System

Abstract

An object of a system according to an embodiment is to provide an optimal meal recipe or exercise menu based on body data of a user and a goal and to evaluate a disease risk.SOLUTION: A system according to an embodiment includes a data input unit, a recommendation unit, a schedule generation unit, and a risk evaluation unit. The data input unit inputs body data of a user and a target. The recommendation unit recommends an optimal meal recipe based on the data input by the data input unit. The schedule generation unit generates a schedule based on the meal recipe and the exercise menu recommended by the recommendation unit. The risk evaluation unit evaluates a disease risk on the basis of the data input by the data input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of providing optimal meal recipes and exercise menus for individual users, as well as assessing their risk of disease.

[0005] The system according to the embodiment aims to provide optimal meal recipes and exercise menus based on the user's physical data and goals, and to assess the risk of disease. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, a recommendation unit, a schedule generation unit, and a risk assessment unit. The data input unit inputs a user's physical data and goals. The recommendation unit recommends optimal meal recipes based on the data input by the data input unit. The schedule generation unit generates a schedule based on the meal recipes and exercise menu recommended by the recommendation unit. The risk assessment unit assesses disease risk based on the data input by the data input unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal meal recipes and exercise menus based on the user's physical data and goals, and can assess the risk of disease. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system in which a user inputs their physical data and goals, and a generating AI specifically recommends optimal meal recipes, exercise menus, and schedules, and outputs disease risks. This allows the health management system to contribute to maintaining the user's health and providing smooth medical diagnoses.

[0029] A health management system according to an embodiment includes a data input unit, a recommendation unit, a schedule generation unit, and a risk assessment unit. The data input unit inputs a user's physical data and goals. For example, height, weight, age, gender, allergy information, current health condition, etc. are input. Furthermore, goals such as weight loss, muscle building, and performance improvement in a specific sport can be input. The recommendation unit recommends optimal meal recipes based on the data input by the data input unit. For example, for a user aiming to lose weight, it suggests low-calorie, nutritionally balanced recipes. It also customizes recipes taking into account the user's allergy information and food preferences. The schedule generation unit generates a schedule based on the meal recipes and exercise menus recommended by the recommendation unit. For example, it suggests daily meal timings and exercise times. It also customizes the schedule taking into account the user's lifestyle and work schedule. The risk assessment unit assesses disease risk based on the data input by the data input unit. For example, it assesses the risk of obesity, diabetes, high blood pressure, etc., and suggests preventive measures to the user. As a result, the health management system according to the embodiment can recommend optimal meal recipes, exercise menus, and schedules based on the user's physical data and goals, and can evaluate the risk of disease.

[0030] The data input unit automatically imports the user's past health data or medical history, enabling more accurate goal setting. The data input unit, for example, automatically imports the user's past health data and medical history from electronic medical records or health management apps to grasp a detailed understanding of their current health condition. For example, goals are set based on past diagnostic results and prescription history. Furthermore, automatic import of health data eliminates the need for the user to enter data and improves data accuracy. For example, realistic goals are suggested based on past weight fluctuations and exercise history. Furthermore, through collaboration with medical institutions, the latest health data is obtained in real time and reflected in goal setting. For example, the results of regular health checkups are automatically imported and goals are updated. This enables more accurate goal setting based on past health data and medical history.

[0031] The data input unit can collect the user's lifestyle or daily activity data from a sensor in real time and dynamically update goals. The data input unit collects the user's daily activity data in real time, for example, using a sensor in a wearable device or smartphone. For example, a pedometer or heart rate monitor is used to record the amount of exercise done each day. The data input unit also analyzes the user's lifestyle based on the collected activity data and dynamically updates goals. For example, if the user continues to lack exercise, it suggests adding more exercise to the menu. The data from the sensor is also analyzed in real time to set goals that match the user's lifestyle rhythm. For example, it suggests appropriate exercise times based on sleep data. This allows the goals to be dynamically updated based on the lifestyle and daily activity data.

[0032] The data input unit can also input health data of the user's family or friends to set health goals for the entire group. The data input unit, for example, builds a system for sharing health data of the user's family and friends and setting health goals for the entire group. For example, a weight loss goal can be set for the entire family and they can work together. Common goals can also be set based on the health data of the entire group and progress can be shared. For example, friends can set exercise challenges and encourage each other. The health data of family and friends can also be imported to link individual goals with group goals. For example, meal recipes can be standardized for the entire family to promote healthy eating habits. This allows family and friends to set health goals and work together.

[0033] The data input unit inputs health data of the user's pet, allowing health management together with the pet. The data input unit, for example, inputs health data of the user's pet and builds a system for health management together with the pet. For example, it records the pet's weight and amount of exercise, and sets common health goals. It also suggests a common exercise menu for the user and the pet based on the pet's health data. For example, it sets the walking time and distance, and exercises together. It also inputs dietary data, linking dietary recipes for the user and the pet. For example, it suggests recipes that take the pet's dietary restrictions into account. This allows health management together with the pet.

[0034] The recommendation unit can suggest recipes that use seasonal ingredients based on the season or local specialties. For example, the recommendation unit suggests recipes that use seasonal ingredients, taking into consideration the season and local specialties. For example, in spring, it suggests recipes that use asparagus or strawberries. It also suggests recipes that use local ingredients based on local specialties. For example, it suggests recipes that use fresh seafood to a user in Hokkaido. It also suggests recipes that use nutritious seasonal ingredients based on seasonal ingredients. For example, it suggests recipes that use pumpkin or sweet potato in autumn. In this way, it is possible to suggest recipes that use seasonal ingredients, taking into consideration the season and local specialties.

[0035] The recommendation unit can analyze the user's past meal history and generate a variety of recipes to prevent the user from getting bored. For example, the recommendation unit can analyze the user's past meal history and suggest recipes with a wide variety to prevent the user from eating the same ingredients or dishes repeatedly. For example, it can suggest different dishes each week. It can also customize recipes based on the meal history and taking into account the user's preferences and allergy information to prevent the user from getting bored. For example, it can suggest different cooking methods and seasonings. It can also analyze the past meal history and suggest new ingredients and dishes that the user has not tried yet. For example, it can suggest recipes using exotic dishes or new ingredients. In this way, it can analyze the user's past meal history and generate a wide variety of recipes to prevent the user from getting bored.

[0036] The recommendation unit can share a user's meal recipes with other users and improve the recipes based on feedback within the community. The recommendation unit, for example, shares meal recipes created by a user within the community and improves the recipes based on feedback from other users. For example, it provides a function for accepting comments and ratings. It also builds a system in which other users can suggest improvements to shared recipes. For example, it accepts suggestions for changes to cooking methods or ingredients. It also improves the user's recipes based on recipes that are popular within the community. For example, it suggests new recipes based on highly rated recipes. This allows the user's meal recipes to be shared with other users and the recipes to be improved based on feedback within the community.

[0037] The recommendation unit can automatically add a user's meal recipe to an online shopping cart, making it easy to purchase the necessary ingredients. The recommendation unit, for example, builds a system that automatically adds ingredients for a meal recipe selected by the user to the online shopping cart. For example, it allows the ingredients needed for a recipe to be purchased all at once. It also links with online shopping sites to make it easy to purchase ingredients for a recipe selected by the user. For example, it provides a purchase link at an affiliated site. It also develops a system that checks ingredient inventory in real time and automatically adds necessary ingredients to the cart. For example, it suggests alternatives if an ingredient is out of stock. This makes it possible to automatically add a user's meal recipe to an online shopping cart, making it easy to purchase the necessary ingredients.

[0038] The recommendation unit can analyze the user's exercise history and generate an optimal menu based on past achievements or failures. The recommendation unit, for example, analyzes the user's exercise history and suggests an optimal exercise menu based on past achievements and failures. For example, it re-suggests training menus that were successful in the past. Also, based on the exercise history, it generates a menu that takes into account the user's physical fitness level and exercise habits. For example, it suggests a menu that gradually increases the load based on past data. Also, it analyzes the user's exercise history and adjusts it to avoid menus that have failed. For example, it suggests a menu that avoids exercises that have caused injuries in the past. In this way, it is possible to analyze the user's exercise history and generate an optimal menu based on past achievements and failures.

[0039] The recommendation unit can monitor the user's physical condition or fatigue level in real time and dynamically adjust the exercise menu. The recommendation unit, for example, uses a wearable device to monitor the user's physical condition and fatigue level in real time and dynamically adjust the exercise menu. For example, the recommendation unit adjusts exercise intensity based on heart rate and oxygen saturation. The recommendation unit also evaluates the user's fatigue level based on physical condition data and suggests appropriate rest periods. For example, if fatigue is accumulating, it suggests light exercise or stretching. The recommendation unit also analyzes real-time physical condition data and builds a system that dynamically changes the exercise menu. For example, if the user's physical condition worsens during exercise, it suggests stopping the exercise. This makes it possible to monitor the user's physical condition and fatigue level in real time and dynamically adjust the exercise menu.

[0040] The recommendation unit can share a user's exercise menu with other users and increase motivation through competition or cooperation. The recommendation unit can, for example, share a user's exercise menu within a community and increase motivation through competition or cooperation with other users. For example, an exercise challenge can be set and users can compete for achievement. A system can also be built in which other users can provide feedback and advice on the shared exercise menu. For example, exercise tips and areas for improvement can be shared. Furthermore, by sharing exercise menus within the community, an environment can be created in which users can encourage each other. For example, exercise progress can be shared and messages of encouragement can be sent to each other. This allows a user's exercise menu to be shared with other users and motivation can be increased through competition or cooperation.

[0041] The recommendation unit can visualize the user's exercise menu using AR technology, making it easier to realize the effects of exercise. The recommendation unit, for example, uses AR technology to build a system that visualizes the user's exercise menu and makes it easier to realize the effects of exercise. For example, the exercise movements are displayed using a 3D model. AR technology can also be used to allow the user to check the correct form while exercising. For example, form can be checked in real time using a smartphone or AR glasses. Exercise progress and results can also be visualized using AR, making it easier for the user to feel a sense of accomplishment. For example, calories burned and muscle growth can be visually displayed after exercise. In this way, the user's exercise menu can be visualized using AR technology, making it easier to realize the effects of exercise.

[0042] The schedule generation unit can analyze the user's lifestyle rhythm and suggest optimal timings for eating and exercising. The schedule generation unit, for example, analyzes the user's lifestyle rhythm and suggests optimal timings for eating and exercising. For example, it sets breakfast and exercise times based on the user's sleep patterns. It also identifies time periods when the user can be efficient based on the lifestyle rhythm and suggests a schedule that matches those time periods. For example, it suggests exercising at times when concentration is strongest. It also builds a system that analyzes the user's lifestyle rhythm in real time and dynamically adjusts the schedule. For example, it adjusts meal and exercise times according to changes in the lifestyle rhythm. In this way, it is possible to analyze the user's lifestyle rhythm and suggest optimal timings for eating and exercising.

[0043] The schedule generation unit can generate a reasonable schedule in cooperation with the user's work or home schedule. The schedule generation unit, for example, builds a system that incorporates the user's work and home schedules and generates a reasonable schedule. For example, it sets exercise and meal times taking into account work meetings and home events. It also adjusts the schedule in cooperation with the work and home schedules so that the user does not feel stressed. For example, it sets time for relaxation before and after important meetings. It also develops a system that analyzes the user's schedule in real time and dynamically adjusts the schedule. For example, it automatically updates the schedule in response to sudden changes in plans. This makes it possible to generate a reasonable schedule in cooperation with the user's work and home schedules.

[0044] The schedule generation unit allows a user to share their schedule with other users and jointly manage their health. The schedule generation unit, for example, builds a system for sharing a user's schedule within a community and jointly managing their health with other users. For example, making exercise and meal plans together with friends and family. A system is also developed in which other users provide feedback and advice on the shared schedule. For example, improvements to the schedule and new suggestions are accepted. Furthermore, an environment is created in which users encourage each other through schedule sharing within the community. For example, they can jointly set goals and share progress. This allows a user's schedule to be shared with other users and jointly managing their health.

[0045] The schedule generation unit can link the user's schedule with a smart device and automatically send reminders or notifications. The schedule generation unit, for example, builds a system that links the user's schedule with a smart device and automatically sends reminders and notifications. For example, it notifies a smartphone of exercise and meal times. The smart device is also used to manage the user's schedule in real time and send necessary reminders. For example, it sends notifications before important appointments. A system is also developed that links the user's schedule with a smart device and dynamically adjusts the schedule. For example, it automatically updates reminders in response to changes in the schedule. This makes it possible to link the user's schedule with a smart device and automatically send reminders and notifications.

[0046] The risk assessment unit can incorporate the user's genetic information and perform disease risk assessment based on the genetic risk. The risk assessment unit, for example, incorporates the user's genetic information and builds a system that performs disease risk assessment taking genetic risk into account. For example, it calculates disease risk based on genetic test results. It also identifies specific diseases with a high genetic risk based on the genetic information and suggests preventive measures to the user. For example, it suggests diet and exercise if the user has a high genetic risk of diabetes. It also analyzes the user's genetic information in real time and dynamically assesses disease risk taking genetic risk into account. For example, it updates the risk assessment in response to updates to the genetic information. This makes it possible to incorporate the user's genetic information and perform disease risk assessment taking genetic risk into account.

[0047] The risk assessment unit can analyze the user's lifestyle habits or environmental factors in detail and provide a personalized disease risk assessment. The risk assessment unit, for example, builds a system that analyzes the user's lifestyle habits and environmental factors in detail and provides a personalized disease risk assessment. For example, it calculates disease risk based on smoking and drinking habits. It also identifies specific high-risk diseases based on lifestyle habits and environmental factors and suggests preventive measures to the user. For example, it suggests an exercise menu if a user has a high risk of heart disease due to lack of exercise. It also analyzes the user's lifestyle habits and environmental factors in real time and dynamically performs a personalized disease risk assessment. For example, it updates the risk assessment in response to changes in lifestyle habits. This makes it possible to analyze the user's lifestyle habits and environmental factors in detail and provide a personalized disease risk assessment.

[0048] The risk assessment unit can compare the user's disease risk with other users and assess the relative risk. The risk assessment unit, for example, builds a system that compares the user's disease risk with other users and assesses the relative risk. For example, it compares with users of the same age or with the same lifestyle. Furthermore, based on the relative risk assessment, it suggests specific preventive measures to the user. For example, if the risk is higher than the average risk for the same age, it suggests improving lifestyle habits. Furthermore, it visually displays the user's disease risk based on comparison data with other users. For example, it shows the relative risk using graphs or charts. This makes it possible to compare the user's disease risk with other users and assess the relative risk.

[0049] The risk assessment unit shares the user's disease risk with a medical institution, allowing the user to receive suggestions for early diagnosis or preventive measures. The risk assessment unit, for example, builds a system for sharing the user's disease risk with a medical institution and receiving suggestions for early diagnosis or preventive measures. For example, if the risk is high, the user receives advice from a medical institution. In addition, through collaboration with a medical institution, the user's disease risk data is shared and specialized diagnosis and preventive measures are provided. For example, risk assessment is performed based on the results of regular health checkups. In addition, a system is developed for sharing the user's disease risk data with a medical institution in real time and receiving suggestions for early diagnosis or preventive measures. For example, the user receives a notification from a medical institution if the risk increases. This allows the user's disease risk to be shared with a medical institution and the user to receive suggestions for early diagnosis or preventive measures.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The data input unit automatically imports the user's past health data or medical history, enabling more accurate goal setting. For example, the user's past health data and medical history can be automatically imported from electronic medical records or health management apps to gain a detailed understanding of their current health condition. For example, goals can be set based on past diagnostic results and prescription history. Automatic import of health data also eliminates the need for users to enter data and improves data accuracy. For example, realistic goals can be suggested based on past weight fluctuations and exercise history. In addition, by linking with medical institutions, the latest health data can be obtained in real time and reflected in goal setting. For example, the results of regular health checkups can be automatically imported and goals can be updated. This enables more accurate goal setting based on past health data and medical history.

[0052] The data input unit can collect the user's lifestyle or daily activity data from sensors in real time and dynamically update goals. For example, the data input unit collects the user's daily activity data in real time using sensors in a wearable device or smartphone. For example, a pedometer or heart rate monitor is used to record the amount of exercise each day. The data input unit also analyzes the user's lifestyle based on the collected activity data and dynamically updates the goals. For example, if the user continues to lack exercise, the data input unit suggests adding more exercise to the user's routine. The data input unit also analyzes the data from the sensors in real time and sets goals that match the user's lifestyle. For example, the data input unit suggests appropriate exercise times based on sleep data. This allows the goals to be dynamically updated based on the lifestyle and daily activity data.

[0053] The data input unit can also input health data of the user's family or friends, allowing the user to set health goals for the entire group. For example, a system can be built to share the health data of the user's family and friends and set health goals for the entire group. For example, a weight loss goal can be set for the whole family and they can work together. Common goals can also be set based on the health data of the entire group and progress can be shared. For example, friends can set exercise challenges and encourage each other. Health data of family and friends can also be imported to link individual goals with group goals. For example, meal recipes can be standardized for the whole family to promote healthy eating habits. This allows family and friends to set health goals and work together.

[0054] The data input unit can input health data of the user's pet and manage the health of the pet together. For example, a system can be constructed in which the user's pet's health data is input and health management is performed together with the pet. For example, the pet's weight and amount of exercise can be recorded and common health goals can be set. Also, based on the pet's health data, a common exercise menu can be proposed for the user and the pet. For example, walking time and distance can be set and the users can exercise together. Also, the pet's dietary data can be input and dietary recipes for the user and the pet can be linked. For example, recipes that take the pet's dietary restrictions into account can be proposed. This allows health management to be performed together with the pet.

[0055] The recommendation unit can suggest recipes that use seasonal ingredients based on the season or local specialties. For example, recipes that use seasonal ingredients are suggested, taking into account the season and local specialties. For example, in spring, recipes using asparagus or strawberries are suggested. Also, recipes that use local ingredients are suggested based on local specialties. For example, recipes using fresh seafood are suggested to a user in Hokkaido. Also, recipes that use nutritious seasonal ingredients are suggested based on seasonal ingredients. For example, recipes using pumpkin or sweet potato are suggested in autumn. In this way, it is possible to suggest recipes that use seasonal ingredients, taking into account the season and local specialties.

[0056] The recommendation unit can analyze the user's past meal history and generate a variety of recipes to prevent the user from getting bored. For example, it can analyze the user's past meal history and suggest recipes with a wide variety to prevent the user from repeatedly using the same ingredients or dishes. For example, it can suggest different dishes each week. It can also customize recipes based on the meal history and taking into account the user's preferences and allergy information to prevent the user from getting bored. For example, it can suggest different cooking methods and seasonings. It can also analyze the past meal history and suggest new ingredients and dishes that the user has not tried yet. For example, it can suggest recipes using exotic dishes or new ingredients. In this way, it can analyze the user's past meal history and generate a wide variety of recipes to prevent the user from getting bored.

[0057] The recommendation unit can share a user's meal recipes with other users and improve the recipes based on feedback within the community. For example, a meal recipe created by a user can be shared within the community and the recipe can be improved based on feedback from other users. For example, a function for accepting comments and ratings can be provided. A system can also be built in which other users can make improvement suggestions for shared recipes. For example, suggestions for changes to cooking methods or ingredients can be accepted. The recommendation unit can also improve the user's recipes based on recipes that are popular within the community. For example, new recipes can be suggested based on highly rated recipes. This allows a user's meal recipes to be shared with other users and the recipes can be improved based on feedback within the community.

[0058] The recommendation unit can automatically add a user's meal recipe to an online shopping cart, making it easy to purchase the necessary ingredients. For example, we will build a system that automatically adds ingredients for a meal recipe selected by the user to the online shopping cart. For example, we will make it possible to purchase all the ingredients needed for a recipe in one go. We will also link with online shopping sites to make it easy to purchase ingredients for a recipe selected by the user. For example, we will provide a purchase link at an affiliated site. We will also develop a system that checks ingredient inventory in real time and automatically adds necessary ingredients to the cart. For example, we will suggest alternatives if an ingredient is out of stock. This will allow the user's meal recipe to be automatically added to the online shopping cart, making it easy to purchase the necessary ingredients.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The data input section allows the user to input their physical data and goals, such as height, weight, age, gender, allergy information, current health condition, etc. Goals can also be input, such as weight loss, muscle building, or improving performance in a specific sport. Step 2: The recommendation unit recommends optimal meal recipes based on the data entered by the data input unit. For example, for a user who is trying to lose weight, it will suggest low-calorie, nutritionally balanced recipes. It also customizes recipes by taking into account the user's allergy information and food preferences. Step 3: The schedule generation unit generates a schedule based on the meal recipes and exercise menus recommended by the recommendation unit. For example, it suggests the timing of meals and the time of day to exercise. The schedule is customized taking into account the user's daily rhythm and work schedule. Step 4: The risk assessment unit assesses the risk of disease based on the data input by the data input unit. For example, the risk of obesity, diabetes, high blood pressure, etc. is assessed, and preventive measures are proposed to the user.

[0061] (Example 2) A health management system according to an embodiment of the present invention is a system in which a user inputs their physical data and goals, and a generating AI specifically recommends optimal meal recipes, exercise menus, and schedules, and outputs disease risks. This allows the health management system to contribute to maintaining the user's health and providing smooth medical diagnoses.

[0062] A health management system according to an embodiment includes a data input unit, a recommendation unit, a schedule generation unit, and a risk assessment unit. The data input unit inputs a user's physical data and goals. For example, height, weight, age, gender, allergy information, current health condition, etc. are input. Furthermore, goals such as weight loss, muscle building, and performance improvement in a specific sport can be input. The recommendation unit recommends optimal meal recipes based on the data input by the data input unit. For example, for a user aiming to lose weight, it suggests low-calorie, nutritionally balanced recipes. It also customizes recipes taking into account the user's allergy information and food preferences. The schedule generation unit generates a schedule based on the meal recipes and exercise menus recommended by the recommendation unit. For example, it suggests daily meal timings and exercise times. It also customizes the schedule taking into account the user's lifestyle and work schedule. The risk assessment unit assesses disease risk based on the data input by the data input unit. For example, it assesses the risk of obesity, diabetes, high blood pressure, etc., and suggests preventive measures to the user. As a result, the health management system according to the embodiment can recommend optimal meal recipes, exercise menus, and schedules based on the user's physical data and goals, and can evaluate the risk of disease.

[0063] The data input unit can estimate the user's emotional state in real time and correct the data based on the stress level and motivation at the time of input. For example, when the user inputs body data, the data input unit analyzes the user's emotional state in real time using a camera or microphone and quantifies the stress level and motivation. For example, the data input unit analyzes facial expressions and voice tone and simplifies data input if stress is high. The data input unit also evaluates the reliability of the input data based on the user's emotional state and corrects it as necessary. For example, if stress is high, the data input unit provides a function to automatically complete part of the input data. The data input process can also be flexibly adjusted taking the user's emotional state into consideration. For example, if motivation is low, an encouraging message can be displayed to encourage input. This makes it possible to improve the accuracy of data input by taking the user's emotional state into consideration.

[0064] The data input unit automatically imports the user's past health data or medical history, enabling more accurate goal setting. The data input unit, for example, automatically imports the user's past health data and medical history from electronic medical records or health management apps to grasp a detailed understanding of their current health condition. For example, goals are set based on past diagnostic results and prescription history. Furthermore, automatic import of health data eliminates the need for the user to enter data and improves data accuracy. For example, realistic goals are suggested based on past weight fluctuations and exercise history. Furthermore, through collaboration with medical institutions, the latest health data is obtained in real time and reflected in goal setting. For example, the results of regular health checkups are automatically imported and goals are updated. This enables more accurate goal setting based on past health data and medical history.

[0065] The data input unit can collect the user's lifestyle or daily activity data from a sensor in real time and dynamically update goals. The data input unit collects the user's daily activity data in real time, for example, using a sensor in a wearable device or smartphone. For example, a pedometer or heart rate monitor is used to record the amount of exercise done each day. The data input unit also analyzes the user's lifestyle based on the collected activity data and dynamically updates goals. For example, if the user continues to lack exercise, it suggests adding more exercise to the menu. The data from the sensor is also analyzed in real time to set goals that match the user's lifestyle rhythm. For example, it suggests appropriate exercise times based on sleep data. This allows the goals to be dynamically updated based on the lifestyle and daily activity data.

[0066] The data input unit can also input health data of the user's family or friends to set health goals for the entire group. The data input unit, for example, builds a system for sharing health data of the user's family and friends and setting health goals for the entire group. For example, a weight loss goal can be set for the entire family and they can work together. Common goals can also be set based on the health data of the entire group and progress can be shared. For example, friends can set exercise challenges and encourage each other. The health data of family and friends can also be imported to link individual goals with group goals. For example, meal recipes can be standardized for the entire family to promote healthy eating habits. This allows family and friends to set health goals and work together.

[0067] The data input unit inputs health data of the user's pet, allowing health management together with the pet. The data input unit, for example, inputs health data of the user's pet and builds a system for health management together with the pet. For example, it records the pet's weight and amount of exercise, and sets common health goals. It also suggests a common exercise menu for the user and the pet based on the pet's health data. For example, it sets the walking time and distance, and exercises together. It also inputs dietary data, linking dietary recipes for the user and the pet. For example, it suggests recipes that take the pet's dietary restrictions into account. This allows health management together with the pet.

[0068] The data input unit can use an emotion estimation function to analyze the user's emotions when entering data and provide positive feedback to encourage input. For example, when a user enters physical data, the data input unit uses the emotion estimation function to analyze emotions in real time and provide positive feedback. For example, an encouraging message is displayed each time input is completed. The emotion estimation function can also be used to analyze the user's emotional state when entering data and make positive suggestions if negative emotions are detected. For example, options to simplify input are presented if input is difficult. The input process can also be flexibly adjusted based on the user's emotional state and positive feedback is provided. For example, a message that makes the user feel a sense of accomplishment is displayed each time input is completed. In this way, input can be encouraged by analyzing the user's emotions and providing positive feedback.

[0069] The recommendation unit can select ingredients that contribute to stress reduction or mood improvement by taking into account the user's emotional state. The recommendation unit, for example, analyzes the user's emotional state and selects ingredients that contribute to stress reduction or mood improvement. For example, it suggests recipes that include bananas or dark chocolate, which increase serotonin. It also suggests recipes that use herbs and spices that have a relaxing effect based on the emotional state. For example, it suggests dishes that use chamomile or lavender. It also suggests recipes that use colorful ingredients that improve mood by taking into account the user's emotional state. For example, it suggests salads using colorful vegetables and fruits. In this way, it is possible to select ingredients that contribute to stress reduction or mood improvement by taking into account the user's emotional state.

[0070] The recommendation unit can suggest recipes that use seasonal ingredients based on the season or local specialties. For example, the recommendation unit suggests recipes that use seasonal ingredients, taking into consideration the season and local specialties. For example, in spring, it suggests recipes that use asparagus or strawberries. It also suggests recipes that use local ingredients based on local specialties. For example, it suggests recipes that use fresh seafood to a user in Hokkaido. It also suggests recipes that use nutritious seasonal ingredients based on seasonal ingredients. For example, it suggests recipes that use pumpkin or sweet potato in autumn. In this way, it is possible to suggest recipes that use seasonal ingredients, taking into consideration the season and local specialties.

[0071] The recommendation unit can analyze the user's past meal history and generate a variety of recipes to prevent the user from getting bored. For example, the recommendation unit can analyze the user's past meal history and suggest recipes with a wide variety to prevent the user from eating the same ingredients or dishes repeatedly. For example, it can suggest different dishes each week. It can also customize recipes based on the meal history and taking into account the user's preferences and allergy information to prevent the user from getting bored. For example, it can suggest different cooking methods and seasonings. It can also analyze the past meal history and suggest new ingredients and dishes that the user has not tried yet. For example, it can suggest recipes using exotic dishes or new ingredients. In this way, it can analyze the user's past meal history and generate a wide variety of recipes to prevent the user from getting bored.

[0072] The recommendation unit can share a user's meal recipes with other users and improve the recipes based on feedback within the community. The recommendation unit, for example, shares meal recipes created by a user within the community and improves the recipes based on feedback from other users. For example, it provides a function for accepting comments and ratings. It also builds a system in which other users can suggest improvements to shared recipes. For example, it accepts suggestions for changes to cooking methods or ingredients. It also improves the user's recipes based on recipes that are popular within the community. For example, it suggests new recipes based on highly rated recipes. This allows the user's meal recipes to be shared with other users and the recipes to be improved based on feedback within the community.

[0073] The recommendation unit can automatically add a user's meal recipe to an online shopping cart, making it easy to purchase the necessary ingredients. The recommendation unit, for example, builds a system that automatically adds ingredients for a meal recipe selected by the user to the online shopping cart. For example, it allows the ingredients needed for a recipe to be purchased all at once. It also links with online shopping sites to make it easy to purchase ingredients for a recipe selected by the user. For example, it provides a purchase link at an affiliated site. It also develops a system that checks ingredient inventory in real time and automatically adds necessary ingredients to the cart. For example, it suggests alternatives if an ingredient is out of stock. This makes it possible to automatically add a user's meal recipe to an online shopping cart, making it easy to purchase the necessary ingredients.

[0074] The recommendation unit uses the emotion estimation function to suggest recipes for the user to enjoy a meal, thereby improving meal satisfaction. The recommendation unit, for example, uses the emotion estimation function to suggest recipes for the user to enjoy a meal. For example, it analyzes the user's emotional state and suggests dishes that match the user's mood. It also customizes recipes based on the emotion estimation data to improve meal satisfaction. For example, it suggests recipes that use ingredients that have a relaxing effect. It also analyzes the user's emotional state in real time and provides feedback to improve meal satisfaction. For example, it displays positive messages while the user is eating. In this way, it is possible to use the emotion estimation function to suggest recipes for the user to enjoy a meal, thereby improving meal satisfaction.

[0075] The recommendation unit can take the user's emotional state into consideration and suggest an exercise menu to maintain motivation. The recommendation unit, for example, analyzes the user's emotional state and suggests an exercise menu to maintain motivation. For example, if the emotional score is low, it suggests light exercise. It also suggests an exercise menu that the user can enjoy based on the emotional state. For example, it suggests dance exercises set to favorite music. It also analyzes the user's emotional state in real time and provides feedback to increase motivation. For example, it displays encouraging messages during exercise. This makes it possible to suggest an exercise menu to maintain motivation taking the user's emotional state into consideration.

[0076] The recommendation unit can analyze the user's exercise history and generate an optimal menu based on past achievements or failures. The recommendation unit, for example, analyzes the user's exercise history and suggests an optimal exercise menu based on past achievements and failures. For example, it re-suggests training menus that were successful in the past. Also, based on the exercise history, it generates a menu that takes into account the user's physical fitness level and exercise habits. For example, it suggests a menu that gradually increases the load based on past data. Also, it analyzes the user's exercise history and adjusts it to avoid menus that have failed. For example, it suggests a menu that avoids exercises that have caused injuries in the past. In this way, it is possible to analyze the user's exercise history and generate an optimal menu based on past achievements and failures.

[0077] The recommendation unit can monitor the user's physical condition or fatigue level in real time and dynamically adjust the exercise menu. The recommendation unit, for example, uses a wearable device to monitor the user's physical condition and fatigue level in real time and dynamically adjust the exercise menu. For example, the recommendation unit adjusts exercise intensity based on heart rate and oxygen saturation. The recommendation unit also evaluates the user's fatigue level based on physical condition data and suggests appropriate rest periods. For example, if fatigue is accumulating, it suggests light exercise or stretching. The recommendation unit also analyzes real-time physical condition data and builds a system that dynamically changes the exercise menu. For example, if the user's physical condition worsens during exercise, it suggests stopping the exercise. This makes it possible to monitor the user's physical condition and fatigue level in real time and dynamically adjust the exercise menu.

[0078] The recommendation unit can share a user's exercise menu with other users and increase motivation through competition or cooperation. The recommendation unit can, for example, share a user's exercise menu within a community and increase motivation through competition or cooperation with other users. For example, an exercise challenge can be set and users can compete for achievement. A system can also be built in which other users can provide feedback and advice on the shared exercise menu. For example, exercise tips and areas for improvement can be shared. Furthermore, by sharing exercise menus within the community, an environment can be created in which users can encourage each other. For example, exercise progress can be shared and messages of encouragement can be sent to each other. This allows a user's exercise menu to be shared with other users and motivation can be increased through competition or cooperation.

[0079] The recommendation unit can visualize the user's exercise menu using AR technology, making it easier to realize the effects of exercise. The recommendation unit, for example, uses AR technology to build a system that visualizes the user's exercise menu and makes it easier to realize the effects of exercise. For example, the exercise movements are displayed using a 3D model. AR technology can also be used to allow the user to check the correct form while exercising. For example, form can be checked in real time using a smartphone or AR glasses. Exercise progress and results can also be visualized using AR, making it easier for the user to feel a sense of accomplishment. For example, calories burned and muscle growth can be visually displayed after exercise. In this way, the user's exercise menu can be visualized using AR technology, making it easier to realize the effects of exercise.

[0080] The recommendation unit uses the emotion estimation function to suggest an exercise menu that allows the user to enjoy exercise, thereby improving the rate at which the user continues to exercise. The recommendation unit, for example, uses the emotion estimation function to suggest an exercise menu that allows the user to enjoy exercise. For example, it analyzes the user's emotional state and suggests exercises that suit their mood. In addition, it customizes the menu based on the emotion estimation data to improve the rate at which the user continues to exercise. For example, it suggests exercises that have a relaxing effect or fun activities. In addition, it analyzes the user's emotional state in real time and provides feedback to improve the rate at which the user continues to exercise. For example, it displays positive messages during exercise. In this way, the emotion estimation function can be used to suggest a menu that allows the user to enjoy exercise, thereby improving the rate at which the user continues to exercise.

[0081] The schedule generation unit can propose a schedule for reducing stress by taking into account the user's emotional state. The schedule generation unit, for example, analyzes the user's emotional state and proposes a schedule for reducing stress. For example, if the emotional score is low, more time is set aside for relaxation. Furthermore, based on the emotional state, activities that allow the user to relax are incorporated into the schedule. For example, time for yoga or meditation is suggested. Furthermore, the user's emotional state is analyzed in real time and feedback for reducing stress is provided. For example, adjustments to the schedule or suggestions for rest periods are made. In this way, a schedule for reducing stress can be proposed by taking into account the user's emotional state.

[0082] The schedule generation unit can analyze the user's lifestyle rhythm and suggest optimal timings for eating and exercising. The schedule generation unit, for example, analyzes the user's lifestyle rhythm and suggests optimal timings for eating and exercising. For example, it sets breakfast and exercise times based on the user's sleep patterns. It also identifies time periods when the user can be efficient based on the lifestyle rhythm and suggests a schedule that matches those time periods. For example, it suggests exercising at times when concentration is strongest. It also builds a system that analyzes the user's lifestyle rhythm in real time and dynamically adjusts the schedule. For example, it adjusts meal and exercise times according to changes in the lifestyle rhythm. In this way, it is possible to analyze the user's lifestyle rhythm and suggest optimal timings for eating and exercising.

[0083] The schedule generation unit can generate a reasonable schedule in cooperation with the user's work or home schedule. The schedule generation unit, for example, builds a system that incorporates the user's work and home schedules and generates a reasonable schedule. For example, it sets exercise and meal times taking into account work meetings and home events. It also adjusts the schedule in cooperation with the work and home schedules so that the user does not feel stressed. For example, it sets time for relaxation before and after important meetings. It also develops a system that analyzes the user's schedule in real time and dynamically adjusts the schedule. For example, it automatically updates the schedule in response to sudden changes in plans. This makes it possible to generate a reasonable schedule in cooperation with the user's work and home schedules.

[0084] The schedule generation unit allows a user to share their schedule with other users and jointly manage their health. The schedule generation unit, for example, builds a system for sharing a user's schedule within a community and jointly managing their health with other users. For example, making exercise and meal plans together with friends and family. A system is also developed in which other users provide feedback and advice on the shared schedule. For example, improvements to the schedule and new suggestions are accepted. Furthermore, an environment is created in which users encourage each other through schedule sharing within the community. For example, they can jointly set goals and share progress. This allows a user's schedule to be shared with other users and jointly managing their health.

[0085] The schedule generation unit can link the user's schedule with a smart device and automatically send reminders or notifications. The schedule generation unit, for example, builds a system that links the user's schedule with a smart device and automatically sends reminders and notifications. For example, it notifies a smartphone of exercise and meal times. The smart device is also used to manage the user's schedule in real time and send necessary reminders. For example, it sends notifications before important appointments. A system is also developed that links the user's schedule with a smart device and dynamically adjusts the schedule. For example, it automatically updates reminders in response to changes in the schedule. This makes it possible to link the user's schedule with a smart device and automatically send reminders and notifications.

[0086] The schedule generation unit can use the emotion estimation function to make suggestions to make it easier for the user to stick to the schedule. The schedule generation unit, for example, uses the emotion estimation function to build a system that makes suggestions to make it easier for the user to stick to the schedule. For example, it analyzes the emotional state and suggests schedule adjustments. It also analyzes the user's emotional state in real time and provides feedback to make it easier for the user to stick to the schedule. For example, it displays a message that elicits positive emotions. It also provides an interface based on the emotion estimation data to make it easier for the user to stick to the schedule. For example, it sends reminders and notifications according to the emotional state. In this way, it is possible to use the emotion estimation function to make suggestions to make it easier for the user to stick to the schedule.

[0087] The risk assessment unit can assess the impact of stress or anxiety on disease risk by taking into account the user's emotional state. The risk assessment unit, for example, analyzes the user's emotional state and builds a system to assess the impact of stress or anxiety on disease risk. For example, it calculates disease risk based on an emotional score. It also identifies factors that increase disease risk when stress or anxiety is high based on the emotional state. For example, it evaluates that high stress increases the risk of heart disease. It also analyzes the user's emotional state in real time and dynamically assesses the impact of stress or anxiety on disease risk. For example, it updates the risk assessment according to changes in the emotional state. This makes it possible to assess the impact of stress and anxiety on disease risk by taking into account the user's emotional state.

[0088] The risk assessment unit can incorporate the user's genetic information and perform disease risk assessment based on the genetic risk. The risk assessment unit, for example, incorporates the user's genetic information and builds a system that performs disease risk assessment taking genetic risk into account. For example, it calculates disease risk based on genetic test results. It also identifies specific diseases with a high genetic risk based on the genetic information and suggests preventive measures to the user. For example, it suggests diet and exercise if the user has a high genetic risk of diabetes. It also analyzes the user's genetic information in real time and dynamically assesses disease risk taking genetic risk into account. For example, it updates the risk assessment in response to updates to the genetic information. This makes it possible to incorporate the user's genetic information and perform disease risk assessment taking genetic risk into account.

[0089] The risk assessment unit can analyze the user's lifestyle habits or environmental factors in detail and provide a personalized disease risk assessment. The risk assessment unit, for example, builds a system that analyzes the user's lifestyle habits and environmental factors in detail and provides a personalized disease risk assessment. For example, it calculates disease risk based on smoking and drinking habits. It also identifies specific high-risk diseases based on lifestyle habits and environmental factors and suggests preventive measures to the user. For example, it suggests an exercise menu if a user has a high risk of heart disease due to lack of exercise. It also analyzes the user's lifestyle habits and environmental factors in real time and dynamically performs a personalized disease risk assessment. For example, it updates the risk assessment in response to changes in lifestyle habits. This makes it possible to analyze the user's lifestyle habits and environmental factors in detail and provide a personalized disease risk assessment.

[0090] The risk assessment unit can compare the user's disease risk with other users and assess the relative risk. The risk assessment unit, for example, builds a system that compares the user's disease risk with other users and assesses the relative risk. For example, it compares with users of the same age or with the same lifestyle. Furthermore, based on the relative risk assessment, it suggests specific preventive measures to the user. For example, if the risk is higher than the average risk for the same age, it suggests improving lifestyle habits. Furthermore, it visually displays the user's disease risk based on comparison data with other users. For example, it shows the relative risk using graphs or charts. This makes it possible to compare the user's disease risk with other users and assess the relative risk.

[0091] The risk assessment unit shares the user's disease risk with a medical institution, allowing the user to receive suggestions for early diagnosis or preventive measures. The risk assessment unit, for example, builds a system for sharing the user's disease risk with a medical institution and receiving suggestions for early diagnosis or preventive measures. For example, if the risk is high, the user receives advice from a medical institution. In addition, through collaboration with a medical institution, the user's disease risk data is shared and specialized diagnosis and preventive measures are provided. For example, risk assessment is performed based on the results of regular health checkups. In addition, a system is developed for sharing the user's disease risk data with a medical institution in real time and receiving suggestions for early diagnosis or preventive measures. For example, the user receives a notification from a medical institution if the risk increases. This allows the user's disease risk to be shared with a medical institution and the user to receive suggestions for early diagnosis or preventive measures.

[0092] The risk assessment unit can use the emotion estimation function to provide information to help users reduce anxiety about disease risk. The risk assessment unit, for example, uses the emotion estimation function to build a system that provides information to help users reduce anxiety about disease risk. For example, the system analyzes the emotional state and displays a message that gives a sense of security. Furthermore, specific advice is provided to help users reduce anxiety about disease risk based on the user's emotional state. For example, relaxation methods and stress management are suggested. Furthermore, information is provided in real time based on the emotion estimation data to help users reduce anxiety about disease risk. For example, appropriate information is displayed according to changes in the emotional state. In this way, the emotion estimation function can be used to provide information to help users reduce anxiety about disease risk.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The data input unit automatically imports the user's past health data or medical history, enabling more accurate goal setting. For example, the user's past health data and medical history can be automatically imported from electronic medical records or health management apps to gain a detailed understanding of their current health condition. For example, goals can be set based on past diagnostic results and prescription history. Automatic import of health data also eliminates the need for users to enter data and improves data accuracy. For example, realistic goals can be suggested based on past weight fluctuations and exercise history. In addition, by linking with medical institutions, the latest health data can be obtained in real time and reflected in goal setting. For example, the results of regular health checkups can be automatically imported and goals can be updated. This enables more accurate goal setting based on past health data and medical history.

[0095] The data input unit can collect the user's lifestyle or daily activity data from sensors in real time and dynamically update goals. For example, the data input unit collects the user's daily activity data in real time using sensors in a wearable device or smartphone. For example, a pedometer or heart rate monitor is used to record the amount of exercise each day. The data input unit also analyzes the user's lifestyle based on the collected activity data and dynamically updates the goals. For example, if the user continues to lack exercise, the data input unit suggests adding more exercise to the user's routine. The data input unit also analyzes the data from the sensors in real time and sets goals that match the user's lifestyle. For example, the data input unit suggests appropriate exercise times based on sleep data. This allows the goals to be dynamically updated based on the lifestyle and daily activity data.

[0096] The data input unit can also input health data of the user's family or friends, allowing the user to set health goals for the entire group. For example, a system can be built to share the health data of the user's family and friends and set health goals for the entire group. For example, a weight loss goal can be set for the whole family and they can work together. Common goals can also be set based on the health data of the entire group and progress can be shared. For example, friends can set exercise challenges and encourage each other. Health data of family and friends can also be imported to link individual goals with group goals. For example, meal recipes can be standardized for the whole family to promote healthy eating habits. This allows family and friends to set health goals and work together.

[0097] The data input unit can input health data of the user's pet and manage the health of the pet together. For example, a system can be constructed in which the user's pet's health data is input and health management is performed together with the pet. For example, the pet's weight and amount of exercise can be recorded and common health goals can be set. Also, based on the pet's health data, a common exercise menu can be proposed for the user and the pet. For example, walking time and distance can be set and the users can exercise together. Also, the pet's dietary data can be input and dietary recipes for the user and the pet can be linked. For example, recipes that take the pet's dietary restrictions into account can be proposed. This allows health management to be performed together with the pet.

[0098] The data input unit can use an emotion estimation function to analyze the user's emotions when entering data and provide positive feedback to encourage input. For example, when a user enters physical data, the emotion estimation function can be used to analyze the user's emotions in real time and provide positive feedback. For example, an encouraging message can be displayed each time input is completed. The emotion estimation function can also be used to analyze the user's emotional state when entering data and make positive suggestions if negative emotions are detected. For example, options to simplify input can be presented if input is difficult. The input process can also be flexibly adjusted based on the user's emotional state and positive feedback can be provided. For example, a message that makes the user feel a sense of accomplishment can be displayed each time input is completed. In this way, input can be encouraged by analyzing the user's emotions and providing positive feedback.

[0099] The recommendation unit can select ingredients that contribute to stress reduction or mood improvement by taking into account the user's emotional state. For example, it analyzes the user's emotional state and selects ingredients that contribute to stress reduction or mood improvement. For example, it suggests recipes that include bananas or dark chocolate, which increase serotonin. It also suggests recipes that use herbs and spices that have a relaxing effect based on the emotional state. For example, it suggests dishes that use chamomile or lavender. It also suggests recipes that use colorful ingredients that improve mood by taking into account the user's emotional state. For example, it suggests salads using colorful vegetables and fruits. In this way, it is possible to select ingredients that contribute to stress reduction or mood improvement by taking into account the user's emotional state.

[0100] The recommendation unit can suggest recipes that use seasonal ingredients based on the season or local specialties. For example, recipes that use seasonal ingredients are suggested, taking into account the season and local specialties. For example, in spring, recipes using asparagus or strawberries are suggested. Also, recipes that use local ingredients are suggested based on local specialties. For example, recipes using fresh seafood are suggested to a user in Hokkaido. Also, recipes that use nutritious seasonal ingredients are suggested based on seasonal ingredients. For example, recipes using pumpkin or sweet potato are suggested in autumn. In this way, it is possible to suggest recipes that use seasonal ingredients, taking into account the season and local specialties.

[0101] The recommendation unit can analyze the user's past meal history and generate a variety of recipes to prevent the user from getting bored. For example, it can analyze the user's past meal history and suggest recipes with a wide variety to prevent the user from repeatedly using the same ingredients or dishes. For example, it can suggest different dishes each week. It can also customize recipes based on the meal history and taking into account the user's preferences and allergy information to prevent the user from getting bored. For example, it can suggest different cooking methods and seasonings. It can also analyze the past meal history and suggest new ingredients and dishes that the user has not tried yet. For example, it can suggest recipes using exotic dishes or new ingredients. In this way, it can analyze the user's past meal history and generate a wide variety of recipes to prevent the user from getting bored.

[0102] The recommendation unit can share a user's meal recipes with other users and improve the recipes based on feedback within the community. For example, a meal recipe created by a user can be shared within the community and the recipe can be improved based on feedback from other users. For example, a function for accepting comments and ratings can be provided. A system can also be built in which other users can make improvement suggestions for shared recipes. For example, suggestions for changes to cooking methods or ingredients can be accepted. The recommendation unit can also improve the user's recipes based on recipes that are popular within the community. For example, new recipes can be suggested based on highly rated recipes. This allows a user's meal recipes to be shared with other users and the recipes can be improved based on feedback within the community.

[0103] The recommendation unit can automatically add a user's meal recipe to an online shopping cart, making it easy to purchase the necessary ingredients. For example, we will build a system that automatically adds ingredients for a meal recipe selected by the user to the online shopping cart. For example, we will make it possible to purchase all the ingredients needed for a recipe in one go. We will also link with online shopping sites to make it easy to purchase ingredients for a recipe selected by the user. For example, we will provide a purchase link at an affiliated site. We will also develop a system that checks ingredient inventory in real time and automatically adds necessary ingredients to the cart. For example, we will suggest alternatives if an ingredient is out of stock. This will allow the user's meal recipe to be automatically added to the online shopping cart, making it easy to purchase the necessary ingredients.

[0104] The recommendation unit uses the emotion estimation function to suggest recipes that will allow the user to enjoy a meal and improve meal satisfaction. For example, the emotion estimation function is used to suggest recipes that will allow the user to enjoy a meal. For example, the emotion estimation function is used to analyze the user's emotional state and suggest dishes that match the user's mood. Furthermore, recipes are customized based on the emotion estimation data to improve meal satisfaction. For example, recipes that use ingredients that have a relaxing effect are suggested. Furthermore, the user's emotional state is analyzed in real time and feedback is provided to improve meal satisfaction. For example, positive messages are displayed during the meal. In this way, the emotion estimation function is used to suggest recipes that will allow the user to enjoy a meal and improve meal satisfaction.

[0105] The recommendation unit can take the user's emotional state into consideration and suggest an exercise menu to maintain motivation. For example, it can analyze the user's emotional state and suggest an exercise menu to maintain motivation. For example, if the emotional score is low, it can suggest light exercise. It can also suggest an exercise menu that the user can enjoy based on the emotional state. For example, it can suggest dance exercises to their favorite music. It can also analyze the user's emotional state in real time and provide feedback to increase motivation. For example, it can display encouraging messages during exercise. This makes it possible to suggest an exercise menu to maintain motivation taking the user's emotional state into consideration.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The data input section allows the user to input their physical data and goals, such as height, weight, age, gender, allergy information, current health condition, etc. Goals can also be input, such as weight loss, muscle building, or improving performance in a specific sport. Step 2: The recommendation unit recommends optimal meal recipes based on the data entered by the data input unit. For example, for a user who is trying to lose weight, it will suggest low-calorie, nutritionally balanced recipes. It also customizes recipes by taking into account the user's allergy information and food preferences. Step 3: The schedule generation unit generates a schedule based on the meal recipes and exercise menus recommended by the recommendation unit. For example, it suggests the timing of meals and the time of day to exercise. The schedule is customized taking into account the user's daily rhythm and work schedule. Step 4: The risk assessment unit assesses the risk of disease based on the data input by the data input unit. For example, the risk of obesity, diabetes, high blood pressure, etc. is assessed, and preventive measures are proposed to the user.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0110] 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0140] 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data input unit for inputting the user's body data and goals; a recommendation unit that recommends optimal meal recipes based on the data input by the data input unit; a schedule generation unit that generates a schedule based on the meal recipes and exercise menus recommended by the recommendation unit; a risk assessment unit that assesses a disease risk based on the data input by the data input unit. A system characterized by:

2. The data input unit The system estimates the user's emotional state in real time and corrects the data based on their stress level and motivation at the time of input.

2. The system of claim 1.

3. The data input unit Automatically incorporates the user's past health data or medical history to set more accurate goals 2. The system of claim 1.

4. The data input unit Collecting lifestyle or daily activity data of the user from a sensor in real time and dynamically updating goals 2. The system of claim 1.

5. The data input unit The user's family or friends' health data may also be entered to set health goals for the entire group.

2. The system of claim 1.

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