore, the first reception unit can adjust the type of data to be received according to the user's health condition and lifestyle habits. This makes it possible to filter data according to the user's health condition and lifestyle habits. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first reception unit can input data on the user's health condition and lifestyle habits to the generation AI and cause the generation AI to perform filtering.

[0037] When accepting the medical examination data, the first reception unit can preferentially accept highly relevant data in consideration of the geographical location information of the user. For example, when the user lives in a specific area, the first reception unit can preferentially accept data related to the area. In addition, when the user is traveling, the first reception unit can also preferentially accept data related to the current location. Furthermore, the first reception unit can also accept the most relevant data based on the geographical location information of the user. This makes it possible to preferentially accept data that is highly relevant based on the geographical location information of the user. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the first reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to select highly relevant data.

[0038] The first reception unit can analyze the social media activity of the user and accept related data when accepting the health checkup data. The first reception unit can accept related data based on, for example, health information shared by the user on social media. The first reception unit can also analyze health-related interests from the user's social media activity and accept related data. Furthermore, the first reception unit can also accept the most relevant data based on the user's social media activity. This makes it possible to accept related data based on the user's social media activity. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related data.

[0039] The instruction unit can adjust the level of detail of the instruction based on the user's health goal during instruction. For example, if the user wants to lose weight, the instruction unit can provide detailed dietary instruction. Also, if the user wants to increase muscle strength, the instruction unit can provide detailed exercise instruction. Furthermore, if the user wants to maintain health, the instruction unit can provide balanced instruction. This enables detailed instruction according to the user's health goal. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's health goal data into the generation AI and cause the generation AI to adjust the level of detail of the instruction.

[0040] The instruction unit can apply different instruction algorithms according to the user's lifestyle during instruction. For example, if the user is a night owl, the instruction unit can provide instruction suitable for the night. Also, if the user is a morning person, the instruction unit can provide instruction suitable for the morning. Furthermore, if the user has an irregular lifestyle, the instruction unit can provide flexible instruction. This enables flexible instruction according to the user's lifestyle. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's lifestyle data into the generation AI and cause the generation AI to apply the instruction algorithm.

[0041] The guidance unit can determine the priority of guidance based on the time of submission of the user's health data when providing guidance. The guidance unit can provide the latest guidance based on, for example, the health data recently submitted by the user. The guidance unit can also determine the priority based on the health data submitted by the user in the past. Furthermore, the guidance unit can adjust the priority of guidance according to the time of submission of the user's health data. This makes it possible to provide guidance with a priority according to the time of submission of the user's health data. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the time of submission of the user's health data to the generation AI and cause the generation AI to determine the priority of guidance.

[0042] The instruction unit can adjust the order of instruction based on the relevance of the user during instruction. The instruction unit can prioritize the most relevant instruction based on, for example, the user's health goal. The instruction unit can also prioritize the most relevant instruction based on the user's lifestyle habits. Furthermore, the instruction unit can also prioritize the most relevant instruction based on the user's health condition. This makes it possible to adjust the order of instruction based on the user's relevance. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's relevance data to the generation AI and cause the generation AI to adjust the order of instruction.

[0043] The second reception unit can analyze the user's past exercise amount and meal contents and select the optimal registration method. For example, the second reception unit can preferentially suggest a registration method that the user has frequently used in the past. The second reception unit can also select the most efficient registration method from the user's past exercise amount and meal contents. Furthermore, the second reception unit can also suggest the optimal registration method for a specific time period based on the user's past exercise amount and meal contents. This makes it possible to select the optimal registration method based on the user's past data. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the second reception unit can input data on the user's past exercise amount and meal contents to the generation AI and cause the generation AI to select the optimal registration method.

[0044] The second reception unit can perform filtering based on the user's current living situation and field of interest when registering the amount of exercise and meal contents. The second reception unit can, for example, preferentially register important data based on the user's current living situation. The second reception unit can also filter highly relevant data based on the user's field of interest. Furthermore, the second reception unit can adjust the type of data to be registered according to the user's living situation and field of interest. This makes it possible to filter data according to the user's living situation and field of interest. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the second reception unit can input data on the user's living situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0045] The second reception unit can preferentially register highly relevant data in consideration of the user's geographical location information when registering the amount of exercise or the contents of meals. For example, if the user lives in a specific area, the second reception unit can preferentially register data related to that area. In addition, if the user is traveling, the second reception unit can also preferentially register data related to the current location. Furthermore, the second reception unit can also register the most relevant data based on the user's geographical location information. This makes it possible to preferentially register data based on the user's geographical location information. A part or all of the above-mentioned processing in the second reception unit may be performed, for example, using AI or may be performed without using AI. For example, the second reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0046] The second reception unit can analyze the user's social media activity and register related data when registering the amount of exercise and dietary details. The second reception unit can register related data based on, for example, health information shared by the user on social media. The second reception unit can also analyze the user's health interests from the user's social media activity and register related data. Furthermore, the second reception unit can also register the most relevant data based on the user's social media activity. This makes it possible to register related data based on the user's social media activity. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the second reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related data.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the user's health goal when making a suggestion. For example, if the user wants to lose weight, the suggestion unit can suggest a calorie-restricted meal. Also, if the user wants to increase muscle strength, the suggestion unit can suggest a protein-rich meal. Furthermore, if the user wants to maintain health, the suggestion unit can suggest a balanced meal. This enables detailed suggestions according to the user's health goal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's health goal data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0048] The suggestion unit can apply different suggestion algorithms according to the user's lifestyle at the time of suggestion. For example, if the user is a night owl, the suggestion unit can suggest meal contents suitable for the night. Also, if the user is a morning person, the suggestion unit can suggest meal contents suitable for the morning. Furthermore, if the user has an irregular lifestyle, the suggestion unit can suggest flexible meal contents. This enables flexible suggestions according to the user's lifestyle. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0049] At the time of making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the user's health data. The suggestion unit can make the latest proposal based on, for example, the health data recently submitted by the user. The suggestion unit can also determine the priority based on the health data previously submitted by the user. Furthermore, the suggestion unit can adjust the priority of the proposal according to the time of submission of the user's health data. This makes it possible to propose a priority according to the time of submission of the user's health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the time of submission of the user's health data to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0050] The suggestion unit can adjust the order of the suggestions based on the relevance of the user at the time of suggestion. The suggestion unit can prioritize the most relevant suggestions based on, for example, the health goals of the user. The suggestion unit can also prioritize the highly relevant suggestions based on the lifestyle habits of the user. Furthermore, the suggestion unit can also prioritize the highly relevant suggestions based on the health condition of the user. This makes it possible to adjust the order of the suggestions based on the relevance of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the relevance data of the user to the generation AI and cause the generation AI to adjust the order of the suggestions.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The health management support system may further include a moisture acquisition unit that monitors the amount of moisture intake of the user. The moisture acquisition unit records the amount of moisture intake by the user per day, and the suggestion unit can suggest appropriate moisture intake to the user based on this. For example, if the user is not taking in enough moisture, the suggestion unit can suggest increasing the amount of moisture intake. Also, if the user is taking in too much moisture, the suggestion unit can suggest an appropriate amount of moisture intake. Furthermore, the suggestion unit can suggest the timing of moisture intake according to a specific situation, such as after the user exercises. This allows the user to take in appropriate moisture, which is expected to maintain and improve the user's health.

[0053] The health management support system may further include a nutritional analysis unit that analyzes the user's dietary content in detail. The nutritional analysis unit may analyze in detail the nutrients of the meals consumed by the user, and the suggestion unit may propose a balanced meal plan to the user based on the analysis. For example, if the user is consuming an excessive amount of a particular nutrient, the suggestion unit may suggest reducing the amount of that nutrient. Also, if the user is deficient in a nutrient, the suggestion unit may propose a meal plan to supplement the nutrient. Furthermore, if the user has a particular health goal, a nutritionally balanced meal plan according to the goal may be proposed. This allows the user to continue a healthy diet, and an improvement in overall health can be expected.

[0054] The health management support system may further include an exercise evaluation unit that evaluates the user's exercise performance. The exercise evaluation unit performs a detailed analysis of the user's exercise data, and the instructor can suggest improvements to the user's exercise based on the analysis. For example, if the user is not able to perform a specific exercise effectively, the instructor can suggest a correct way to perform the exercise. Also, if the user is not able to properly adjust the frequency or intensity of the exercise, the instructor can suggest a way to adjust it. Furthermore, if the user has a specific exercise goal, an exercise plan according to the goal can be suggested. This allows the user to perform effective exercise, and overall health improvement can be expected.

[0055] The health management support system may further include a life rhythm acquisition unit that monitors the user's life rhythm. The life rhythm acquisition unit records the user's wake-up time, bedtime, meal timing, etc., and the suggestion unit can suggest an appropriate life rhythm to the user based on this. For example, if the user has an irregular life rhythm, the suggestion unit can suggest a regular life rhythm. Also, if the user stays up late, it can suggest going to bed early and getting up early. Furthermore, if the user is unable to properly adjust meal timing, it can suggest appropriate meal timing. This allows the user to maintain a healthy life rhythm, and overall health improvement can be expected.

[0056] The health management support system may further include an exercise history analysis unit that analyzes the user's exercise history. The exercise history analysis unit performs detailed analysis of the data of the user's past exercises, and the instructor can suggest improvements to the user's exercise based on the analysis. For example, if the user has been performing a specific exercise continuously, the instructor can evaluate the effectiveness of the exercise and suggest further improvements. In addition, if the user has not been able to properly adjust the frequency or intensity of the exercise, the instructor can suggest a method of adjustment. Furthermore, if the user has a specific exercise goal, an exercise plan according to the goal can be proposed. This allows the user to perform effective exercise, and overall health improvement can be expected.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The first reception unit receives one or more pieces of information among the results of the user's medical checkup and medical examination data. For example, the first reception unit can receive data such as the user's blood test results and electrocardiogram results. The first reception unit can also receive medical examination data such as the user's height, weight, and blood pressure. Step 2: The instruction unit provides the user with instruction regarding diet and exercise based on the information received by the first reception unit. For example, the instruction unit can provide instruction regarding calorie restrictions and the type and frequency of exercise for the user. The instruction unit can also provide appropriate diet and exercise advice according to the user's health condition. Step 3: The second reception unit receives the registration of the user's amount of exercise and dietary details. For example, the second reception unit can register the number of steps the user takes on his / her commute and photos of what he / she eats every hour. The second reception unit can also receive information such as the user's exercise time and intensity, and the calories and nutrients of meals. Step 4: The suggestion unit suggests meal contents to the user based on the information received by the second reception unit. For example, the suggestion unit can suggest meal contents for the next day or approximate meal contents for each week. The suggestion unit can also suggest an appropriate meal plan according to the user's health condition and goals. This allows the user to continue a healthy diet, and is expected to improve weight and blood data.

[0059] (Example 2) A health management support system according to an embodiment of the present invention is a system that receives the results of a medical checkup and medical examination data of a user, and provides guidance on diet and exercise based on the results. In this health management support system, a first reception unit receives the results of a medical checkup and medical examination data of a user. Next, based on the information received by the first reception unit, an instruction unit provides guidance on diet and exercise to the user. Furthermore, a second reception unit receives registration of the user's amount of exercise and dietary content. For example, the number of steps a user takes during commuting and photos of what they eat can be registered every hour. Based on the information received by the second reception unit, a suggestion unit suggests dietary content to the user. For example, it can suggest the next day's dietary content and approximate dietary content for each week. This system allows a user to understand his or her health condition and to take appropriate diet and exercise. For example, if a user wants to lose weight, the system provides guidance on appropriate diet and exercise based on the user's current weight and blood data. In addition, by registering photos of what the user has eaten, the system analyzes the information and suggests the next day's dietary content. This allows the user to continue eating a healthy diet, and is expected to improve weight and blood data. This enables the health management support system to efficiently manage the user's health condition and provide appropriate dietary and exercise advice.

[0060] The health management support system according to the embodiment includes a first reception unit, an instruction unit, a second reception unit, and a suggestion unit. The first reception unit receives one or more pieces of information among the results of a medical checkup and medical examination data of the user. For example, the first reception unit can receive data such as the user's blood test results and electrocardiogram results. The first reception unit can also receive medical examination data such as the user's height, weight, and blood pressure. The instruction unit provides the user with instruction regarding diet and exercise based on the information received by the first reception unit. For example, the instruction unit can provide instruction regarding the calorie restriction of the user's diet and the type and frequency of exercise. The instruction unit can also provide appropriate diet and exercise advice according to the user's health condition. The second reception unit receives registration of the user's exercise amount and dietary content. For example, the second reception unit can register the number of steps the user walked during commuting and photos of what the user ate every hour. The second reception unit can also receive information such as the user's exercise time and intensity, and the calories and nutrients of the diet. The suggestion unit suggests meal contents to the user based on the information accepted by the second acceptance unit. For example, the suggestion unit can suggest meal contents for the next day and approximate meal contents for each week. The suggestion unit can also suggest an appropriate meal plan according to the user's health condition and goals. As a result, the health management support system according to the embodiment can efficiently manage the user's health condition and provide appropriate diet and exercise guidance. For example, the suggestion unit can receive the user's meal contents as input and suggest the next day's meal contents using the generation AI. The generation AI generates an appropriate meal plan based on the user's health condition and goals. For example, the generation AI can analyze the user's meal contents and suggest a meal plan based on nutritional balance and calorie restriction. As a result, the user can continue to eat a healthy diet, and improvement in weight and blood data can be expected.

[0061] The first reception unit receives one or more pieces of information among the results of the medical checkup and the health checkup data of the user. For example, the first reception unit can receive data such as the user's blood test results and electrocardiogram results. Specifically, the blood test results include blood glucose levels, cholesterol levels, and liver function indicators, and the electrocardiogram results include heart rate and the presence or absence of arrhythmia. The first reception unit can also receive health checkup data such as the user's height, weight, and blood pressure. These data are collected by the user inputting the results of tests taken at a medical institution in digital format or by acquiring data directly from the medical institution. In addition, the first reception unit can also receive the user's past health checkup data and medical history, which makes it possible to grasp long-term changes in the user's health condition. These data are securely stored in a cloud server and used for subsequent analysis and guidance. The first reception unit provides an interface that allows the user to easily input data and also has a check function to ensure the accuracy and completeness of the data. For example, if the input data contains abnormal values, an alert is displayed to prompt the user to recheck. In addition, when data input is completed, a confirmation message is sent to the user to notify the user that the data has been correctly accepted. This enables the first accepting unit to efficiently and accurately collect the user's health data and improve the reliability of the entire system.

[0062] The instruction unit provides the user with instruction on diet and exercise based on the information received by the first reception unit. For example, the instruction unit can provide instruction on calorie restrictions and the type and frequency of exercise for the user. Specifically, the instruction unit analyzes data such as the user's blood test results, electrocardiogram results, height, weight, and blood pressure, and proposes optimal meal plans and exercise programs for each user. For example, a carbohydrate-restricted diet is proposed for a user with high blood sugar levels, and mild aerobic exercise is recommended for a user with an unstable heart rate. The instruction unit can also provide appropriate diet and exercise advice according to the user's health condition. For example, a low-calorie, high-protein diet is recommended for a user who tends to be obese, and specific guidance is provided on the frequency and method of muscle training for a user who wants to increase muscle strength. The instruction unit uses AI to analyze the user's data and generate optimal instruction content. The AI ​​can provide optimal instruction content for each user based on past data and success stories of similar users. Furthermore, the instruction unit collects user feedback and evaluates the effectiveness of the instruction content. For example, the system monitors how weight and blood pressure change as a result of the user following the advice and modifies the advice content as necessary. This allows the advice unit to continuously improve the user's health condition and support optimal health management.

[0063] The second reception unit accepts the registration of the user's amount of exercise and dietary content. For example, the second reception unit can register the number of steps the user took during commuting and photos of what they ate every hour. Specifically, the user can automatically record data such as the number of steps, exercise time, and exercise intensity using a smartphone or wearable device. In addition, the calorie and nutrient content of the meal can be automatically analyzed by taking a photo of what the user ate and uploading it through the app. The second reception unit stores these data in a cloud server and uses them for subsequent analysis and proposals. In addition, the second reception unit also provides an interface for the user to manually input data, allowing the user to specifically record the details of the meal and the content of the exercise. For example, the user can input the calorie and nutrient content of the meal, the type, time, and intensity of the exercise, etc. in detail. The second reception unit can centrally manage these data and link with other systems and departments as necessary. For example, the exercise data and dietary data of the user can be made accessible to the guidance department and proposal department, and used as basic data for providing optimal guidance and proposals to each individual user. This enables the second reception unit to efficiently and accurately collect information on the amount of exercise and dietary details of the user, thereby improving the performance of the entire system.

[0064] The suggestion unit suggests meal contents to the user based on the information received by the second reception unit. For example, the suggestion unit can suggest the next day's meal contents and the approximate meal contents for each week. Specifically, the suggestion unit analyzes the user's exercise amount, meal contents, and health condition, and generates an optimal meal plan based on nutritional balance and calorie restriction. The suggestion unit analyzes these data using the generation AI and suggests a meal plan according to the user's health condition and goals. For example, the generation AI can analyze the user's meal contents and suggest a meal plan based on nutritional balance and calorie restriction. Specifically, the generation AI suggests the next day's meal contents based on the user's past meal data and exercise data, and provides a menu that takes nutritional balance into consideration. In addition, the generation AI can customize the meal plan according to the user's health condition and goals. For example, it suggests a low-calorie, high-protein meal to a user who is aiming to lose weight, and suggests a meal containing appropriate nutrients to a user who wants to increase muscle strength. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the suggestion unit monitors how the user's weight and blood data change as a result of following the proposed meal plan, and modifies the meal plan as necessary. This allows the suggestion unit to provide the user with an optimal meal plan and support the improvement of the user's health condition.

[0065] The suggestion unit can suggest at least one of the next day's meal contents or the weekly meal contents. The suggestion unit can suggest, for example, the menus for the next day's breakfast, lunch, and dinner. For example, the suggestion unit can suggest balanced meal contents based on the user's health condition and goals. The suggestion unit can also suggest a weekly meal plan and menu. For example, the suggestion unit can suggest a weekly meal plan based on the user's health condition and goals. This allows the user to continue a healthy diet from a long-term perspective. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can receive the user's meal contents as input, input a prompt to the generation AI, and the generation AI can generate a meal plan. The generation AI generates an appropriate meal plan based on the user's health condition and goals. For example, the generation AI can analyze the user's meal contents and suggest a meal plan based on nutritional balance and calorie restriction. This allows the user to continue a healthy diet, and improvement in weight and blood data can be expected.

[0066] The first reception unit can estimate the user's emotion and adjust the timing of receiving the medical checkup results and medical examination data based on the estimated emotion of the user. For example, when the user is feeling stressed, the first reception unit can receive data during a time period when the user can relax. Also, when the user is relaxed, the first reception unit can immediately receive data. Furthermore, when the user is busy, the first reception unit can receive data during a time period when the user is not busy. This can reduce the burden on the user by adjusting the timing of receiving data according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0067] The first reception unit can analyze the user's past health check data and select the optimal reception method. For example, the first reception unit can preferentially suggest a reception method that the user has frequently used in the past. The first reception unit can also select the most efficient reception method from the user's past health check data. Furthermore, the first reception unit can also suggest the optimal reception method for a specific time period based on the user's past health check data. This allows efficient data reception by selecting the optimal reception method based on the user's past data. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the first reception unit can input the user's past health check data to the generation AI and cause the generation AI to select the optimal reception method.

[0068] The first reception unit can perform filtering based on the user's current health condition and lifestyle habits when receiving the health checkup data. The first reception unit can preferentially receive important data based on the user's current health condition, for example. The first reception unit can also filter highly relevant data based on the user's lifestyle habits. Furthermore, the first reception unit can adjust the type of data to be received according to the user's health condition and lifestyle habits. This makes it possible to filter data according to the user's health condition and lifestyle habits. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first reception unit can input data on the user's health condition and lifestyle habits to the generation AI and cause the generation AI to perform filtering.

[0069] The first reception unit can estimate the user's emotion and determine the priority of data to be received based on the estimated user's emotion. For example, when the user is stressed, the first reception unit can preferentially receive important data. Also, when the user is relaxed, the first reception unit can equally receive all data. Furthermore, when the user is busy, the first reception unit can preferentially receive the most important data. In this way, by determining the priority of data according to the user's emotion, important data can be preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0070] When accepting the medical examination data, the first reception unit can preferentially accept highly relevant data in consideration of the geographical location information of the user. For example, when the user lives in a specific area, the first reception unit can preferentially accept data related to the area. In addition, when the user is traveling, the first reception unit can also preferentially accept data related to the current location. Furthermore, the first reception unit can also accept the most relevant data based on the geographical location information of the user. This makes it possible to preferentially accept data that is highly relevant based on the geographical location information of the user. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the first reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to select highly relevant data.

[0071] The first reception unit can analyze the social media activity of the user and accept related data when accepting the health checkup data. The first reception unit can accept related data based on, for example, health information shared by the user on social media. The first reception unit can also analyze health-related interests from the user's social media activity and accept related data. Furthermore, the first reception unit can also accept the most relevant data based on the user's social media activity. This makes it possible to accept related data based on the user's social media activity. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the first reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related data.

[0072] The instruction unit can estimate the user's emotion and adjust the way of expressing the instruction based on the estimated user's emotion. For example, when the user is stressed, the instruction unit can provide instruction in gentle words. In addition, when the user is relaxed, the instruction unit can provide detailed instruction. Furthermore, when the user is in a hurry, the instruction unit can provide concise instruction. This allows for more effective instruction by adjusting the way of expressing the instruction according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0073] The instruction unit can adjust the level of detail of the instruction based on the user's health goal during instruction. For example, if the user wants to lose weight, the instruction unit can provide detailed dietary instruction. Also, if the user wants to increase muscle strength, the instruction unit can provide detailed exercise instruction. Furthermore, if the user wants to maintain health, the instruction unit can provide balanced instruction. This enables detailed instruction according to the user's health goal. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's health goal data into the generation AI and cause the generation AI to adjust the level of detail of the instruction.

[0074] The instruction unit can apply different instruction algorithms according to the user's lifestyle during instruction. For example, if the user is a night owl, the instruction unit can provide instruction suitable for the night. Also, if the user is a morning person, the instruction unit can provide instruction suitable for the morning. Furthermore, if the user has an irregular lifestyle, the instruction unit can provide flexible instruction. This enables flexible instruction according to the user's lifestyle. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's lifestyle data into the generation AI and cause the generation AI to apply the instruction algorithm.

[0075] The instruction unit can estimate the user's emotion and adjust the length of the instruction based on the estimated user's emotion. For example, the instruction unit can provide short instruction when the user is stressed. Also, the instruction unit can provide long instruction when the user is relaxed. Furthermore, the instruction unit can provide brief instruction when the user is in a hurry. This allows more appropriate instruction by adjusting the length of the instruction according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0076] The guidance unit can determine the priority of guidance based on the time of submission of the user's health data when providing guidance. The guidance unit can provide the latest guidance based on, for example, the health data recently submitted by the user. The guidance unit can also determine the priority based on the health data submitted by the user in the past. Furthermore, the guidance unit can adjust the priority of guidance according to the time of submission of the user's health data. This makes it possible to provide guidance with a priority according to the time of submission of the user's health data. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the time of submission of the user's health data to the generation AI and cause the generation AI to determine the priority of guidance.

[0077] The instruction unit can adjust the order of instruction based on the relevance of the user during instruction. The instruction unit can prioritize the most relevant instruction based on, for example, the user's health goal. The instruction unit can also prioritize the most relevant instruction based on the user's lifestyle habits. Furthermore, the instruction unit can also prioritize the most relevant instruction based on the user's health condition. This makes it possible to adjust the order of instruction based on the user's relevance. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's relevance data to the generation AI and cause the generation AI to adjust the order of instruction.

[0078] The second reception unit can estimate the user's emotion and adjust the timing of registering the amount of exercise and meal contents based on the estimated emotion of the user. For example, when the user is feeling stressed, the second reception unit can prompt the user to register during a time period when the user can relax. In addition, when the user is relaxed, the second reception unit can prompt the user to register immediately. Furthermore, when the user is busy, the second reception unit can prompt the user to register during a time period when the user is not busy. This makes it possible to adjust the registration timing according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the second reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0079] The second reception unit can analyze the user's past exercise amount and meal contents and select the optimal registration method. For example, the second reception unit can preferentially suggest a registration method that the user has frequently used in the past. The second reception unit can also select the most efficient registration method from the user's past exercise amount and meal contents. Furthermore, the second reception unit can also suggest the optimal registration method for a specific time period based on the user's past exercise amount and meal contents. This makes it possible to select the optimal registration method based on the user's past data. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the second reception unit can input data on the user's past exercise amount and meal contents to the generation AI and cause the generation AI to select the optimal registration method.

[0080] The second reception unit can perform filtering based on the user's current living situation and field of interest when registering the amount of exercise and meal contents. The second reception unit can, for example, preferentially register important data based on the user's current living situation. The second reception unit can also filter highly relevant data based on the user's field of interest. Furthermore, the second reception unit can adjust the type of data to be registered according to the user's living situation and field of interest. This makes it possible to filter data according to the user's living situation and field of interest. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the second reception unit can input data on the user's living situation and field of interest to the generation AI and cause the generation AI to perform filtering.

[0081] The second reception unit can estimate the user's emotion and determine the priority of data to be registered based on the estimated user's emotion. For example, when the user is feeling stressed, the second reception unit can preferentially register important data. In addition, when the user is relaxed, the second reception unit can also register all data equally. Furthermore, when the user is busy, the second reception unit can also preferentially register the most important data. This makes it possible to determine the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the second reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The second reception unit can preferentially register highly relevant data in consideration of the user's geographical location information when registering the amount of exercise or the contents of meals. For example, if the user lives in a specific area, the second reception unit can preferentially register data related to that area. In addition, if the user is traveling, the second reception unit can also preferentially register data related to the current location. Furthermore, the second reception unit can also register the most relevant data based on the user's geographical location information. This makes it possible to preferentially register data based on the user's geographical location information. A part or all of the above-mentioned processing in the second reception unit may be performed, for example, using AI or may be performed without using AI. For example, the second reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0083] The second reception unit can analyze the user's social media activity and register related data when registering the amount of exercise and dietary details. The second reception unit can register related data based on, for example, health information shared by the user on social media. The second reception unit can also analyze the user's health interests from the user's social media activity and register related data. Furthermore, the second reception unit can also register the most relevant data based on the user's social media activity. This makes it possible to register related data based on the user's social media activity. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the second reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related data.

[0084] The suggestion unit can estimate the user's emotion and adjust the meal content suggestion method based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit can suggest a meal content that can relax the user. In addition, when the user is relaxed, the suggestion unit can also suggest a balanced meal content. Furthermore, when the user is in a hurry, the suggestion unit can also suggest a meal content that can be easily prepared. This makes it possible to adjust the meal content suggestion method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0085] The suggestion unit can adjust the level of detail of the suggestion based on the user's health goal when making a suggestion. For example, if the user wants to lose weight, the suggestion unit can suggest a calorie-restricted meal. Also, if the user wants to increase muscle strength, the suggestion unit can suggest a protein-rich meal. Furthermore, if the user wants to maintain health, the suggestion unit can suggest a balanced meal. This enables detailed suggestions according to the user's health goal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's health goal data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0086] The suggestion unit can apply different suggestion algorithms according to the user's lifestyle at the time of suggestion. For example, if the user is a night owl, the suggestion unit can suggest meal contents suitable for the night. Also, if the user is a morning person, the suggestion unit can suggest meal contents suitable for the morning. Furthermore, if the user has an irregular lifestyle, the suggestion unit can suggest flexible meal contents. This enables flexible suggestions according to the user's lifestyle. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can make a short suggestion when the user is stressed. In addition, the suggestion unit can make a detailed suggestion when the user is relaxed. Furthermore, the suggestion unit can make a brief suggestion when the user is in a hurry. This enables the length of the suggestion to be adjusted according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute the emotion estimation.

[0088] At the time of making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the user's health data. The suggestion unit can make the latest proposal based on, for example, the health data recently submitted by the user. The suggestion unit can also determine the priority based on the health data previously submitted by the user. Furthermore, the suggestion unit can adjust the priority of the proposal according to the time of submission of the user's health data. This makes it possible to propose a priority according to the time of submission of the user's health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the time of submission of the user's health data to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0089] The suggestion unit can adjust the order of the suggestions based on the relevance of the user at the time of suggestion. The suggestion unit can prioritize the most relevant suggestions based on, for example, the health goals of the user. The suggestion unit can also prioritize the highly relevant suggestions based on the lifestyle habits of the user. Furthermore, the suggestion unit can also prioritize the highly relevant suggestions based on the health condition of the user. This makes it possible to adjust the order of the suggestions based on the relevance of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the relevance data of the user to the generation AI and cause the generation AI to adjust the order of the suggestions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The health management support system may further include a sleep acquisition unit that acquires the user's sleep data. The sleep acquisition unit monitors the user's sleep patterns and sleep quality, and the guidance unit can provide the user with appropriate sleep guidance based on the results. For example, if the user is not getting enough sleep, the guidance unit can suggest extending the sleep time or improving the sleep environment. In addition, if the user has an irregular sleep pattern, the guidance unit can also suggest a regular sleep schedule. Furthermore, if the user is feeling stressed, the guidance unit can also suggest a relaxing sleep method. This allows the user to sleep better, which is expected to improve the overall health condition.

[0092] The health management support system may further include a moisture acquisition unit that monitors the amount of moisture intake of the user. The moisture acquisition unit records the amount of moisture intake by the user per day, and the suggestion unit can suggest appropriate moisture intake to the user based on this. For example, if the user is not taking in enough moisture, the suggestion unit can suggest increasing the amount of moisture intake. Also, if the user is taking in too much moisture, the suggestion unit can suggest an appropriate amount of moisture intake. Furthermore, the suggestion unit can suggest the timing of moisture intake according to a specific situation, such as after the user exercises. This allows the user to take in appropriate moisture, which is expected to maintain and improve the user's health.

[0093] The health management support system may further include a stress acquisition unit that monitors the user's stress level. The stress acquisition unit acquires biometric data such as the user's heart rate and skin electrodermal activity, and evaluates the user's stress level based on the biometric data. The guidance unit may suggest an appropriate stress management method based on the user's stress level. For example, if the user shows a high stress level, the guidance unit may suggest relaxation techniques or exercises for stress reduction. Also, if the user shows a low stress level, the guidance unit may provide advice for maintaining that state. Furthermore, if the user is prone to feeling stress in a particular situation, a suggestion may be made to avoid that situation. This allows the user to effectively manage stress and improve overall health.

[0094] The health management support system may further include a nutritional analysis unit that analyzes the user's dietary content in detail. The nutritional analysis unit may analyze the nutrients of the meals taken by the user in detail, and the suggestion unit may propose a balanced meal plan to the user based on the analysis. For example, if the user is taking in an excessive amount of a particular nutrient, the suggestion unit may suggest reducing the amount of that nutrient. Also, if the user is deficient in a nutrient, the suggestion unit may propose a meal plan to supplement the nutrient. Furthermore, if the user has a particular health goal, a nutritionally balanced meal plan according to the goal may be proposed. This allows the user to continue a healthy diet, and an improvement in overall health can be expected.

[0095] The health management support system may further include an exercise evaluation unit that evaluates the user's exercise performance. The exercise evaluation unit performs a detailed analysis of the user's exercise data, and the instructor can suggest improvements to the user's exercise based on the analysis. For example, if the user is not able to perform a specific exercise effectively, the instructor can suggest a correct way to perform the exercise. Also, if the user is not able to properly adjust the frequency or intensity of the exercise, the instructor can suggest a way to adjust it. Furthermore, if the user has a specific exercise goal, an exercise plan according to the goal can be suggested. This allows the user to perform effective exercise, and overall health improvement can be expected.

[0096] The health management support system can further estimate the user's emotions and suggest a type of exercise based on the estimated user's emotions. For example, if the user is feeling stressed, relaxing yoga or stretching can be suggested. Also, if the user is feeling energetic, running or high-intensity interval training can be suggested. Furthermore, if the user is tired, light walking or relaxation exercise can be suggested. This allows for more effective exercise by suggesting a type of exercise according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the guidance unit can input the user's emotion data into the generation AI and cause the generation AI to execute emotion estimation.

[0097] The health management support system may further include a life rhythm acquisition unit that monitors the user's life rhythm. The life rhythm acquisition unit records the user's wake-up time, bedtime, meal timing, etc., and the suggestion unit can suggest an appropriate life rhythm to the user based on this. For example, if the user has an irregular life rhythm, the suggestion unit can suggest a regular life rhythm. Also, if the user stays up late, it can suggest going to bed early and getting up early. Furthermore, if the user is unable to properly adjust meal timing, it can suggest appropriate meal timing. This allows the user to maintain a healthy life rhythm, and overall health improvement can be expected.

[0098] The health management support system can further estimate the user's emotions and suggest meal timing based on the estimated user's emotions. For example, if the user is feeling stressed, it can suggest that the user eats during a time period when the user can relax. Also, if the user is relaxed, it can suggest normal meal timing. Furthermore, if the user is busy, it can suggest that the user eats during a time period when the user is free. This allows for more effective meal management by suggesting meal timing according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The health management support system may further include an exercise history analysis unit that analyzes the user's exercise history. The exercise history analysis unit performs detailed analysis of the data of the user's past exercises, and the instructor can suggest improvements to the user's exercise based on the analysis. For example, if the user has been performing a specific exercise continuously, the instructor can evaluate the effectiveness of the exercise and suggest further improvements. In addition, if the user has not been able to properly adjust the frequency or intensity of the exercise, the instructor can suggest a method of adjustment. Furthermore, if the user has a specific exercise goal, an exercise plan according to the goal can be proposed. This allows the user to perform effective exercise, and overall health improvement can be expected.

[0100] The health management support system can further estimate the user's emotions and suggest meal contents based on the estimated user's emotions. For example, if the user is stressed, a meal content that can relax the user can be suggested. Also, if the user is relaxed, a balanced meal content can be suggested. Furthermore, if the user is in a hurry, a meal content that can be easily prepared can be suggested. This makes it possible to suggest meal contents according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0101] The process flow of the second embodiment will be briefly described below.

[0102] Step 1: The first reception unit receives one or more pieces of information among the results of the user's medical checkup and medical examination data. For example, the first reception unit can receive data such as the user's blood test results and electrocardiogram results. The first reception unit can also receive medical examination data such as the user's height, weight, and blood pressure. Step 2: The instruction unit provides the user with instruction regarding diet and exercise based on the information received by the first reception unit. For example, the instruction unit can provide instruction regarding calorie restrictions and the type and frequency of exercise for the user. The instruction unit can also provide appropriate diet and exercise advice according to the user's health condition. Step 3: The second reception unit receives the registration of the user's amount of exercise and dietary details. For example, the second reception unit can register the number of steps the user takes on his / her commute and photos of what he / she eats every hour. The second reception unit can also receive information such as the user's exercise time and intensity, and the calories and nutrients of meals. Step 4: The suggestion unit suggests meal contents to the user based on the information received by the second reception unit. For example, the suggestion unit can suggest meal contents for the next day or approximate meal contents for each week. The suggestion unit can also suggest an appropriate meal plan according to the user's health condition and goals. This allows the user to continue a healthy diet, and is expected to improve weight and blood data.

[0103] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0105] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0106] For example, the first reception unit can receive the results of a medical checkup or medical examination data of the user using the reception device 38 or the camera 42 of the smart device 14. The instruction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the user with instruction regarding diet and exercise. The second reception unit can receive the registration of the user's amount of exercise and dietary content using, for example, the reception device 38 or the camera 42 of the smart device 14. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests dietary content to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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, the RAM 30, and the 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.

[0110] 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.

[0111] 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 the voice according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0113] 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 performed in a secure state.

[0114] Fig. 4 shows an example of 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.

[0115] 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.

[0116] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0120] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0121] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] For example, the first reception unit can receive the results of a medical checkup or medical examination data of the user using the microphone 238 or the camera 42 of the smart glasses 214. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides guidance on diet and exercise to the user. The second reception unit can receive registration of the amount of exercise and dietary content of the user using, for example, the microphone 238 or the camera 42 of the smart glasses 214. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests dietary content to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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, the RAM 30, and the 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.

[0126] 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.

[0127] 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 the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] 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 performed in a secure state.

[0130] 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.

[0131] 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.

[0132] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 voice indicating a user input for 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.

[0136] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] For example, the first reception unit can receive the results of a medical checkup or medical examination data of the user using the microphone 238 or camera 42 of the headset type terminal 314. The instruction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the user with instruction regarding diet and exercise. The second reception unit can receive the registration of the user's amount of exercise and dietary content using, for example, the microphone 238 or camera 42 of the headset type terminal 314. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests dietary content to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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, the RAM 30, and the 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.

[0142] 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.

[0143] 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 the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0145] 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 performed in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0147] 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.

[0148] 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.

[0149] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0151] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] 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 a voice indicating a user input for 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.

[0153] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0155] For example, the first reception unit can receive the results of a medical checkup or medical examination data of the user using the microphone 238 or the camera 42 of the robot 414. The instruction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the user with instruction regarding diet and exercise. The second reception unit can receive the registration of the user's amount of exercise and dietary content using, for example, the microphone 238 or the camera 42 of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests dietary content to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0157] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0159] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0161] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0163] 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.

[0164] 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 Universal Serial Bus (USB) 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.

[0165] In addition, 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 upon request from the data processing device 12.

[0166] 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.

[0167] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0168] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0169] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0170] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0172] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0174] (Appendix 1) A first reception unit that receives one or more pieces of information of a result of a medical checkup of a user and medical checkup data of the user; an instruction unit that provides the user with instruction regarding diet or exercise based on the information received by the first reception unit; A second reception unit that receives registration of the amount of exercise or meal contents of the user; a suggestion unit that suggests meal contents to the user based on the information accepted by the second acceptance unit. A system characterized by: (Appendix 2) The suggestion unit, Provide at least one meal plan for the next day or a weekly meal plan 2. The system of claim 1, (Appendix 3) The first reception unit is Estimating user emotions and adjusting the timing of accepting medical checkup results and data based on the estimated user emotions 2. The system of claim 1, (Appendix 4) The first reception unit is Analyze the user's past medical checkup data and select the appropriate reception method 2. The system of claim 1, (Appendix 5) The first reception unit is When accepting health checkup data, filtering is performed based on the user's current health condition and lifestyle habits. 2. The system of claim 1, (Appendix 6) The first reception unit is Infer user emotions and prioritize data to be accepted based on the estimated user emotions. 2. The system of claim 1, (Appendix 7) The first reception unit is When accepting health checkup data, the system takes into account the user's geographic location information and prioritizes accepting data that is highly relevant. 2. The system of claim 1, (Appendix 8) The first reception unit is When accepting health checkup data, analyze users' social media activity and accept related data. 2. The system of claim 1, (Appendix 9) The leadership team: Estimating a user's emotions and adjusting the way instructions are presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 10) The leadership team: During instruction, adjust the level of detail of instruction based on the user's health goals 2. The system of claim 1, (Appendix 11) The leadership team: During guidance, different guidance algorithms are applied according to the user's lifestyle habits. 2. The system of claim 1, (Appendix 12) The leadership team: Estimating a user's emotion and adjusting the length of instruction based on the estimated user's emotion 2. The system of claim 1, (Appendix 13) The leadership team: During guidance, the guidance is prioritized based on when the user submitted their health data. 2. The system of claim 1, (Appendix 14) The leadership team: During instruction, adjust the instruction sequence based on the user's relevance 2. The system of claim 1, (Appendix 15) The second reception unit is The system estimates the user's emotions and adjusts the timing of registering the amount of exercise and meal contents based on the estimated user emotions. 2. The system of claim 1, (Appendix 16) The second reception unit is Analyze the user's past exercise and dietary habits to select the optimal registration method 2. The system of claim 1, (Appendix 17) The second reception unit is When registering exercise and dietary information, filtering is performed based on the user's current lifestyle and interests. 2. The system of claim 1, (Appendix 18) The second reception unit is Estimate user emotions and prioritize data to be registered based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The second reception unit is When registering the amount of exercise and the contents of meals, the system takes into account the user's geographic location information and prioritizes the registration of data that is more relevant. 2. The system of claim 1, (Appendix 20) The second reception unit is When registering the amount of exercise and dietary details, the user's social media activity is analyzed and related data is registered. 2. The system of claim 1, (Appendix 21) The suggestion unit, Estimate the user's emotions and adjust the meal suggestion method based on the estimated user's emotions 2. The system of claim 1, (Appendix 22) The suggestion unit, When making suggestions, adjust the level of detail based on the user's health goals 2. The system of claim 1, (Appendix 23) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the user's lifestyle. 2. The system of claim 1, (Appendix 24) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, the system prioritizes the suggestions based on when the user submitted their health data. 2. The system of claim 1, (Appendix 26) The suggestion unit, When making suggestions, adjust the order of suggestions based on user relevance 2. The system of claim 1, [Explanation of symbols]

[0175] 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 first reception unit that receives one or more pieces of information of a result of a medical checkup of a user and medical examination data of the user; an instruction unit that provides the user with instruction regarding diet or exercise based on the information received by the first reception unit; A second reception unit that receives registration of the amount of exercise or meal contents of the user; a suggestion unit that suggests meal contents to the user based on the information accepted by the second acceptance unit. A system characterized by:

2. The suggestion unit, Provide at least one meal plan for the next day or a weekly meal plan 2. The system of claim 1 .

3. The first reception unit is The user's feelings are estimated, and the timing of accepting the results of the medical checkup and the medical examination data is adjusted based on the estimated feelings of the user.

2. The system of claim 1 .

4. The first reception unit is Analyze the user's past medical examination data and select an appropriate reception method.

2. The system of claim 1 .

5. The first reception unit is When receiving health checkup data, filtering is performed based on the user's current health condition and lifestyle habits.

2. The system of claim 1 .

6. The first reception unit is Estimating the user's emotion and determining a priority order for data to be accepted based on the estimated user's emotion.

2. The system of claim 1 .

7. The first reception unit is When accepting medical checkup data, data with high relevance is preferentially accepted in consideration of the geographical location information of the user.

2. The system of claim 1 .

8. The first reception unit is Upon receipt of the health check data, the social media activity of the user is analyzed and relevant data is received.

2. The system of claim 1 .

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