System

Abstract

An object of the system according to the embodiment is to individually propose and adjust a diet plan for healthy weight gain for thin people.SOLUTION: A system includes a user information input unit, a meal plan suggestion unit, and a meal plan adjustment unit. The user information input unit inputs detailed information on the user's physical constitution, dietary preferences, and health condition. The meal plan suggestion unit suggests a meal plan based on the information input by the user information input unit. The meal plan adjustment part adjusts the meal plan suggested by the meal plan suggestion part on the basis of the user's physical condition and reaction to the meal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to individually tailor meal plans to help lean people gain weight in a healthy way.

[0005] The system according to the embodiment aims to individually propose and adjust a meal plan for thin people to gain weight in a healthy way. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, a meal plan proposal unit, and a meal plan adjustment unit. The user information input unit inputs detailed information about the user's constitution, dietary preferences, and health condition. The meal plan proposal unit proposes a meal plan based on the information input by the user information input unit. The meal plan adjustment unit adjusts the meal plan proposed by the meal plan proposal unit based on the user's physical condition and dietary response. [Effects of the Invention]

[0007] The system according to the embodiment can propose and tailor a personalized meal plan for lean people to gain weight in a healthy way. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A nutrition management system according to an embodiment of the present invention is a system that provides an individually customized meal plan based on detailed information such as a user's constitution, food preferences, health status, etc. This allows the nutrition management system to propose and adjust meal plans according to the user's individual needs, provide customized recipes, track progress, and maintain motivation.

[0029] A nutrition management system according to an embodiment includes a user information input unit, a meal plan proposal unit, and a meal plan adjustment unit. The user information input unit inputs detailed information about the user's constitution, dietary preferences, and health condition. For example, the user inputs information such as height, weight, whether or not they have allergies, favorite and disliked foods, and past health checkup results. The meal plan proposal unit proposes a meal plan based on the information input by the user information input unit. For example, it proposes a meal plan that is easy to digest, high in calories, and nutritious. The generation AI generates a meal plan using a text generation AI (e.g., LLM). The meal plan adjustment unit adjusts the meal plan proposed by the meal plan proposal unit based on the user's physical condition and dietary reactions. For example, if the user has an allergic reaction to a particular ingredient, that ingredient is excluded and alternative ingredients are suggested. This makes it possible to propose and adjust meal plans according to the user's individual needs.

[0030] The user information input unit automatically imports the user's past dietary and exercise history, enabling analysis based on more detailed information. For example, the user information input unit automatically imports data from fitness apps or food recording apps that the user has used in the past, and acquires detailed dietary and exercise histories. For example, the data can be linked using an API. This enables more detailed analysis based on the user's past dietary and exercise histories.

[0031] The user information input unit can improve the accuracy of the input information by monitoring the user's lifestyle habits and daily activity level using a sensor. The user information input unit monitors the daily activity level using, for example, a sensor installed in the user's smartphone or wearable device. For example, data such as the number of steps taken, distance traveled, and sleep time is collected. In this way, the accuracy of the input information is improved by monitoring the lifestyle habits and activity level.

[0032] The user information input unit uses voice recognition technology to input user information, thereby reducing the effort required. The user information input unit, for example, introduces voice recognition technology so that the user can input information by voice. For example, what the user says is converted into text and automatically input into the app. In this way, the use of voice recognition technology can reduce the effort required to input user information.

[0033] The user information input unit can also input health information of the user's family and friends to provide a more comprehensive meal plan. For example, the user information input unit can add a function that allows the user to input health information of the user's family and friends to provide a more comprehensive meal plan. For example, allergy information and dietary preferences of all family members can be taken into account. This allows a more comprehensive meal plan to be provided by taking into account the health information of family and friends.

[0034] The meal plan proposal unit can provide a plan that incorporates seasonal ingredients, taking into account the season or local ingredients. For example, the meal plan proposal unit can provide a meal plan that incorporates seasonal ingredients, taking into account the season or local ingredients. For example, in spring, it can provide a menu that uses asparagus and strawberries, and in autumn, it can provide a menu that uses pumpkin and apples. This makes it possible to provide a nutritious meal plan by incorporating seasonal and local ingredients.

[0035] The meal plan proposal unit can provide a meal plan that takes into consideration optimal nutritional balance based on the user's genetic information. The meal plan proposal unit, for example, analyzes the user's genetic information and proposes a meal plan that takes into consideration optimal nutritional balance. For example, it provides a menu that includes nutrients corresponding to specific gene mutations. In this way, by proposing a meal plan based on genetic information, individual nutritional balance can be optimized.

[0036] The meal plan proposal unit can integrate an exercise plan into a meal plan to provide comprehensive health management. The meal plan proposal unit, for example, combines an exercise plan with a meal plan to provide comprehensive health management. For example, it proposes an exercise menu that matches the contents of the meal. In this way, by combining a meal plan and an exercise plan, comprehensive health management is possible.

[0037] The meal plan suggestion unit allows a user to share their meal plan with other users and receive feedback from the community. The meal plan suggestion unit provides, for example, a function for sharing a user's meal plan with other users and receiving feedback from the community. For example, the user can post a meal plan on a social networking site and receive comments and advice from other users. By sharing a meal plan, the user can receive feedback from the community and improve the plan.

[0038] The meal plan adjustment unit can acquire biometric data such as the user's blood glucose level or blood pressure in real time and adjust the meal plan based on that. The meal plan adjustment unit can acquire biometric data such as the user's blood glucose level or blood pressure in real time and adjust the meal plan based on that. For example, if the blood glucose level is high, low GI foods can be suggested. In this way, by acquiring biometric data in real time and adjusting the meal plan based on that, it is possible to provide an optimal meal plan according to the user's health condition.

[0039] The meal plan adjustment unit can analyze the user's meal history and make optimal adjustments based on past successes and failures. The meal plan adjustment unit, for example, analyzes the user's meal history and makes optimal adjustments based on past successes and failures. For example, it refers to meal plans that have led to weight gain in the past. In this way, by analyzing the user's past meal history and making optimal adjustments based on past successes and failures, it is possible to provide the user with an optimal meal plan.

[0040] The meal plan adjustment unit can coordinate with the meal plans of the user's family or friends to make adjustments for enjoying a meal together. The meal plan adjustment unit, for example, coordinates with the meal plans of the user's family or friends to make adjustments for enjoying a meal together. For example, it can make it so that all family members can enjoy the same menu. This allows the user to enjoy a meal together by coordinating the meal plan with family and friends.

[0041] The customized recipe provider can provide optimal substitute ingredients based on the user's ingredient preferences and allergy information. The customized recipe provider, for example, suggests optimal substitute ingredients based on the user's ingredient preferences and allergy information. For example, a user with a dairy allergy can be suggested a recipe that does not use dairy products. This allows the user to enjoy a meal without worry by suggesting substitute ingredients that take into account the user's ingredient preferences and allergy information.

[0042] The customized recipe providing unit can propose easy-to-make recipes taking into consideration the user's cooking skills and kitchen facilities. The customized recipe providing unit proposes easy-to-make recipes taking into consideration the user's cooking skills and kitchen facilities, for example. For example, it proposes easy recipes for beginners and menus that can be cooked in a microwave. By providing recipes that take into consideration the user's cooking skills and kitchen facilities, anyone can enjoy meals that are easy to make.

[0043] The customized recipe providing unit can combine elements of exercise or relaxation with recipes to provide comprehensive health management. The customized recipe providing unit can, for example, combine elements of exercise or relaxation with recipes to provide comprehensive health management. For example, it can suggest stretching or yoga to do after meals. In this way, by combining elements of exercise or relaxation with recipes, comprehensive health management becomes possible.

[0044] The customized recipe providing unit allows a user to share their recipes with other users and receive feedback from the community. The customized recipe providing unit provides, for example, a function for sharing a user's recipes with other users and receiving feedback from the community. For example, a user can post a recipe on a social networking site and receive comments and advice from other users. This allows a user to share a recipe and receive feedback from the community to improve the recipe.

[0045] The progress tracking unit can visualize the user's progress data to enable intuitive understanding in the form of a graph or chart. The progress tracking unit, for example, visualizes the user's progress data to enable intuitive understanding in the form of a graph or chart. For example, weight gain or calorie intake may be displayed in a line graph. In this way, visualizing the progress data makes it easier for the user to intuitively understand.

[0046] The progress tracking unit may implement a system that provides rewards or incentives according to the user's achievement of a goal. The progress tracking unit may implement a system that provides rewards or incentives according to the user's achievement of a goal. For example, a system may be provided that allows a user to earn a badge when they reach their target weight. This can increase the user's motivation by providing rewards or incentives according to the achievement of their goal.

[0047] The progress tracking unit allows users to share their progress data with other users, encouraging competition and cooperation within the community. The progress tracking unit provides, for example, a function for sharing progress data with other users, encouraging competition and cooperation within the community. For example, users can compare their progress with friends, enhancing their competitive spirit. This allows users to share their progress data, encouraging competition and cooperation within the community and increasing their motivation.

[0048] The progress tracking unit can provide advice from a personal trainer or a nutritionist according to the user's progress. The progress tracking unit, for example, implements a system that provides advice from a personal trainer or a nutritionist according to the user's progress. For example, the progress tracking unit receives advice from an expert based on the progress data. This allows the user to support their health management by providing advice from an expert according to their progress.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The user information input unit can input information about the user's living environment. For example, the climate and environment of the area where the user lives, and the type of residence (apartment, detached house, etc.) can be input. This makes it possible to propose meal plans that suit the user's living environment. For example, it is possible to propose menus that use ingredients that warm the body to a user living in a cold region. It is also possible to propose menus that are easy to prepare to a user living in an urban area. This makes it possible to provide meal plans that suit the user's living environment.

[0051] The user information input unit can automatically import the user's past dietary and exercise history and perform analysis based on more detailed information. For example, it can automatically import data from fitness apps or food recording apps that the user has used in the past to obtain detailed dietary and exercise history. Data can be linked using an API. This allows for more detailed analysis based on past dietary and exercise history. For example, it can identify the user's favorite foods and foods to avoid based on the past dietary history and reflect this in meal plans. It can also suggest meal plans based on the user's exercise history according to their activity level.

[0052] The user information input unit can monitor the user's lifestyle habits and daily activity level using sensors to improve the accuracy of the input information. For example, the daily activity level can be monitored using sensors installed in the user's smartphone or wearable device. Data such as the number of steps taken, distance traveled, and sleep time can be collected. By monitoring the lifestyle habits and activity level, the accuracy of the input information can be improved. For example, a meal plan incorporating light exercise can be suggested to a user with a low activity level. Also, a menu using ingredients with a relaxing effect can be suggested to a user who sleeps less.

[0053] The user information input unit uses voice recognition technology to input user information, reducing the effort required. For example, voice recognition technology can be introduced so that users can input information by voice. What the user says can be converted into text and automatically input into the app. In this way, using voice recognition technology can reduce the effort required to input user information. For example, users can input their dietary preferences and allergy information by voice. Users can also input past health checkup results by voice. This simplifies the input of user information and reduces input errors.

[0054] The user information input unit can also input health information of the user's family and friends to provide a more comprehensive meal plan. For example, a function for inputting health information of the user's family and friends can be added to provide a more comprehensive meal plan. Allergy information and dietary preferences of all family members can be taken into consideration. This allows a more comprehensive meal plan to be provided by taking into account the health information of family and friends. For example, it is possible to ensure that all family members can enjoy the same menu. It is also possible to suggest plans for enjoying meals with friends. This makes it possible to support health management through meals with family and friends.

[0055] The meal plan proposal unit can provide plans that incorporate seasonal ingredients, taking into account the season or local ingredients. For example, it can propose meal plans that incorporate seasonal ingredients, taking into account the season or local ingredients. In spring, it can provide menus using asparagus and strawberries, and in autumn, it can provide menus using pumpkins and apples. This makes it possible to provide highly nutritious meal plans by incorporating seasonal and local ingredients. For example, it can propose a salad using tomatoes and cucumbers in summer, and a warm soup using root vegetables in winter. This makes it possible to provide meal plans that take advantage of the characteristics of the season and the region.

[0056] The meal plan suggestion unit can provide a meal plan that takes into account optimal nutritional balance based on the user's genetic information. For example, it can analyze the user's genetic information and suggest a meal plan that takes into account optimal nutritional balance. It can provide a menu that includes nutrients that correspond to specific gene mutations. This makes it possible to optimize individual nutritional balance by suggesting a meal plan based on genetic information. For example, for a user who has genetically poor absorption of vitamin D, it can suggest a menu that uses ingredients that are rich in vitamin D. Furthermore, for a user who is genetically lactose intolerant, it can suggest a menu that does not use dairy products. This makes it possible to optimize individual nutritional balance based on genetic information.

[0057] The meal plan suggestion unit can integrate an exercise plan into a meal plan to provide comprehensive health management. For example, a meal plan can be combined with an exercise plan to provide comprehensive health management. An exercise menu tailored to the contents of the meal can be suggested. This allows for comprehensive health management by combining a meal plan and an exercise plan. For example, it can suggest the amount of exercise based on calorie intake. It can also suggest exercises that are effective when performed after ingesting specific nutrients. This allows for comprehensive health management that combines diet and exercise.

[0058] The meal plan suggestion unit allows a user to share their meal plan with other users and obtain feedback from the community. For example, a function is provided that allows a user to share their meal plan with other users and obtain feedback from the community. A meal plan can be posted to a social networking site and receive comments and advice from other users. By sharing a meal plan, the user can obtain feedback from the community and improve the plan. For example, a menu incorporating new ingredients can be suggested based on advice from other users. The meal plan can also be improved by taking into account the success stories of other users. This makes it possible to provide more effective meal plans by utilizing feedback from the community.

[0059] The meal plan adjustment unit can acquire biometric data such as the user's blood glucose level or blood pressure in real time and adjust the meal plan based on that. For example, the unit acquires biometric data such as the user's blood glucose level or blood pressure in real time and adjusts the meal plan based on that. If the blood glucose level is high, low GI foods can be suggested. Also, if the blood pressure is high, low-salt menus can be suggested. In this way, by acquiring biometric data in real time and adjusting the meal plan based on that, it is possible to provide an optimal meal plan according to the user's health condition. For example, it is possible to suggest menus using ingredients that are high in dietary fiber to prevent a sudden rise in blood glucose levels. It is also possible to suggest menus using ingredients that are high in potassium, which has the effect of lowering blood pressure. This makes it possible to adjust the meal plan according to the user's health condition.

[0060] The meal plan adjustment unit can analyze the user's meal history and make optimal adjustments based on past successes or failures. For example, the unit analyzes the user's meal history and makes optimal adjustments based on past successes or failures. Meal plans that have led to weight gain in the past can be used as reference. Also, meal plans that have led to weight loss in the past can be used as reference. In this way, by analyzing the past meal history and making optimal adjustments based on successes or failures, it is possible to provide the user with an optimal meal plan. For example, it is possible to exclude ingredients that have caused weight gain in the past and suggest alternative ingredients. Also, it is possible to suggest menus using similar ingredients based on past successes in weight loss. This makes it possible to adjust an optimal meal plan by utilizing the user's past meal history.

[0061] The meal plan adjustment unit can coordinate with the meal plans of the user's family or friends to make adjustments for enjoying a meal together. For example, it can coordinate with the meal plans of the user's family and friends to make adjustments for enjoying a meal together. It can ensure that all family members can enjoy the same menu. It can also suggest plans for enjoying a meal together with friends. This allows for enjoying a meal together by coordinating the meal plan with family and friends. For example, it can suggest a menu that takes into account allergy information and food preferences of all family members. It can also suggest a party menu that can be enjoyed with friends. This can support health management through meals with family and friends.

[0062] The customized recipe provider can provide optimal substitute ingredients based on the user's ingredient preferences and allergy information. For example, it can suggest optimal substitute ingredients based on the user's ingredient preferences and allergy information. For a user with a dairy allergy, it can suggest recipes that do not use dairy products. Also, for a user with a gluten allergy, it can suggest recipes that use gluten-free ingredients. This allows users to enjoy meals with peace of mind by suggesting substitute ingredients that take into account the user's ingredient preferences and allergy information. For example, it can suggest recipes that use almond milk instead of dairy products. Also, it can suggest recipes that use rice flour instead of wheat flour. This makes it possible to provide recipes that take into account the user's ingredient preferences and allergy information.

[0063] The customized recipe providing unit can suggest easy recipes taking into account the user's cooking skills and kitchen facilities. For example, it can provide easy recipes taking into account the user's cooking skills and kitchen facilities. It can suggest simple recipes for beginners and menus that can be cooked in a microwave. By providing recipes that take into account the user's cooking skills and kitchen facilities, anyone can enjoy meals that are easy to make. For example, it can suggest salads that can be made without using a knife and pasta that can be cooked in a microwave. It can also suggest recipes with short cooking times. This makes it possible to provide recipes that suit the user's cooking skills and kitchen facilities.

[0064] The customized recipe providing unit can combine elements of exercise or relaxation into recipes to provide comprehensive health management. For example, by combining elements of exercise and relaxation into recipes, comprehensive health management can be provided. Stretching or yoga to be done after meals can be suggested. Herbal tea with a relaxing effect can also be provided. By combining elements of exercise and relaxation into recipes, comprehensive health management is possible. For example, simple stretching to be done after meals or yoga poses with a relaxing effect can be suggested. Deep breathing or meditation to be done before meals can also be suggested. This makes it possible to provide comprehensive health management that combines diet with exercise and relaxation.

[0065] The customized recipe providing unit allows a user to share their recipes with other users and obtain feedback within the community. For example, a function is provided that allows a user to share their recipes with other users and obtain feedback within the community. A user can post a recipe on a social networking site and receive comments and advice from other users. This allows the user to share a recipe and obtain feedback within the community to improve the recipe. For example, a menu incorporating new ingredients can be suggested based on advice from other users. Also, a recipe can be improved by referring to the success stories of other users. This makes it possible to utilize feedback within the community to provide more effective recipes.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The user information input unit inputs detailed information about the user's constitution, food preferences, and health condition. For example, the user inputs their height, weight, whether they have any allergies, their favorite and least favorite foods, and the results of past health checkups. Step 2: The meal plan suggestion unit proposes a meal plan based on the information entered by the user information input unit. For example, it proposes a meal plan that is easy to digest, high in calories, and nutritious. The generation AI generates the meal plan using a text generation AI (e.g., LLM). Step 3: The meal plan adjustment unit adjusts the meal plan proposed by the meal plan proposal unit based on the user's physical condition and dietary reactions. For example, if the user has an allergic reaction to a particular ingredient, the unit excludes that ingredient and suggests an alternative ingredient.

[0068] (Example 2) A nutrition management system according to an embodiment of the present invention is a system that provides an individually customized meal plan based on detailed information such as a user's constitution, food preferences, health status, etc. This allows the nutrition management system to propose and adjust meal plans according to the user's individual needs, provide customized recipes, track progress, and maintain motivation.

[0069] A nutrition management system according to an embodiment includes a user information input unit, a meal plan proposal unit, and a meal plan adjustment unit. The user information input unit inputs detailed information about the user's constitution, dietary preferences, and health condition. For example, the user inputs information such as height, weight, whether or not they have allergies, favorite and disliked foods, and past health checkup results. The meal plan proposal unit proposes a meal plan based on the information input by the user information input unit. For example, it proposes a meal plan that is easy to digest, high in calories, and nutritious. The generation AI generates a meal plan using a text generation AI (e.g., LLM). The meal plan adjustment unit adjusts the meal plan proposed by the meal plan proposal unit based on the user's physical condition and dietary reactions. For example, if the user has an allergic reaction to a particular ingredient, that ingredient is excluded and alternative ingredients are suggested. This makes it possible to propose and adjust meal plans according to the user's individual needs.

[0070] The user information input unit can estimate the user's emotional state in real time and provide an interface that reduces stress and anxiety during input. For example, when the user inputs information, the user information input unit uses a camera or microphone to analyze facial expressions and voice tone and estimate the emotional state in real time. For example, if the user is feeling stressed, relaxing music or an encouraging message can be displayed. This takes the user's emotional state into consideration and reduces stress and anxiety during input.

[0071] The user information input unit automatically imports the user's past dietary and exercise history, enabling analysis based on more detailed information. For example, the user information input unit automatically imports data from fitness apps or food recording apps that the user has used in the past, and acquires detailed dietary and exercise histories. For example, the data can be linked using an API. This enables more detailed analysis based on the user's past dietary and exercise histories.

[0072] The user information input unit can improve the accuracy of the input information by monitoring the user's lifestyle habits and daily activity level using a sensor. The user information input unit monitors the daily activity level using, for example, a sensor installed in the user's smartphone or wearable device. For example, data such as the number of steps taken, distance traveled, and sleep time is collected. In this way, the accuracy of the input information is improved by monitoring the lifestyle habits and activity level.

[0073] The user information input unit uses voice recognition technology to input user information, thereby reducing the effort required. The user information input unit, for example, introduces voice recognition technology so that the user can input information by voice. For example, what the user says is converted into text and automatically input into the app. In this way, the use of voice recognition technology can reduce the effort required to input user information.

[0074] The user information input unit can also input health information of the user's family and friends to provide a more comprehensive meal plan. For example, the user information input unit can add a function that allows the user to input health information of the user's family and friends to provide a more comprehensive meal plan. For example, allergy information and dietary preferences of all family members can be taken into account. This allows a more comprehensive meal plan to be provided by taking into account the health information of family and friends.

[0075] The user information input unit can use the emotion estimation function to analyze the emotion of the user when entering information and provide positive feedback. For example, when the user enters information, the user information input unit uses the emotion estimation function to analyze facial expressions and voice tones to analyze the emotion. For example, if the user has positive emotions, an encouraging message is displayed. In this way, by analyzing the user's emotions and providing positive feedback, stress and anxiety during entry can be reduced.

[0076] The meal plan suggestion unit can propose a meal plan aimed at reducing stress or improving mood, taking into account the user's emotional state. The meal plan suggestion unit, for example, analyzes the user's emotional state and proposes a meal plan aimed at reducing stress or improving mood. For example, it provides a menu using ingredients that have a relaxing effect. In this way, by proposing a meal plan that takes into account the user's emotional state, stress reduction and mood improvement can be expected.

[0077] The meal plan proposal unit can provide a plan that incorporates seasonal ingredients, taking into account the season or local ingredients. For example, the meal plan proposal unit can provide a meal plan that incorporates seasonal ingredients, taking into account the season or local ingredients. For example, in spring, it can provide a menu that uses asparagus and strawberries, and in autumn, it can provide a menu that uses pumpkin and apples. This makes it possible to provide a nutritious meal plan by incorporating seasonal and local ingredients.

[0078] The meal plan proposal unit can provide a meal plan that takes into consideration optimal nutritional balance based on the user's genetic information. The meal plan proposal unit, for example, analyzes the user's genetic information and proposes a meal plan that takes into consideration optimal nutritional balance. For example, it provides a menu that includes nutrients corresponding to specific gene mutations. In this way, by proposing a meal plan based on genetic information, individual nutritional balance can be optimized.

[0079] The meal plan proposal unit can integrate an exercise plan into a meal plan to provide comprehensive health management. The meal plan proposal unit, for example, combines an exercise plan with a meal plan to provide comprehensive health management. For example, it proposes an exercise menu that matches the contents of the meal. In this way, by combining a meal plan and an exercise plan, comprehensive health management is possible.

[0080] The meal plan suggestion unit allows a user to share their meal plan with other users and receive feedback from the community. The meal plan suggestion unit provides, for example, a function for sharing a user's meal plan with other users and receiving feedback from the community. For example, the user can post a meal plan on a social networking site and receive comments and advice from other users. By sharing a meal plan, the user can receive feedback from the community and improve the plan.

[0081] The meal plan proposal unit uses the emotion estimation function to analyze how the user feels about the meal plan and use the analysis to improve the plan. For example, the meal plan proposal unit uses the emotion estimation function to analyze how the user feels about the meal plan. For example, it analyzes whether the user is satisfied with the plan. In this way, by using the emotion estimation function, it is possible to improve the meal plan taking the user's emotions into consideration.

[0082] The meal plan adjustment unit can monitor the user's emotional state in real time and adjust the meal plan to reduce stress or anxiety. The meal plan adjustment unit can, for example, monitor the user's emotional state in real time and adjust the meal plan to reduce stress or anxiety. For example, it can add ingredients that have a relaxing effect. This allows the emotional state to be monitored in real time and the meal plan to reduce stress or anxiety.

[0083] The meal plan adjustment unit can acquire biometric data such as the user's blood glucose level or blood pressure in real time and adjust the meal plan based on that. The meal plan adjustment unit can acquire biometric data such as the user's blood glucose level or blood pressure in real time and adjust the meal plan based on that. For example, if the blood glucose level is high, low GI foods can be suggested. In this way, by acquiring biometric data in real time and adjusting the meal plan based on that, it is possible to provide an optimal meal plan according to the user's health condition.

[0084] The meal plan adjustment unit can analyze the user's meal history and make optimal adjustments based on past successes and failures. The meal plan adjustment unit, for example, analyzes the user's meal history and makes optimal adjustments based on past successes and failures. For example, it refers to meal plans that have led to weight gain in the past. In this way, by analyzing the user's past meal history and making optimal adjustments based on past successes and failures, it is possible to provide the user with an optimal meal plan.

[0085] The meal plan adjustment unit can coordinate with the meal plans of the user's family or friends to make adjustments for enjoying a meal together. The meal plan adjustment unit, for example, coordinates with the meal plans of the user's family or friends to make adjustments for enjoying a meal together. For example, it can make it so that all family members can enjoy the same menu. This allows the user to enjoy a meal together by coordinating the meal plan with family and friends.

[0086] The meal plan adjustment unit uses the emotion estimation function to analyze how the user feels about the meal plan adjustment, thereby improving the accuracy of the adjustment. The meal plan adjustment unit, for example, uses the emotion estimation function to analyze how the user feels about the meal plan adjustment. For example, it analyzes whether the user is satisfied. In this way, by using the emotion estimation function, it is possible to adjust the meal plan taking the user's emotions into consideration.

[0087] The customized recipe providing unit can consider the emotional state of the user and suggest recipes to improve the user's mood. The customized recipe providing unit, for example, analyzes the user's emotional state and suggests recipes to improve the user's mood. For example, it suggests a menu using ingredients that have a relaxing effect. In this way, the user's mood can be improved by providing recipes that consider the user's emotional state.

[0088] The customized recipe provider can provide optimal substitute ingredients based on the user's ingredient preferences and allergy information. The customized recipe provider, for example, suggests optimal substitute ingredients based on the user's ingredient preferences and allergy information. For example, a user with a dairy allergy can be suggested a recipe that does not use dairy products. This allows the user to enjoy a meal without worry by suggesting substitute ingredients that take into account the user's ingredient preferences and allergy information.

[0089] The customized recipe providing unit can propose easy-to-make recipes taking into consideration the user's cooking skills and kitchen facilities. The customized recipe providing unit proposes easy-to-make recipes taking into consideration the user's cooking skills and kitchen facilities, for example. For example, it proposes easy recipes for beginners and menus that can be cooked in a microwave. By providing recipes that take into consideration the user's cooking skills and kitchen facilities, anyone can enjoy meals that are easy to make.

[0090] The customized recipe providing unit can combine elements of exercise or relaxation with recipes to provide comprehensive health management. The customized recipe providing unit can, for example, combine elements of exercise or relaxation with recipes to provide comprehensive health management. For example, it can suggest stretching or yoga to do after meals. In this way, by combining elements of exercise or relaxation with recipes, comprehensive health management becomes possible.

[0091] The customized recipe providing unit allows a user to share their recipes with other users and receive feedback from the community. The customized recipe providing unit provides, for example, a function for sharing a user's recipes with other users and receiving feedback from the community. For example, a user can post a recipe on a social networking site and receive comments and advice from other users. This allows a user to share a recipe and receive feedback from the community to improve the recipe.

[0092] The customized recipe providing unit uses the emotion estimation function to analyze how the user feels about the recipe, and can use this information to improve the recipe. For example, the customized recipe providing unit uses the emotion estimation function to analyze how the user feels about the recipe. For example, it analyzes whether the user is satisfied with the recipe. In this way, by using the emotion estimation function, it is possible to improve the recipe by taking the user's emotions into consideration.

[0093] The progress tracking unit can monitor the user's emotional state in real time and suggest feedback to maintain motivation. For example, the progress tracking unit can monitor the user's emotional state in real time and provide feedback to maintain motivation. For example, if the user is feeling down, an encouraging message can be displayed. In this way, the emotional state can be monitored in real time and feedback to maintain motivation can be provided to support the user in achieving their goals.

[0094] The progress tracking unit can visualize the user's progress data to enable intuitive understanding in the form of a graph or chart. The progress tracking unit, for example, visualizes the user's progress data to enable intuitive understanding in the form of a graph or chart. For example, weight gain or calorie intake may be displayed in a line graph. In this way, visualizing the progress data makes it easier for the user to intuitively understand.

[0095] The progress tracking unit may implement a system that provides rewards or incentives according to the user's achievement of a goal. The progress tracking unit may implement a system that provides rewards or incentives according to the user's achievement of a goal. For example, a system may be provided that allows a user to earn a badge when they reach their target weight. This can increase the user's motivation by providing rewards or incentives according to the achievement of their goal.

[0096] The progress tracking unit allows users to share their progress data with other users, encouraging competition and cooperation within the community. The progress tracking unit provides, for example, a function for sharing progress data with other users, encouraging competition and cooperation within the community. For example, users can compare their progress with friends, enhancing their competitive spirit. This allows users to share their progress data, encouraging competition and cooperation within the community and increasing their motivation.

[0097] The progress tracking unit can provide advice from a personal trainer or a nutritionist according to the user's progress. The progress tracking unit, for example, implements a system that provides advice from a personal trainer or a nutritionist according to the user's progress. For example, the progress tracking unit receives advice from an expert based on the progress data. This allows the user to support their health management by providing advice from an expert according to their progress.

[0098] The progress tracking unit can use the emotion estimation function to analyze how the user feels about the progress and provide feedback to maintain motivation. The progress tracking unit, for example, uses the emotion estimation function to analyze how the user feels about the progress. For example, it analyzes whether the user is satisfied with the progress. In this way, by using the emotion estimation function, it is possible to provide feedback that takes the user's emotions into consideration and maintain motivation.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The user information input unit can input information about the user's living environment. For example, the climate and environment of the area where the user lives, and the type of residence (apartment, detached house, etc.) can be input. This makes it possible to propose meal plans that suit the user's living environment. For example, it is possible to propose menus that use ingredients that warm the body to a user living in a cold region. It is also possible to propose menus that are easy to prepare to a user living in an urban area. This makes it possible to provide meal plans that suit the user's living environment.

[0101] The user information input unit can provide an interface that estimates the user's emotional state and reduces stress and anxiety during input. For example, when the user inputs information, a camera or microphone can be used to analyze facial expressions and voice tone to estimate the user's emotional state in real time. If the user is feeling stressed, relaxing music or an encouraging message can be displayed. If the user is feeling anxious, simple guidance or help messages can be displayed. This allows the user's emotional state to be taken into consideration and stress and anxiety during input to be reduced.

[0102] The user information input unit can automatically import the user's past dietary and exercise history and perform analysis based on more detailed information. For example, it can automatically import data from fitness apps or food recording apps that the user has used in the past to obtain detailed dietary and exercise history. Data can be linked using an API. This allows for more detailed analysis based on past dietary and exercise history. For example, it can identify the user's favorite foods and foods to avoid based on the past dietary history and reflect this in meal plans. It can also suggest meal plans based on the user's exercise history according to their activity level.

[0103] The user information input unit can monitor the user's lifestyle habits and daily activity level using sensors to improve the accuracy of the input information. For example, the daily activity level can be monitored using sensors installed in the user's smartphone or wearable device. Data such as the number of steps taken, distance traveled, and sleep time can be collected. By monitoring the lifestyle habits and activity level, the accuracy of the input information can be improved. For example, a meal plan incorporating light exercise can be suggested to a user with a low activity level. Also, a menu using ingredients with a relaxing effect can be suggested to a user who sleeps less.

[0104] The user information input unit uses voice recognition technology to input user information, reducing the effort required. For example, voice recognition technology can be introduced so that users can input information by voice. What the user says can be converted into text and automatically input into the app. In this way, using voice recognition technology can reduce the effort required to input user information. For example, users can input their dietary preferences and allergy information by voice. Users can also input past health checkup results by voice. This simplifies the input of user information and reduces input errors.

[0105] The user information input unit can also input health information of the user's family and friends to provide a more comprehensive meal plan. For example, a function for inputting health information of the user's family and friends can be added to provide a more comprehensive meal plan. Allergy information and dietary preferences of all family members can be taken into consideration. This allows a more comprehensive meal plan to be provided by taking into account the health information of family and friends. For example, it is possible to ensure that all family members can enjoy the same menu. It is also possible to suggest plans for enjoying meals with friends. This makes it possible to support health management through meals with family and friends.

[0106] The user information input unit can use the emotion estimation function to analyze the emotions of the user when entering information and provide positive feedback. For example, when the user enters information, the emotion estimation function is used to analyze facial expressions and voice tone to analyze the emotions. If the user has positive emotions, an encouraging message can be displayed. Also, if the user has negative emotions, relaxing music or advice on how to refresh can be provided. In this way, by analyzing the user's emotions and providing positive feedback, stress and anxiety during input can be reduced.

[0107] The meal plan suggestion unit can propose a meal plan aimed at reducing stress or improving mood, taking into account the user's emotional state. For example, it can analyze the user's emotional state and propose a meal plan aimed at reducing stress or improving mood. It can provide a menu using ingredients with a relaxing effect. It can also propose a menu using ingredients rich in vitamins and minerals to improve mood. By doing so, it is possible to propose a meal plan that takes the user's emotional state into consideration, which can be expected to reduce stress and improve mood. For example, if the user is feeling stressed, it can propose a menu using chamomile tea or dark chocolate. It can also propose a menu using oranges or kiwis, which are rich in vitamin C, if the user wants to improve their mood.

[0108] The meal plan proposal unit can provide plans that incorporate seasonal ingredients, taking into account the season or local ingredients. For example, it can propose meal plans that incorporate seasonal ingredients, taking into account the season or local ingredients. In spring, it can provide menus using asparagus and strawberries, and in autumn, it can provide menus using pumpkins and apples. This makes it possible to provide highly nutritious meal plans by incorporating seasonal and local ingredients. For example, it can propose a salad using tomatoes and cucumbers in summer, and a warm soup using root vegetables in winter. This makes it possible to provide meal plans that take advantage of the characteristics of the season and the region.

[0109] The meal plan suggestion unit can provide a meal plan that takes into account optimal nutritional balance based on the user's genetic information. For example, it can analyze the user's genetic information and suggest a meal plan that takes into account optimal nutritional balance. It can provide a menu that includes nutrients that correspond to specific gene mutations. This makes it possible to optimize individual nutritional balance by suggesting a meal plan based on genetic information. For example, for a user who has genetically poor absorption of vitamin D, it can suggest a menu that uses ingredients that are rich in vitamin D. Furthermore, for a user who is genetically lactose intolerant, it can suggest a menu that does not use dairy products. This makes it possible to optimize individual nutritional balance based on genetic information.

[0110] The meal plan suggestion unit can integrate an exercise plan into a meal plan to provide comprehensive health management. For example, a meal plan can be combined with an exercise plan to provide comprehensive health management. An exercise menu tailored to the contents of the meal can be suggested. This allows for comprehensive health management by combining a meal plan and an exercise plan. For example, it can suggest the amount of exercise based on calorie intake. It can also suggest exercises that are effective when performed after ingesting specific nutrients. This allows for comprehensive health management that combines diet and exercise.

[0111] The meal plan suggestion unit allows a user to share their meal plan with other users and obtain feedback from the community. For example, a function is provided that allows a user to share their meal plan with other users and obtain feedback from the community. A meal plan can be posted to a social networking site and receive comments and advice from other users. By sharing a meal plan, the user can obtain feedback from the community and improve the plan. For example, a menu incorporating new ingredients can be suggested based on advice from other users. The meal plan can also be improved by taking into account the success stories of other users. This makes it possible to provide more effective meal plans by utilizing feedback from the community.

[0112] The meal plan suggestion unit can use the emotion estimation function to analyze the user's emotions toward the meal plan and use this information to improve the plan. For example, the emotion estimation function can be used to analyze the user's emotions toward the meal plan. It can analyze whether the user is satisfied with the plan. If the user is dissatisfied, the cause can be identified and improvements can be proposed. This makes it possible to improve the meal plan by taking the user's emotions into consideration. For example, if the user has negative emotions toward a particular ingredient, it can exclude that ingredient and suggest alternative ingredients. If the user has positive emotions toward a particular menu item, it can frequently suggest that menu item. This makes it possible to improve the meal plan by taking the user's emotions into consideration.

[0113] The meal plan adjustment unit can monitor the user's emotional state in real time and adjust the meal plan to reduce stress or anxiety. For example, the unit can monitor the user's emotional state in real time and adjust the meal plan to reduce stress or anxiety. Ingredients with a relaxing effect can be added. Furthermore, if the user is feeling anxious, a menu using ingredients with a calming effect can be suggested. This makes it possible to monitor the emotional state in real time and adjust the meal plan to reduce stress and anxiety. For example, if the user is feeling stressed, a menu using chamomile tea or dark chocolate can be suggested. Furthermore, if the user is feeling anxious, a menu using herbal tea or avocado with a relaxing effect can be suggested. This makes it possible to adjust the meal plan taking the user's emotional state into consideration.

[0114] The meal plan adjustment unit can acquire biometric data such as the user's blood glucose level or blood pressure in real time and adjust the meal plan based on that. For example, the unit acquires biometric data such as the user's blood glucose level or blood pressure in real time and adjusts the meal plan based on that. If the blood glucose level is high, low GI foods can be suggested. Also, if the blood pressure is high, low-salt menus can be suggested. In this way, by acquiring biometric data in real time and adjusting the meal plan based on that, it is possible to provide an optimal meal plan according to the user's health condition. For example, it is possible to suggest menus using ingredients that are high in dietary fiber to prevent a sudden rise in blood glucose levels. It is also possible to suggest menus using ingredients that are high in potassium, which has the effect of lowering blood pressure. This makes it possible to adjust the meal plan according to the user's health condition.

[0115] The meal plan adjustment unit can analyze the user's meal history and make optimal adjustments based on past successes or failures. For example, the unit analyzes the user's meal history and makes optimal adjustments based on past successes or failures. Meal plans that have led to weight gain in the past can be used as reference. Also, meal plans that have led to weight loss in the past can be used as reference. In this way, by analyzing the past meal history and making optimal adjustments based on successes or failures, it is possible to provide the user with an optimal meal plan. For example, it is possible to exclude ingredients that have caused weight gain in the past and suggest alternative ingredients. Also, it is possible to suggest menus using similar ingredients based on past successes in weight loss. This makes it possible to adjust an optimal meal plan by utilizing the user's past meal history.

[0116] The meal plan adjustment unit can coordinate with the meal plans of the user's family or friends to make adjustments for enjoying a meal together. For example, it can coordinate with the meal plans of the user's family and friends to make adjustments for enjoying a meal together. It can ensure that all family members can enjoy the same menu. It can also suggest plans for enjoying a meal together with friends. This allows for enjoying a meal together by coordinating the meal plan with family and friends. For example, it can suggest a menu that takes into account allergy information and food preferences of all family members. It can also suggest a party menu that can be enjoyed with friends. This can support health management through meals with family and friends.

[0117] The meal plan adjustment unit can use the emotion estimation function to analyze how the user feels about the meal plan adjustment and improve the accuracy of the adjustment. For example, the emotion estimation function can be used to analyze how the user feels about the meal plan adjustment. It can analyze whether the user is satisfied. If the user is dissatisfied, the cause can be identified and a remedy can be proposed. This makes it possible to adjust the meal plan while taking the user's emotions into consideration. For example, if the user has negative emotions toward a particular ingredient, it can exclude that ingredient and suggest an alternative ingredient. If the user has positive emotions toward a particular menu item, it can frequently suggest that menu item. This makes it possible to adjust the meal plan while taking the user's emotions into consideration.

[0118] The customized recipe providing unit can consider the user's emotional state and suggest recipes to improve their mood. For example, it can analyze the user's emotional state and provide recipes to improve their mood. It can suggest menus using ingredients with a relaxing effect. It can also suggest menus using ingredients rich in vitamins and minerals that improve mood. This makes it possible to improve mood by providing recipes that consider the user's emotional state. For example, if the user is feeling stressed, it can suggest menus using chamomile tea or dark chocolate. Also, if the user wants to improve their mood, it can suggest menus using oranges or kiwis, which are rich in vitamin C. This makes it possible to provide recipes that consider the user's emotional state.

[0119] The customized recipe provider can provide optimal substitute ingredients based on the user's ingredient preferences and allergy information. For example, it can suggest optimal substitute ingredients based on the user's ingredient preferences and allergy information. For a user with a dairy allergy, it can suggest recipes that do not use dairy products. Also, for a user with a gluten allergy, it can suggest recipes that use gluten-free ingredients. This allows users to enjoy meals with peace of mind by suggesting substitute ingredients that take into account the user's ingredient preferences and allergy information. For example, it can suggest recipes that use almond milk instead of dairy products. Also, it can suggest recipes that use rice flour instead of wheat flour. This makes it possible to provide recipes that take into account the user's ingredient preferences and allergy information.

[0120] The customized recipe providing unit can suggest easy recipes taking into account the user's cooking skills and kitchen facilities. For example, it can provide easy recipes taking into account the user's cooking skills and kitchen facilities. It can suggest simple recipes for beginners and menus that can be cooked in a microwave. By providing recipes that take into account the user's cooking skills and kitchen facilities, anyone can enjoy meals that are easy to make. For example, it can suggest salads that can be made without using a knife and pasta that can be cooked in a microwave. It can also suggest recipes with short cooking times. This makes it possible to provide recipes that suit the user's cooking skills and kitchen facilities.

[0121] The customized recipe providing unit can combine elements of exercise or relaxation into recipes to provide comprehensive health management. For example, by combining elements of exercise and relaxation into recipes, comprehensive health management can be provided. Stretching or yoga to be done after meals can be suggested. Herbal tea with a relaxing effect can also be provided. By combining elements of exercise and relaxation into recipes, comprehensive health management is possible. For example, simple stretching to be done after meals or yoga poses with a relaxing effect can be suggested. Deep breathing or meditation to be done before meals can also be suggested. This makes it possible to provide comprehensive health management that combines diet with exercise and relaxation.

[0122] The customized recipe providing unit allows a user to share their recipes with other users and obtain feedback within the community. For example, a function is provided that allows a user to share their recipes with other users and obtain feedback within the community. A user can post a recipe on a social networking site and receive comments and advice from other users. This allows the user to share a recipe and obtain feedback within the community to improve the recipe. For example, a menu incorporating new ingredients can be suggested based on advice from other users. Also, a recipe can be improved by referring to the success stories of other users. This makes it possible to utilize feedback within the community to provide more effective recipes.

[0123] The customized recipe provider uses the emotion estimation function to analyze how the user feels about a recipe and use this information to improve the recipe. For example, the emotion estimation function can be used to analyze how the user feels about the recipe. It can analyze whether the user is satisfied with the recipe. If the user is dissatisfied, the cause can be identified and improvements can be proposed. This makes it possible to improve recipes by taking the user's emotions into account. For example, if the user has negative feelings about a particular ingredient, it can exclude that ingredient and suggest alternative ingredients. Furthermore, if the user has positive feelings about a particular menu item, it can frequently suggest that menu item. This makes it possible to improve recipes by taking the user's emotions into account.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The user information input unit inputs detailed information about the user's constitution, food preferences, and health condition. For example, the user inputs their height, weight, whether they have any allergies, their favorite and least favorite foods, and the results of past health checkups. Step 2: The meal plan suggestion unit proposes a meal plan based on the information entered by the user information input unit. For example, it proposes a meal plan that is easy to digest, high in calories, and nutritious. The generation AI generates the meal plan using a text generation AI (e.g., LLM). Step 3: The meal plan adjustment unit adjusts the meal plan proposed by the meal plan proposal unit based on the user's physical condition and dietary reactions. For example, if the user has an allergic reaction to a particular ingredient, the unit excludes that ingredient and suggests an alternative ingredient.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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.

[0138] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] 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.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] 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.

[0152] 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.

[0153] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] 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.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] 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.

[0168] 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.

[0169] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0170] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0193] 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 user information input unit for inputting detailed information about the user's constitution, dietary preferences, and health condition; a meal plan proposal unit that proposes a meal plan based on the information input by the user information input unit; and a meal plan adjustment unit that adjusts the meal plan proposed by the meal plan proposal unit based on the user's physical condition and dietary response. A system characterized by:

2. The user information input unit Provides an interface that estimates a user's emotional state in real time and reduces stress or anxiety when inputting.

2. The system of claim 1.

3. The meal plan proposal unit Considering the user's emotional state, it suggests meal plans aimed at reducing stress or improving mood.

2. The system of claim 1.

4. The meal plan adjustment unit Monitors the user's emotional state in real time and adjusts meal plans to reduce stress or anxiety 2. The system of claim 1.

5. Customized recipes are provided by Considers the user's emotional state and suggests recipes to improve their mood 2. The system of claim 1.

6. The progress tracking section Monitor users' emotional state in real time and provide feedback to help them stay motivated 2. The system of claim 1.

7. The user information input unit Automatically imports a user's past diet or exercise history for more detailed analysis 2. The system of claim 1.

8. The meal plan proposal unit Provides meal plans that take into account optimal nutritional balance based on the user's genetic information 2. The system of claim 1.

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