System

Abstract

To provide a system that generates individually appropriate meal menus and exercise menus based on results of health checkups, and instructs users on precautions for children regarding items that are highly hereditary.SOLUTION: A system according to the embodiment comprises a reception unit, a menu generation unit, an exercise menu generation unit, and an instruction unit. The reception unit receives results of users' health checkups. The menu generation unit generates meal menus for the users based on the information received by the reception unit. The exercise menu generation unit generates exercise menus for the users based on the information received by the reception unit. The instruction unit instructs the users on precautions for their children based on the information received by the reception unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to automatically generate meal plans and exercise menus that are appropriate for each individual based on the results of a health check, and there is a lack of adequate guidance for children on points of caution regarding items that are highly hereditary.

[0005] The system according to the embodiment aims to generate individually appropriate meal plans and exercise menus based on the results of health checkups, and to instruct children on points of caution regarding items that are highly hereditary. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a menu creation unit, an exercise menu creation unit, and an instruction unit. The reception unit receives the results of a health check of the user. The menu creation unit creates a meal menu for the user based on the information received by the reception unit. The exercise menu creation unit creates an exercise menu for the user based on the information received by the reception unit. The instruction unit instructs the user on points to watch out for for their child based on the information received by the reception unit. Effect of the Invention

[0007] The system according to the embodiment can generate individually appropriate meal plans and exercise menus based on the results of health checkups, and can provide children with advice on items that are highly hereditary. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system that supports the health management of a user based on the results of a yearly health check (physical examination). In this health management system, a user inputs the results of a health check, and an AI analyzes the results to automatically generate a meal plan that is optimal for the user. In addition, the AI ​​also devise an exercise menu in conjunction with the generated meal plan. For items that are highly hereditary, the AI ​​also teaches the user what to be careful of for their children. This system is linked to recipe sites such as Cookpad and Classil, and the generated meal plan is published on these sites. Based on the number of times the published plan is viewed, the user is given a reward. For example, a user inputs the results of a health check. For example, information such as blood pressure, blood glucose level, and cholesterol level is input. This information is input to the AI. Next, the AI ​​analyzes the input information and automatically generates a meal plan that is optimal for the user. For example, a low-salt meal plan is generated for a user with high blood pressure. Also, a low-carbohydrate meal plan is generated for a user with high blood sugar level. The generated meal plan is published on recipe sites such as Cookpad and Classil. In addition, the AI ​​also devise an exercise menu in conjunction with the generated meal menu. For example, for a user with high blood pressure, an exercise menu centered on aerobic exercise is devised. For a user with high blood sugar, an exercise menu centered on muscle training is devised. For items that are highly hereditary, the AI ​​also teaches the user what to watch out for in their children. For example, if a family member has a history of diabetes, the AI ​​advises the child to limit sugar intake. For example, if a family member has a history of high blood pressure, the AI ​​advises the child to limit salt intake. This system is linked to recipe sites such as Cookpad and Classil, and the generated meal menu is published on these sites. Rewards are awarded to users based on the number of views of the published menu. For example, points are awarded to users who publish menus that are viewed many times. These points can be used on sites such as Cookpad and Classil. In this way, the present invention is a system that supports the user's health management by providing the user with optimal meal menus and exercise menus based on the results of a health check, and further teaching the child what to watch out for in items that are highly hereditary.This allows the health management system to automatically provide meal plans, exercise menus, and advice for children based on the user's health check results.

[0029] The health management system according to the embodiment includes a reception unit, a menu generation unit, an exercise menu generation unit, and an instruction unit. The reception unit receives the results of the user's health checkup. The results of the user's health checkup include, for example, blood pressure, blood glucose level, cholesterol level, etc., but are not limited to such examples. The reception unit receives, for example, the results of the health checkup input by the user in a digital format. The reception unit can also scan and digitize the results of the health checkup submitted on paper. For example, the reception unit stores the results of the health checkup input by the user in a database and uses them for analysis later. The menu generation unit uses a generation AI to generate a meal menu for the user based on the information received by the reception unit. The generated meal menu is, for example, configured based on the menus and nutritional balance of breakfast, lunch, and dinner, but is not limited to such examples. For example, the menu generation unit uses a text generation AI (for example, LLM) to generate a meal menu that is optimal for the user's health condition. The menu generation unit can also use a multimodal generation AI to generate images and recipes for the meal menu. For example, the menu generation unit suggests a low-salt diet or a low-carbohydrate diet based on the user's health check results. The exercise menu generation unit uses a generation AI to generate an exercise menu for the user based on the information received by the reception unit. The generated exercise menu includes, for example, aerobic exercise, muscle training, stretching, etc., but is not limited to such examples. For example, the exercise menu generation unit uses a text generation AI to generate an exercise menu that is optimal for the user's health condition. In addition, the exercise menu generation unit can also use a multimodal generation AI to generate videos and images of the exercise menu. For example, the exercise menu generation unit suggests an exercise menu centered on aerobic exercise and muscle training based on the user's health check results. The instruction unit uses a generation AI to instruct the user's child on points to be careful about based on the information received by the reception unit. The contents of the instruction include, for example, points to be careful about for meals, points to be careful about for exercise, points to be careful about for lifestyle habits, etc., but are not limited to such examples. For example, the instruction unit uses a text generation AI to advise the user's child to limit carbohydrates.The teaching unit can also generate videos and images of points of caution using a multimodal generation AI. For example, the teaching unit advises a child to limit salt intake based on the user's health checkup results. This allows the health management system according to the embodiment to automatically provide meal plans, exercise menus, and points of caution for children based on the user's health checkup results.

[0030] The reception unit accepts the results of the user's health checkup. The results of the user's health checkup include, but are not limited to, blood pressure, blood glucose level, cholesterol level, and the like. For example, the reception unit accepts the results of the health checkup entered by the user in digital format. The reception unit can also scan and digitize the results of the health checkup submitted on paper. For example, the reception unit stores the results of the health checkup entered by the user in a database and uses them for later analysis. The reception unit provides an interface for the user to input the results of the health checkup. This interface is accessible through a web browser or a mobile application, and the user can easily input his or her health checkup results. The input data is immediately stored in the database and used for subsequent processing. Furthermore, the reception unit uses OCR (optical character recognition) technology to scan the results of the health checkup on paper and convert them into digital data. This technology allows handwritten and printed results to be accurately digitized and stored in the database. The reception unit also verifies the input data to ensure the accuracy of the data. For example, the reception unit has a function of detecting abnormal values ​​and input errors and prompting the user to recheck. This enables the reception unit to receive the user's medical examination results accurately and efficiently, thereby improving the reliability of the entire system.

[0031] The menu generation unit uses a generation AI to generate a meal menu for the user based on the information received by the reception unit. The generated meal menu is, for example, configured based on the menus and nutritional balance of breakfast, lunch, and dinner, but is not limited to such an example. For example, the menu generation unit uses a text generation AI (e.g., LLM) to generate a meal menu that is optimal for the user's health condition. The menu generation unit can also generate images and recipes for the meal menu using a multimodal generation AI. For example, the menu generation unit suggests a low-salt meal or a low-carbohydrate meal based on the user's health checkup results. The menu generation unit takes into account not only the user's health checkup results but also the user's dietary preferences and allergy information. For example, if the user has an allergy to a specific ingredient, the menu generation unit generates a menu that does not include that ingredient. Also, if the user desires a specific eating style (e.g., vegetarian or gluten-free), the menu generation unit provides a menu suitable for that style. Furthermore, the menu generation unit can also suggest a menu that utilizes seasonal ingredients, taking into account the characteristics of the season and region. This allows the user to enjoy healthy yet delicious meals. When providing the user with the generated menu, the menu generator also provides detailed nutritional information and cooking instructions. For example, the calorie, protein, fat, and carbohydrate content of each menu item may be displayed, making it easier for the user to manage their nutritional intake. In addition, cooking instructions are explained in an easy-to-understand manner using not only text but also images and videos. This allows the user to easily put the menu into practice.

[0032] The exercise menu generating unit uses a generation AI to generate an exercise menu for the user based on the information received by the reception unit. The generated exercise menu includes, for example, aerobic exercise, strength training, stretching, etc., but is not limited to such examples. For example, the exercise menu generating unit uses a text generation AI to generate an exercise menu that is optimal for the user's health condition. In addition, the exercise menu generating unit can also generate videos and images of the exercise menu using a multimodal generation AI. For example, the exercise menu generating unit proposes an exercise menu centered on aerobic exercise and strength training based on the user's health check results. The exercise menu generating unit also takes into account the user's exercise experience and physical fitness level. For example, a menu starting with simple exercises is proposed for beginners, and more advanced training is provided for experienced exercisers. In addition, the optimal exercise menu is customized taking into account individual characteristics such as the user's age, sex, and weight. Furthermore, the exercise menu generating unit provides a menu according to the user's goal (for example, weight loss, muscle strengthening, and flexibility improvement). This allows the user to effectively exercise toward his or her goal. When providing the generated exercise menu to the user, the exercise menu generation unit also provides detailed explanations and points to note. For example, it explains the correct form and breathing techniques for each exercise, as well as important points, so that the user can exercise safely. It also provides a function for recording the progress of the exercise menu and allowing the user to check their results. This allows the user to continue exercising while maintaining their motivation.

[0033] The teaching unit uses the generation AI to teach the user's child about points to be careful about based on the information accepted by the acceptance unit. The contents of the instruction include, for example, points to be careful about in terms of diet, exercise, and lifestyle habits, but are not limited to such examples. For example, the teaching unit uses the text generation AI to advise the user's child to limit their sugar intake. The teaching unit can also generate videos and images of points to be careful about using the multimodal generation AI. For example, the teaching unit advises the child to limit their salt intake based on the results of the user's health check. The teaching unit provides customized advice according to the age and health condition of the user's child. For example, advice is given to infants that emphasizes a balanced diet, and the importance of exercise is emphasized for school-age children. The teaching unit also makes suggestions for improving lifestyle habits according to the child's growth stage. This allows the user to effectively manage the health of the child. When providing the generated advice to the user, the teaching unit also provides specific practice methods and points to be careful about. For example, healthy recipes using ingredients that the child likes and exercise menus that parents and children can enjoy are suggested. The advice can also be explained with illustrations and videos so that children can easily understand. This allows the user to enjoy health management together with their children. Furthermore, the teaching unit collects feedback from the user and continuously improves the content of the advice. For example, more effective advice can be provided based on the results and impressions of the user's practice. The teaching unit can also incorporate the latest health information and research results to always provide the latest advice. This allows the teaching unit to support the health management of the user and their children and help them build better lifestyle habits.

[0034] The menu generating unit includes a publishing unit that publishes the generated meal menu on a recipe site. The publishing unit publishes the generated meal menu on the recipe site. Examples of recipe sites include, but are not limited to, Cookpad and Kurashiru. The publishing unit, for example, publishes the generated meal menu on Cookpad so that other users can view it. The publishing unit can also publish the generated meal menu on Kurashiru so that other users can view it. For example, the publishing unit automatically posts the generated meal menu on a recipe site so that users can easily access it. This allows users to share information with other users by publishing the generated meal menu on the recipe site. Some or all of the above-mentioned processing in the publishing unit may be performed, for example, using AI, or may be performed without using AI. For example, the publishing unit can input the generated meal menu into an AI model to automate posting to the recipe site.

[0035] The publishing unit includes a reward granting unit that grants a reward to the user based on the number of views of the meal menu on the recipe site. The reward granting unit grants a reward to the user based on the number of views of the meal menu on the recipe site. The reward includes, but is not limited to, points, money, coupons, and the like. For example, the reward granting unit grants points to the user based on the number of views on Cookpad. The reward granting unit can also grant money to the user based on the number of views on Kurashiru. For example, the reward granting unit provides a coupon to a user who has a large number of views on the recipe site. This improves the motivation of the user by granting a reward based on the number of views on the recipe site. A part or all of the above-mentioned processing in the reward granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reward granting unit can input the browsing data of the recipe site into an AI model to automate the calculation and granting of the reward.

[0036] The reception unit analyzes the user's past medical examination results and selects an appropriate input method. The reception unit, for example, automatically completes input items based on data previously input by the user. The reception unit can also preferentially display frequently input items from the user's past input history. Furthermore, the reception unit can analyze the user's past medical examination results and customize the input method. For example, the reception unit stores the past medical examination results in a database and uses them for analysis. In this way, the optimal input method can be provided to the user by analyzing the past medical examination results. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the past medical examination results into an AI model and cause the AI ​​to perform analysis to select the optimal input method.

[0037] The reception unit filters the health check results based on the user's current living situation and areas of interest when the health check results are input. For example, when the user is on a diet, the reception unit preferentially inputs related health check results. In addition, when the user is interested in a specific disease, the reception unit can also highlight items related to the disease. Furthermore, the reception unit can filter related health check results based on the user's lifestyle. For example, the reception unit collects data related to the user's living situation and areas of interest and uses the data for analysis. As a result, highly relevant data can be collected by filtering based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into an AI model and have the AI ​​perform filtering.

[0038] When inputting the medical examination results, the reception unit preferentially inputs highly relevant results in consideration of the geographical location information of the user. For example, if the user lives at high altitude, the reception unit preferentially inputs items related to health risks specific to high altitude. In addition, if the user lives in an urban area, the reception unit can also preferentially input items related to health risks specific to cities. Furthermore, if the user lives in a rural area, the reception unit can also preferentially input items related to health risks specific to rural areas. For example, the reception unit collects the geographical location information of the user and uses it for analysis. In this way, data corresponding to health risks specific to the region can be collected by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user into an AI model and cause the AI ​​to perform analysis for preferentially inputting highly relevant results.

[0039] The reception unit analyzes the user's social media activity when inputting the medical examination result, and inputs the related result. For example, if the user frequently posts about health on social media, the reception unit suggests input items based on the content of the posts. In addition, if the user shows interest in a specific health topic, the reception unit can also preferentially input items related to the topic. Furthermore, the reception unit can analyze the user's health-related interest field from the user's social media activity and customize the input items. For example, the reception unit collects the user's social media activity and uses it for analysis. In this way, data based on the user's interest field can be collected by analyzing the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into an AI model and have the AI ​​perform analysis to input related results.

[0040] The menu generation unit adjusts the level of detail of the menu based on the importance of the health check result when generating the menu. For example, the menu generation unit generates a detailed menu based on an item determined to be high risk in the health check result. The menu generation unit can also generate a menu with a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the menu generation unit can generate a simplified menu based on an item determined to be low risk in the health check result. For example, the menu generation unit evaluates the importance of the health check result and uses it for analysis. This allows the user to be provided with an optimal menu by adjusting the level of detail of the menu based on the importance of the health check result. A part or all of the above-mentioned processing in the menu generation unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generation unit can input importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the menu.

[0041] The menu generating unit applies different menu generating algorithms according to the categories of the health check results when generating the menu. The menu generating unit applies an algorithm for generating a menu with less salt based on, for example, the results related to blood pressure. The menu generating unit can also apply an algorithm for generating a menu with less sugar based on the results related to blood glucose levels. Furthermore, the menu generating unit can also apply an algorithm for generating a menu with less lipids based on the results related to cholesterol levels. For example, the menu generating unit evaluates the category of the health check results and uses it for analysis. This allows a more appropriate menu to be generated by applying different algorithms according to the categories of the health check results. Some or all of the above-mentioned processing in the menu generating unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generating unit can input category data of the health check results into an AI model and cause the AI ​​to perform analysis for applying different algorithms.

[0042] The menu generation unit determines the priority of the menu based on the time of submission of the health check result when generating the menu. For example, if the health check result is recent, the menu generation unit generates the menu based on the result. Also, if the health check result is old, the menu generation unit generates the menu based on the result, but can lower the priority. Furthermore, the menu generation unit can adjust the order of menu generation based on the time of submission of the health check result. For example, the menu generation unit evaluates the time of submission of the health check result and uses it for analysis. As a result, by determining the priority of the menu based on the time of submission of the health check result, it is possible to provide a menu based on the latest data. Part or all of the above-mentioned processing in the menu generation unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generation unit can input data on the time of submission of the health check result into an AI model and cause AI to perform analysis to determine the priority of the menu.

[0043] The menu generation unit adjusts the order of the menu based on the relevance of the health check result when generating the menu. For example, the menu generation unit generates the most relevant menu with priority based on the items whose health check result is determined to be high risk. The menu generation unit also generates a relevant menu based on the items whose health check result is determined to be medium risk, but can also lower the priority. Furthermore, the menu generation unit can generate a less relevant menu based on the items whose health check result is determined to be low risk. For example, the menu generation unit evaluates the relevance of the health check result and uses it for analysis. Thereby, by adjusting the order of the menu based on the relevance of the health check result, a more relevant menu can be provided. A part or all of the above-mentioned processing in the menu generation unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generation unit can input the relevance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the order of the menu.

[0044] The exercise menu generating unit adjusts the level of detail of the exercise menu based on the importance of the health check result when generating the exercise menu. The exercise menu generating unit generates a detailed exercise menu based on, for example, an item determined to be high risk in the health check result. The exercise menu generating unit can also generate an exercise menu with a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the exercise menu generating unit can also generate a simplified exercise menu based on an item determined to be low risk in the health check result. For example, the exercise menu generating unit evaluates the importance of the health check result and uses it for analysis. As a result, the level of detail of the exercise menu can be adjusted based on the importance of the health check result, thereby providing an optimal exercise menu to the user. A part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI or may be performed without using AI. For example, the exercise menu generating unit can input the importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the exercise menu.

[0045] The exercise menu generating unit applies different exercise menu generating algorithms according to the categories of the health check results when generating the exercise menu. The exercise menu generating unit applies an algorithm for generating an exercise menu centered on aerobic exercise, for example, based on the results related to blood pressure. The exercise menu generating unit can also apply an algorithm for generating an exercise menu centered on muscle training, based on the results related to blood glucose levels. Furthermore, the exercise menu generating unit can also apply an algorithm for generating an exercise menu for fat burning, based on the results related to cholesterol levels. For example, the exercise menu generating unit evaluates the categories of the health check results and uses them for analysis. This allows a more appropriate exercise menu to be generated by applying different algorithms according to the categories of the health check results. Part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the exercise menu generating unit can input category data of the health check results into an AI model and cause the AI ​​to perform analysis for applying different algorithms.

[0046] The exercise menu generating unit determines the priority of the exercise menu based on the time of submission of the health check result when generating the exercise menu. For example, if the health check result is recent, the exercise menu generating unit generates the exercise menu based on the result. Also, if the health check result is old, the exercise menu generating unit generates the exercise menu based on the result, but can also lower the priority. Furthermore, the exercise menu generating unit can adjust the order of generation of the exercise menu based on the time of submission of the health check result. For example, the exercise menu generating unit evaluates the time of submission of the health check result and uses it for analysis. As a result, by determining the priority of the exercise menu based on the time of submission of the health check result, it is possible to provide an exercise menu based on the latest data. Part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the exercise menu generating unit can input the submission time data of the health check result into an AI model and cause AI to perform analysis to determine the priority of the exercise menu.

[0047] The exercise menu generating unit adjusts the order of the exercise menu based on the relevance of the health check result when generating the exercise menu. For example, the exercise menu generating unit generates the most relevant exercise menu preferentially based on the item whose health check result is determined to be high risk. In addition, the exercise menu generating unit generates a relevant exercise menu based on the item whose health check result is determined to be medium risk, but can also lower the priority. Furthermore, the exercise menu generating unit can generate a less relevant exercise menu based on the item whose health check result is determined to be low risk. For example, the exercise menu generating unit evaluates the relevance of the health check result and uses it for analysis. As a result, by adjusting the order of the exercise menu based on the relevance of the health check result, a more relevant exercise menu can be provided. Part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI or may be performed without using AI. For example, the exercise menu generating unit can input the relevance data of the health check result into an AI model and cause AI to perform analysis to adjust the order of the exercise menu.

[0048] The teaching unit adjusts the level of detail of the teaching contents based on the importance of the health check result during teaching. For example, the teaching unit provides detailed teaching contents based on an item determined to be high risk in the health check result. The teaching unit can also provide teaching contents with a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the teaching unit can also provide simplified teaching contents based on an item determined to be low risk in the health check result. For example, the teaching unit evaluates the importance of the health check result and uses it for analysis. This allows the level of detail of the teaching contents to be adjusted based on the importance of the health check result, thereby providing optimal teaching contents to the user. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the teaching contents.

[0049] The teaching unit applies different teaching algorithms according to the categories of the health check results during teaching. For example, the teaching unit applies an algorithm that teaches to limit salt intake based on the results related to blood pressure. The teaching unit can also apply an algorithm that teaches to limit carbohydrate intake based on the results related to blood glucose levels. Furthermore, the teaching unit can also apply an algorithm that teaches to limit lipid intake based on the results related to cholesterol levels. For example, the teaching unit evaluates the category of the health check results and uses it for analysis. This allows more appropriate teaching content to be provided by applying different algorithms according to the categories of the health check results. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input category data of the health check results into an AI model and cause the AI ​​to perform analysis to apply different algorithms.

[0050] The teaching unit determines the priority of the teaching contents based on the submission time of the health check result at the time of teaching. For example, when the health check result is the most recent, the teaching unit provides the teaching contents based on the result. Also, when the health check result is old, the teaching unit provides the teaching contents based on the result, but can lower the priority. Furthermore, the teaching unit can adjust the order of providing the teaching contents based on the submission time of the health check result. For example, the teaching unit evaluates the submission time of the health check result and uses it for analysis. In this way, by determining the priority of the teaching contents based on the submission time of the health check result, it is possible to provide the teaching contents based on the latest data. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the submission time data of the health check result into an AI model and cause the AI ​​to perform an analysis for determining the priority of the teaching contents.

[0051] The teaching unit adjusts the order of the teaching contents based on the relevance of the health check result at the time of teaching. For example, the teaching unit provides the most relevant teaching contents preferentially based on the items whose health check result is determined to be high risk. The teaching unit also provides the relevant teaching contents based on the items whose health check result is determined to be medium risk, but can also lower the priority. Furthermore, the teaching unit can also provide the less relevant teaching contents based on the items whose health check result is determined to be low risk. For example, the teaching unit evaluates the relevance of the health check result and uses it for analysis. Thereby, by adjusting the order of the teaching contents based on the relevance of the health check result, it is possible to provide more relevant teaching contents. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the relevance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the order of the teaching contents.

[0052] The disclosure unit adjusts the level of detail of the disclosure content based on the importance of the health check result at the time of disclosure. The disclosure unit provides detailed disclosure content based on, for example, items determined to be high risk in the health check result. The disclosure unit can also provide disclosure content with a moderate level of detail based on items determined to be medium risk in the health check result. Furthermore, the disclosure unit can provide simplified disclosure content based on items determined to be low risk in the health check result. For example, the disclosure unit evaluates the importance of the health check result and uses it for analysis. This allows the disclosure content optimal to the user to be provided by adjusting the level of detail of the disclosure content based on the importance of the health check result. Part or all of the above-mentioned processing in the disclosure unit may be performed, for example, using AI or may be performed without using AI. For example, the disclosure unit can input the importance data of the health check result into an AI model and cause the AI ​​to perform analysis for adjusting the level of detail of the disclosure content.

[0053] The disclosure unit determines the priority of the disclosure contents based on the submission time of the health check results at the time of disclosure. For example, if the health check results are recent, the disclosure unit provides the disclosure contents based on the results with priority. In addition, if the health check results are old, the disclosure unit provides the disclosure contents based on the results, but can also lower the priority. Furthermore, the disclosure unit can adjust the order of providing the disclosure contents based on the submission time of the health check results. For example, the disclosure unit evaluates the submission time of the health check results and uses it for analysis. In this way, by determining the priority of the disclosure contents based on the submission time of the health check results, it is possible to provide the disclosure contents based on the latest data. Part or all of the above-mentioned processing in the disclosure unit may be performed, for example, using AI or may be performed without using AI. For example, the disclosure unit can input the submission time data of the health check results into an AI model and cause AI to perform analysis to determine the priority of the disclosure contents.

[0054] The reward granting unit adjusts the level of detail of the reward based on the importance of the health check result when granting the reward. For example, the reward granting unit provides a detailed reward based on an item determined to be high risk in the health check result. The reward granting unit can also provide a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the reward granting unit can also provide a simplified reward based on an item determined to be low risk in the health check result. For example, the reward granting unit evaluates the importance of the health check result and uses it for analysis. This allows the user to be provided with an optimal reward by adjusting the level of detail of the reward based on the importance of the health check result. A part or all of the above-mentioned processing in the reward granting unit may be performed, for example, using AI or may be performed without using AI. For example, the reward granting unit can input the importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the reward.

[0055] The reward granting unit weights the reward based on the time of submission of the medical examination result when granting the reward. For example, if the medical examination result is recent, the reward granting unit weights the reward based on the result. In addition, if the medical examination result is old, the reward granting unit can also lighten the reward based on the result. Furthermore, the reward granting unit can adjust the weighting of the reward based on the time of submission of the medical examination result. For example, the reward granting unit evaluates the time of submission of the medical examination result and uses it for analysis. As a result, by weighting the reward based on the time of submission of the medical examination result, it is possible to provide a reward based on the latest data. Part or all of the above-mentioned processing in the reward granting unit may be performed, for example, using AI or may be performed without using AI. For example, the reward granting unit can input data on the time of submission of the medical examination result into an AI model and cause the AI ​​to perform analysis to adjust the weighting of the reward.

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

[0057] The health management system can also obtain the user's sleep data and manage the health based on this. For example, the system can monitor the user's sleep time and quality and adjust the meal plan and exercise menu based on this. For a user who is sleep-deprived, it can suggest a plan using ingredients with a relaxing effect and an exercise menu focusing on light stretching. For a user who is getting good quality sleep, it can suggest a plan that emphasizes energy replenishment and a high-intensity exercise menu. This makes it possible to manage the health according to the user's sleep state.

[0058] The health management system can also monitor the user's water intake and manage the health based on the monitoring. For example, if the user does not consume enough water, a notification is sent to encourage the user to hydrate. The system can also adjust the meal plan based on the water intake and suggest a plan that uses ingredients that help with hydration. Furthermore, the system can adjust the exercise menu according to the water intake and suggest exercises to prevent dehydration. This allows the system to manage the health according to the user's water intake.

[0059] The health management system can also obtain allergy information about the user and manage the user's health based on this information. For example, if the user is allergic to a particular ingredient, a menu can be generated that avoids that ingredient. The system can also adjust the exercise menu based on the allergy information and suggest exercises that do not cause an allergic reaction. This makes it possible to manage the user's health based on their allergy information.

[0060] The health management system can also obtain the user's social activity data and manage the user's health based on this data. For example, if the user eats out frequently, the system can suggest healthy menus to choose when eating out. Also, if the user does not get enough exercise, the system can suggest exercise menus that are easy to incorporate into daily life. This makes it possible to manage the user's health according to their social activities.

[0061] The health management system can also obtain the user's exercise history and perform health management based on this. For example, it can suggest an effective exercise menu based on the data of the user's past exercises. It can also adjust meal plans based on the exercise history and suggest meals that use ingredients that help replenish nutrients after exercise. This makes it possible to manage health according to the user's exercise history.

[0062] The health management system can also obtain the user's dietary history and manage the user's health based on this. For example, a balanced meal plan can be proposed based on the data of the meals the user has eaten in the past. In addition, the system can adjust the exercise menu based on the dietary history and suggest exercises according to the calorie intake. This allows for health management based on the user's dietary history.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives the results of the user's health check. The results of the user's health check include, but are not limited to, blood pressure, blood glucose level, cholesterol level, etc. The reception unit, for example, receives the results of the health check input by the user in digital format. The reception unit can also scan and digitize the results of the health check submitted on paper. For example, the reception unit stores the results of the health check input by the user in a database and uses them for later analysis. Step 2: The menu generation unit uses the generation AI to generate a meal menu for the user based on the information received by the reception unit. The generated meal menu is configured, for example, based on the menus for breakfast, lunch, and dinner and nutritional balance, but is not limited to such an example. For example, the menu generation unit uses a text generation AI (e.g., LLM) to generate a meal menu that is optimal for the user's health condition. The menu generation unit can also generate images and recipes for the meal menu using a multimodal generation AI. For example, the menu generation unit suggests low-salt meals and low-carbohydrate meals based on the user's health check results. Step 3: The exercise menu generation unit uses the generation AI to generate an exercise menu for the user based on the information received by the reception unit. The generated exercise menu includes, for example, aerobic exercise, strength training, stretching, etc., but is not limited to such examples. For example, the exercise menu generation unit uses a text generation AI to generate an exercise menu that is optimal for the user's health condition. In addition, the exercise menu generation unit can also generate videos and images of the exercise menu using a multimodal generation AI. For example, the exercise menu generation unit suggests an exercise menu centered on aerobic exercise and strength training based on the user's health check results. Step 4: The teaching unit uses the generation AI to teach the user's child about points to be careful about based on the information accepted by the acceptance unit. The contents of the instruction include, but are not limited to, points to be careful about regarding diet, exercise, and lifestyle habits. For example, the teaching unit uses the text generation AI to advise the user's child to limit their sugar intake. The teaching unit can also use the multimodal generation AI to generate videos and images of the points to be careful about. For example, the teaching unit advises the child to limit their salt intake based on the results of the user's health check.

[0065] (Example 2) A health management system according to an embodiment of the present invention is a system that supports the health management of a user based on the results of a yearly health check (physical examination). In this health management system, a user inputs the results of a health check, and an AI analyzes the results to automatically generate a meal plan that is optimal for the user. In addition, the AI ​​also devise an exercise menu in conjunction with the generated meal plan. For items that are highly hereditary, the AI ​​also teaches the user what to be careful of for their children. This system is linked to recipe sites such as Cookpad and Classil, and the generated meal plan is published on these sites. Based on the number of times the published plan is viewed, the user is given a reward. For example, a user inputs the results of a health check. For example, information such as blood pressure, blood glucose level, and cholesterol level is input. This information is input to the AI. Next, the AI ​​analyzes the input information and automatically generates a meal plan that is optimal for the user. For example, a low-salt meal plan is generated for a user with high blood pressure. Also, a low-carbohydrate meal plan is generated for a user with high blood sugar level. The generated meal plan is published on recipe sites such as Cookpad and Classil. In addition, the AI ​​also devise an exercise menu in conjunction with the generated meal menu. For example, for a user with high blood pressure, an exercise menu centered on aerobic exercise is devised. For a user with high blood sugar, an exercise menu centered on muscle training is devised. For items that are highly hereditary, the AI ​​also teaches the user what to watch out for in their children. For example, if a family member has a history of diabetes, the AI ​​advises the child to limit sugar intake. For example, if a family member has a history of high blood pressure, the AI ​​advises the child to limit salt intake. This system is linked to recipe sites such as Cookpad and Classil, and the generated meal menu is published on these sites. Rewards are awarded to users based on the number of views of the published menu. For example, points are awarded to users who publish menus that are viewed many times. These points can be used on sites such as Cookpad and Classil. In this way, the present invention is a system that supports the user's health management by providing the user with optimal meal menus and exercise menus based on the results of a health check, and further teaching the child what to watch out for in items that are highly hereditary.This allows the health management system to automatically provide meal plans, exercise menus, and advice for children based on the user's health check results.

[0066] The health management system according to the embodiment includes a reception unit, a menu generation unit, an exercise menu generation unit, and an instruction unit. The reception unit receives the results of the user's health checkup. The results of the user's health checkup include, for example, blood pressure, blood glucose level, cholesterol level, etc., but are not limited to such examples. The reception unit receives, for example, the results of the health checkup input by the user in a digital format. The reception unit can also scan and digitize the results of the health checkup submitted on paper. For example, the reception unit stores the results of the health checkup input by the user in a database and uses them for analysis later. The menu generation unit uses a generation AI to generate a meal menu for the user based on the information received by the reception unit. The generated meal menu is, for example, configured based on the menus and nutritional balance of breakfast, lunch, and dinner, but is not limited to such examples. For example, the menu generation unit uses a text generation AI (for example, LLM) to generate a meal menu that is optimal for the user's health condition. The menu generation unit can also use a multimodal generation AI to generate images and recipes for the meal menu. For example, the menu generation unit suggests a low-salt diet or a low-carbohydrate diet based on the user's health check results. The exercise menu generation unit uses a generation AI to generate an exercise menu for the user based on the information received by the reception unit. The generated exercise menu includes, for example, aerobic exercise, muscle training, stretching, etc., but is not limited to such examples. For example, the exercise menu generation unit uses a text generation AI to generate an exercise menu that is optimal for the user's health condition. In addition, the exercise menu generation unit can also use a multimodal generation AI to generate videos and images of the exercise menu. For example, the exercise menu generation unit suggests an exercise menu centered on aerobic exercise and muscle training based on the user's health check results. The instruction unit uses a generation AI to instruct the user's child on points to be careful about based on the information received by the reception unit. The contents of the instruction include, for example, points to be careful about for meals, points to be careful about for exercise, points to be careful about for lifestyle habits, etc., but are not limited to such examples. For example, the instruction unit uses a text generation AI to advise the user's child to limit carbohydrates.The teaching unit can also generate videos and images of points of caution using a multimodal generation AI. For example, the teaching unit advises a child to limit salt intake based on the user's health checkup results. This allows the health management system according to the embodiment to automatically provide meal plans, exercise menus, and points of caution for children based on the user's health checkup results.

[0067] The reception unit accepts the results of the user's health checkup. The results of the user's health checkup include, but are not limited to, blood pressure, blood glucose level, cholesterol level, and the like. For example, the reception unit accepts the results of the health checkup entered by the user in digital format. The reception unit can also scan and digitize the results of the health checkup submitted on paper. For example, the reception unit stores the results of the health checkup entered by the user in a database and uses them for later analysis. The reception unit provides an interface for the user to input the results of the health checkup. This interface is accessible through a web browser or a mobile application, and the user can easily input his or her health checkup results. The input data is immediately stored in the database and used for subsequent processing. Furthermore, the reception unit uses OCR (optical character recognition) technology to scan the results of the health checkup on paper and convert them into digital data. This technology allows handwritten and printed results to be accurately digitized and stored in the database. The reception unit also verifies the input data to ensure the accuracy of the data. For example, the reception unit has a function of detecting abnormal values ​​and input errors and prompting the user to recheck. This enables the reception unit to receive the user's medical examination results accurately and efficiently, thereby improving the reliability of the entire system.

[0068] The menu generation unit uses a generation AI to generate a meal menu for the user based on the information received by the reception unit. The generated meal menu is, for example, configured based on the menus and nutritional balance of breakfast, lunch, and dinner, but is not limited to such an example. For example, the menu generation unit uses a text generation AI (e.g., LLM) to generate a meal menu that is optimal for the user's health condition. The menu generation unit can also generate images and recipes for the meal menu using a multimodal generation AI. For example, the menu generation unit suggests a low-salt meal or a low-carbohydrate meal based on the user's health checkup results. The menu generation unit takes into account not only the user's health checkup results but also the user's dietary preferences and allergy information. For example, if the user has an allergy to a specific ingredient, the menu generation unit generates a menu that does not include that ingredient. Also, if the user desires a specific eating style (e.g., vegetarian or gluten-free), the menu generation unit provides a menu suitable for that style. Furthermore, the menu generation unit can also suggest a menu that utilizes seasonal ingredients, taking into account the characteristics of the season and region. This allows the user to enjoy healthy yet delicious meals. When providing the user with the generated menu, the menu generator also provides detailed nutritional information and cooking instructions. For example, the calorie, protein, fat, and carbohydrate content of each menu item may be displayed, making it easier for the user to manage their nutritional intake. In addition, cooking instructions are explained in an easy-to-understand manner using not only text but also images and videos. This allows the user to easily put the menu into practice.

[0069] The exercise menu generating unit uses a generation AI to generate an exercise menu for the user based on the information received by the reception unit. The generated exercise menu includes, for example, aerobic exercise, strength training, stretching, etc., but is not limited to such examples. For example, the exercise menu generating unit uses a text generation AI to generate an exercise menu that is optimal for the user's health condition. In addition, the exercise menu generating unit can also generate videos and images of the exercise menu using a multimodal generation AI. For example, the exercise menu generating unit proposes an exercise menu centered on aerobic exercise and strength training based on the user's health check results. The exercise menu generating unit also takes into account the user's exercise experience and physical fitness level. For example, a menu starting with simple exercises is proposed for beginners, and more advanced training is provided for experienced exercisers. In addition, the optimal exercise menu is customized taking into account individual characteristics such as the user's age, sex, and weight. Furthermore, the exercise menu generating unit provides a menu according to the user's goal (for example, weight loss, muscle strengthening, and flexibility improvement). This allows the user to effectively exercise toward his or her goal. When providing the generated exercise menu to the user, the exercise menu generation unit also provides detailed explanations and points to note. For example, it explains the correct form and breathing techniques for each exercise, as well as important points, so that the user can exercise safely. It also provides a function for recording the progress of the exercise menu and allowing the user to check their results. This allows the user to continue exercising while maintaining their motivation.

[0070] The teaching unit uses the generation AI to teach the user's child about points to be careful about based on the information accepted by the acceptance unit. The contents of the instruction include, for example, points to be careful about in terms of diet, exercise, and lifestyle habits, but are not limited to such examples. For example, the teaching unit uses the text generation AI to advise the user's child to limit their sugar intake. The teaching unit can also generate videos and images of points to be careful about using the multimodal generation AI. For example, the teaching unit advises the child to limit their salt intake based on the results of the user's health check. The teaching unit provides customized advice according to the age and health condition of the user's child. For example, advice is given to infants that emphasizes a balanced diet, and the importance of exercise is emphasized for school-age children. The teaching unit also makes suggestions for improving lifestyle habits according to the child's growth stage. This allows the user to effectively manage the health of the child. When providing the generated advice to the user, the teaching unit also provides specific practice methods and points to be careful about. For example, healthy recipes using ingredients that the child likes and exercise menus that parents and children can enjoy are suggested. The advice can also be explained with illustrations and videos so that children can easily understand. This allows the user to enjoy health management together with their children. Furthermore, the teaching unit collects feedback from the user and continuously improves the content of the advice. For example, more effective advice can be provided based on the results and impressions of the user's practice. The teaching unit can also incorporate the latest health information and research results to always provide the latest advice. This allows the teaching unit to support the health management of the user and their children and help them build better lifestyle habits.

[0071] The menu generating unit includes a publishing unit that publishes the generated meal menu on a recipe site. The publishing unit publishes the generated meal menu on the recipe site. Examples of recipe sites include, but are not limited to, Cookpad and Kurashiru. The publishing unit, for example, publishes the generated meal menu on Cookpad so that other users can view it. The publishing unit can also publish the generated meal menu on Kurashiru so that other users can view it. For example, the publishing unit automatically posts the generated meal menu on a recipe site so that users can easily access it. This allows users to share information with other users by publishing the generated meal menu on the recipe site. Some or all of the above-mentioned processing in the publishing unit may be performed, for example, using AI, or may be performed without using AI. For example, the publishing unit can input the generated meal menu into an AI model to automate posting to the recipe site.

[0072] The publishing unit includes a reward granting unit that grants a reward to the user based on the number of views of the meal menu on the recipe site. The reward granting unit grants a reward to the user based on the number of views of the meal menu on the recipe site. The reward includes, but is not limited to, points, money, coupons, and the like. For example, the reward granting unit grants points to the user based on the number of views on Cookpad. The reward granting unit can also grant money to the user based on the number of views on Kurashiru. For example, the reward granting unit provides a coupon to a user who has a large number of views on the recipe site. This improves the motivation of the user by granting a reward based on the number of views on the recipe site. A part or all of the above-mentioned processing in the reward granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reward granting unit can input the browsing data of the recipe site into an AI model to automate the calculation and granting of the reward.

[0073] The reception unit estimates the user's emotion and adjusts the input timing of the medical examination result based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit prompts the user to input the medical examination result during a time when the user can relax. In addition, when the user is busy, the reception unit can narrow down the input items so that the user can input in a short time. Furthermore, when the user is relaxed, the reception unit can collect more accurate data by requesting detailed input. For example, the reception unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other technologies. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, more accurate data can be collected by adjusting the input timing according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the input timing.

[0074] The reception unit analyzes the user's past medical examination results and selects an appropriate input method. The reception unit, for example, automatically completes input items based on data previously input by the user. The reception unit can also preferentially display frequently input items from the user's past input history. Furthermore, the reception unit can analyze the user's past medical examination results and customize the input method. For example, the reception unit stores the past medical examination results in a database and uses them for analysis. In this way, the optimal input method can be provided to the user by analyzing the past medical examination results. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the past medical examination results into an AI model and cause the AI ​​to perform analysis to select the optimal input method.

[0075] The reception unit filters the health check results based on the user's current living situation and areas of interest when the health check results are input. For example, when the user is on a diet, the reception unit preferentially inputs related health check results. In addition, when the user is interested in a specific disease, the reception unit can also highlight items related to the disease. Furthermore, the reception unit can filter related health check results based on the user's lifestyle. For example, the reception unit collects data related to the user's living situation and areas of interest and uses the data for analysis. As a result, highly relevant data can be collected by filtering based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into an AI model and have the AI ​​perform filtering.

[0076] The reception unit estimates the user's emotion and determines the priority of the medical examination results to be input based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit inputs important items with priority. In addition, when the user is relaxed, the reception unit can input detailed items. Furthermore, when the user is in a hurry, the reception unit can input only the most important items. For example, the reception unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, or other technology. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important data can be collected with priority by determining the priority of input according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation and input priority determination.

[0077] When inputting the medical examination results, the reception unit preferentially inputs highly relevant results in consideration of the geographical location information of the user. For example, if the user lives at high altitude, the reception unit preferentially inputs items related to health risks specific to high altitude. In addition, if the user lives in an urban area, the reception unit can also preferentially input items related to health risks specific to cities. Furthermore, if the user lives in a rural area, the reception unit can also preferentially input items related to health risks specific to rural areas. For example, the reception unit collects the geographical location information of the user and uses it for analysis. In this way, data corresponding to health risks specific to the region can be collected by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user into an AI model and cause the AI ​​to perform analysis for preferentially inputting highly relevant results.

[0078] The reception unit analyzes the user's social media activity when inputting the medical examination result, and inputs the related result. For example, if the user frequently posts about health on social media, the reception unit suggests input items based on the content of the posts. In addition, if the user shows interest in a specific health topic, the reception unit can also preferentially input items related to the topic. Furthermore, the reception unit can analyze the user's health-related interest field from the user's social media activity and customize the input items. For example, the reception unit collects the user's social media activity and uses it for analysis. In this way, data based on the user's interest field can be collected by analyzing the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into an AI model and have the AI ​​perform analysis to input related results.

[0079] The menu generation unit estimates the user's emotion and adjusts the way of expressing the menu based on the estimated emotion of the user. For example, when the user is stressed, the menu generation unit generates a simple and easy-to-read menu. In addition, when the user is relaxed, the menu generation unit can also generate a menu with detailed recipes. Furthermore, when the user is excited, the menu generation unit can also generate a visually attractive menu. For example, the menu generation unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other technologies. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate menu can be provided by adjusting the way of expressing the menu according to the user's emotion. A part or all of the above-mentioned processing in the menu generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu generation unit can input a user's emotional data into the generation AI and have the generation AI estimate the emotions and adjust the way the menu is expressed.

[0080] The menu generation unit adjusts the level of detail of the menu based on the importance of the health check result when generating the menu. For example, the menu generation unit generates a detailed menu based on an item determined to be high risk in the health check result. The menu generation unit can also generate a menu with a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the menu generation unit can generate a simplified menu based on an item determined to be low risk in the health check result. For example, the menu generation unit evaluates the importance of the health check result and uses it for analysis. This allows the user to be provided with an optimal menu by adjusting the level of detail of the menu based on the importance of the health check result. A part or all of the above-mentioned processing in the menu generation unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generation unit can input importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the menu.

[0081] The menu generating unit applies different menu generating algorithms according to the categories of the health check results when generating the menu. The menu generating unit applies an algorithm for generating a menu with less salt based on, for example, the results related to blood pressure. The menu generating unit can also apply an algorithm for generating a menu with less sugar based on the results related to blood glucose levels. Furthermore, the menu generating unit can also apply an algorithm for generating a menu with less lipids based on the results related to cholesterol levels. For example, the menu generating unit evaluates the category of the health check results and uses it for analysis. This allows a more appropriate menu to be generated by applying different algorithms according to the categories of the health check results. Some or all of the above-mentioned processing in the menu generating unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generating unit can input category data of the health check results into an AI model and cause the AI ​​to perform analysis for applying different algorithms.

[0082] The menu generation unit estimates the user's emotion and adjusts the length of the menu based on the estimated user's emotion. For example, when the user is stressed, the menu generation unit generates a short and simple menu. In addition, when the user is relaxed, the menu generation unit can also generate a longer menu including detailed steps. Furthermore, when the user is excited, the menu generation unit can also generate a visually attractive menu. For example, the menu generation unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other technologies. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate menu can be provided by adjusting the length of the menu according to the user's emotion. A part or all of the above-mentioned processing in the menu generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the menu generation unit can input a user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the length of the menu.

[0083] The menu generation unit determines the priority of the menu based on the time of submission of the health check result when generating the menu. For example, if the health check result is recent, the menu generation unit generates the menu based on the result. Also, if the health check result is old, the menu generation unit generates the menu based on the result, but can lower the priority. Furthermore, the menu generation unit can adjust the order of menu generation based on the time of submission of the health check result. For example, the menu generation unit evaluates the time of submission of the health check result and uses it for analysis. As a result, by determining the priority of the menu based on the time of submission of the health check result, it is possible to provide a menu based on the latest data. Part or all of the above-mentioned processing in the menu generation unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generation unit can input data on the time of submission of the health check result into an AI model and cause AI to perform analysis to determine the priority of the menu.

[0084] The menu generation unit adjusts the order of the menu based on the relevance of the health check result when generating the menu. For example, the menu generation unit generates the most relevant menu with priority based on the items whose health check result is determined to be high risk. The menu generation unit also generates a relevant menu based on the items whose health check result is determined to be medium risk, but can also lower the priority. Furthermore, the menu generation unit can generate a less relevant menu based on the items whose health check result is determined to be low risk. For example, the menu generation unit evaluates the relevance of the health check result and uses it for analysis. Thereby, by adjusting the order of the menu based on the relevance of the health check result, a more relevant menu can be provided. A part or all of the above-mentioned processing in the menu generation unit may be performed, for example, using AI or may be performed without using AI. For example, the menu generation unit can input the relevance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the order of the menu.

[0085] The exercise menu generating unit estimates the user's emotion and adjusts the expression method of the exercise menu based on the estimated user's emotion. For example, when the user is stressed, the exercise menu generating unit generates a simple and easy-to-read exercise menu. In addition, when the user is relaxed, the exercise menu generating unit can also generate an exercise menu with detailed explanations. Furthermore, when the user is excited, the exercise menu generating unit can also generate a visually attractive exercise menu. For example, the exercise menu generating unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other technologies. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate exercise menu can be provided by adjusting the expression method of the exercise menu according to the user's emotion. A part or all of the above-mentioned processing in the exercise menu generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the exercise menu generation unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the way the exercise menu is expressed.

[0086] The exercise menu generating unit adjusts the level of detail of the exercise menu based on the importance of the health check result when generating the exercise menu. The exercise menu generating unit generates a detailed exercise menu based on, for example, an item determined to be high risk in the health check result. The exercise menu generating unit can also generate an exercise menu with a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the exercise menu generating unit can also generate a simplified exercise menu based on an item determined to be low risk in the health check result. For example, the exercise menu generating unit evaluates the importance of the health check result and uses it for analysis. As a result, the level of detail of the exercise menu can be adjusted based on the importance of the health check result, thereby providing an optimal exercise menu to the user. A part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI or may be performed without using AI. For example, the exercise menu generating unit can input the importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the exercise menu.

[0087] The exercise menu generating unit applies different exercise menu generating algorithms according to the categories of the health check results when generating the exercise menu. The exercise menu generating unit applies an algorithm for generating an exercise menu centered on aerobic exercise, for example, based on the results related to blood pressure. The exercise menu generating unit can also apply an algorithm for generating an exercise menu centered on muscle training, based on the results related to blood glucose levels. Furthermore, the exercise menu generating unit can also apply an algorithm for generating an exercise menu for fat burning, based on the results related to cholesterol levels. For example, the exercise menu generating unit evaluates the categories of the health check results and uses them for analysis. This allows a more appropriate exercise menu to be generated by applying different algorithms according to the categories of the health check results. Part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the exercise menu generating unit can input category data of the health check results into an AI model and cause the AI ​​to perform analysis for applying different algorithms.

[0088] The exercise menu generating unit estimates the user's emotion and adjusts the length of the exercise menu based on the estimated user's emotion. For example, when the user is stressed, the exercise menu generating unit generates a short and simple exercise menu. In addition, when the user is relaxed, the exercise menu generating unit can also generate a longer exercise menu including detailed procedures. Furthermore, when the user is excited, the exercise menu generating unit can also generate a visually attractive exercise menu. For example, the exercise menu generating unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other techniques. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate exercise menu can be provided by adjusting the length of the exercise menu according to the user's emotion. A part or all of the above-mentioned processing in the exercise menu generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the exercise menu generation unit can input the user's emotion data to the generation AI and have the generation AI estimate the emotion and adjust the length of the exercise menu.

[0089] The exercise menu generating unit determines the priority of the exercise menu based on the time of submission of the health check result when generating the exercise menu. For example, if the health check result is recent, the exercise menu generating unit generates the exercise menu based on the result. Also, if the health check result is old, the exercise menu generating unit generates the exercise menu based on the result, but can also lower the priority. Furthermore, the exercise menu generating unit can adjust the order of generation of the exercise menu based on the time of submission of the health check result. For example, the exercise menu generating unit evaluates the time of submission of the health check result and uses it for analysis. As a result, by determining the priority of the exercise menu based on the time of submission of the health check result, it is possible to provide an exercise menu based on the latest data. Part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the exercise menu generating unit can input the submission time data of the health check result into an AI model and cause AI to perform analysis to determine the priority of the exercise menu.

[0090] The exercise menu generating unit adjusts the order of the exercise menu based on the relevance of the health check result when generating the exercise menu. For example, the exercise menu generating unit generates the most relevant exercise menu preferentially based on the item whose health check result is determined to be high risk. In addition, the exercise menu generating unit generates a relevant exercise menu based on the item whose health check result is determined to be medium risk, but can also lower the priority. Furthermore, the exercise menu generating unit can generate a less relevant exercise menu based on the item whose health check result is determined to be low risk. For example, the exercise menu generating unit evaluates the relevance of the health check result and uses it for analysis. As a result, by adjusting the order of the exercise menu based on the relevance of the health check result, a more relevant exercise menu can be provided. Part or all of the above-mentioned processing in the exercise menu generating unit may be performed, for example, using AI or may be performed without using AI. For example, the exercise menu generating unit can input the relevance data of the health check result into an AI model and cause AI to perform analysis to adjust the order of the exercise menu.

[0091] The teaching unit estimates the user's emotion and adjusts the expression method of the teaching content based on the estimated user's emotion. For example, when the user is stressed, the teaching unit provides simple and easy-to-read teaching content. In addition, when the user is relaxed, the teaching unit can also provide teaching content with detailed explanations. Furthermore, when the user is excited, the teaching unit can also provide visually attractive teaching content. For example, the teaching unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other technologies. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the expression method of the teaching content according to the user's emotion, more appropriate teaching content can be provided. A part or all of the above-mentioned processing in the teaching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input a user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the way the instruction content is expressed.

[0092] The teaching unit adjusts the level of detail of the teaching contents based on the importance of the health check result during teaching. For example, the teaching unit provides detailed teaching contents based on an item determined to be high risk in the health check result. The teaching unit can also provide teaching contents with a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the teaching unit can also provide simplified teaching contents based on an item determined to be low risk in the health check result. For example, the teaching unit evaluates the importance of the health check result and uses it for analysis. This allows the level of detail of the teaching contents to be adjusted based on the importance of the health check result, thereby providing optimal teaching contents to the user. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the teaching contents.

[0093] The teaching unit applies different teaching algorithms according to the categories of the health check results during teaching. For example, the teaching unit applies an algorithm that teaches to limit salt intake based on the results related to blood pressure. The teaching unit can also apply an algorithm that teaches to limit carbohydrate intake based on the results related to blood glucose levels. Furthermore, the teaching unit can also apply an algorithm that teaches to limit lipid intake based on the results related to cholesterol levels. For example, the teaching unit evaluates the category of the health check results and uses it for analysis. This allows more appropriate teaching content to be provided by applying different algorithms according to the categories of the health check results. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input category data of the health check results into an AI model and cause the AI ​​to perform analysis to apply different algorithms.

[0094] The teaching unit estimates the user's emotion and adjusts the length of the teaching content based on the estimated user's emotion. For example, when the user is stressed, the teaching unit provides short and concise teaching content. Also, when the user is relaxed, the teaching unit can provide longer teaching content including detailed explanations. Furthermore, when the user is excited, the teaching unit can provide visually attractive teaching content. For example, the teaching unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other techniques. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the length of the teaching content according to the user's emotion, more appropriate teaching content can be provided. A part or all of the above-mentioned processing in the teaching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion and adjust the length of the instruction content.

[0095] The teaching unit determines the priority of the teaching contents based on the submission time of the health check result at the time of teaching. For example, when the health check result is the most recent, the teaching unit provides the teaching contents based on the result. Also, when the health check result is old, the teaching unit provides the teaching contents based on the result, but can lower the priority. Furthermore, the teaching unit can adjust the order of providing the teaching contents based on the submission time of the health check result. For example, the teaching unit evaluates the submission time of the health check result and uses it for analysis. In this way, by determining the priority of the teaching contents based on the submission time of the health check result, it is possible to provide the teaching contents based on the latest data. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the submission time data of the health check result into an AI model and cause the AI ​​to perform an analysis for determining the priority of the teaching contents.

[0096] The teaching unit adjusts the order of the teaching contents based on the relevance of the health check result at the time of teaching. For example, the teaching unit provides the most relevant teaching contents preferentially based on the items whose health check result is determined to be high risk. The teaching unit also provides the relevant teaching contents based on the items whose health check result is determined to be medium risk, but can also lower the priority. Furthermore, the teaching unit can also provide the less relevant teaching contents based on the items whose health check result is determined to be low risk. For example, the teaching unit evaluates the relevance of the health check result and uses it for analysis. Thereby, by adjusting the order of the teaching contents based on the relevance of the health check result, it is possible to provide more relevant teaching contents. Part or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the relevance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the order of the teaching contents.

[0097] The publishing unit estimates the user's emotion and adjusts the expression method of the published content based on the estimated user's emotion. For example, when the user is stressed, the publishing unit provides simple and easy-to-read published content. In addition, when the user is relaxed, the publishing unit can also provide published content with detailed explanations. Furthermore, when the user is excited, the publishing unit can also provide visually attractive published content. For example, the publishing unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other technologies. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the expression method of the published content according to the user's emotion, more appropriate published content can be provided. A part or all of the above-mentioned processing in the publishing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the disclosure unit can input the user's emotional data into the generation AI and have the generation AI estimate the emotions and adjust the way the public content is expressed.

[0098] The disclosure unit adjusts the level of detail of the disclosure content based on the importance of the health check result at the time of disclosure. The disclosure unit provides detailed disclosure content based on, for example, items determined to be high risk in the health check result. The disclosure unit can also provide disclosure content with a moderate level of detail based on items determined to be medium risk in the health check result. Furthermore, the disclosure unit can provide simplified disclosure content based on items determined to be low risk in the health check result. For example, the disclosure unit evaluates the importance of the health check result and uses it for analysis. This allows the disclosure content optimal to the user to be provided by adjusting the level of detail of the disclosure content based on the importance of the health check result. Part or all of the above-mentioned processing in the disclosure unit may be performed, for example, using AI or may be performed without using AI. For example, the disclosure unit can input the importance data of the health check result into an AI model and cause the AI ​​to perform analysis for adjusting the level of detail of the disclosure content.

[0099] The publishing unit estimates the user's emotion and adjusts the length of the published content based on the estimated user's emotion. For example, when the user is stressed, the publishing unit provides short and concise published content. In addition, when the user is relaxed, the publishing unit can also provide longer published content including detailed explanations. In addition, when the user is excited, the publishing unit can also provide visually attractive published content. For example, the publishing unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other technologies. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the length of the published content according to the user's emotion, more appropriate published content can be provided. A part or all of the above-mentioned processing in the publishing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the disclosure unit can input the user's emotional data into the generation AI and have the generation AI estimate the emotions and adjust the length of the public content.

[0100] The disclosure unit determines the priority of the disclosure contents based on the submission time of the health check results at the time of disclosure. For example, if the health check results are recent, the disclosure unit provides the disclosure contents based on the results with priority. In addition, if the health check results are old, the disclosure unit provides the disclosure contents based on the results, but can also lower the priority. Furthermore, the disclosure unit can adjust the order of providing the disclosure contents based on the submission time of the health check results. For example, the disclosure unit evaluates the submission time of the health check results and uses it for analysis. In this way, by determining the priority of the disclosure contents based on the submission time of the health check results, it is possible to provide the disclosure contents based on the latest data. Part or all of the above-mentioned processing in the disclosure unit may be performed, for example, using AI or may be performed without using AI. For example, the disclosure unit can input the submission time data of the health check results into an AI model and cause AI to perform analysis to determine the priority of the disclosure contents.

[0101] The reward granting unit estimates the user's emotion and adjusts the reward granting method based on the estimated user's emotion. For example, when the user is stressed, the reward granting unit immediately grants a reward. In addition, when the user is relaxed, the reward granting unit can also grant rewards in stages. Furthermore, when the user is excited, the reward granting unit can also provide a visually attractive reward. For example, the reward granting unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using techniques such as facial expression recognition, voice analysis, and text analysis. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate reward can be provided by adjusting the reward granting method according to the user's emotion. A part or all of the above-mentioned processing in the reward granting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reward granting unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the method of granting the reward.

[0102] The reward granting unit adjusts the level of detail of the reward based on the importance of the health check result when granting the reward. For example, the reward granting unit provides a detailed reward based on an item determined to be high risk in the health check result. The reward granting unit can also provide a moderate level of detail based on an item determined to be medium risk in the health check result. Furthermore, the reward granting unit can also provide a simplified reward based on an item determined to be low risk in the health check result. For example, the reward granting unit evaluates the importance of the health check result and uses it for analysis. This allows the user to be provided with an optimal reward by adjusting the level of detail of the reward based on the importance of the health check result. A part or all of the above-mentioned processing in the reward granting unit may be performed, for example, using AI or may be performed without using AI. For example, the reward granting unit can input the importance data of the health check result into an AI model and cause the AI ​​to perform an analysis for adjusting the level of detail of the reward.

[0103] The reward granting unit estimates the user's emotion and determines the priority order of rewards based on the estimated user's emotion. For example, when the user is stressed, the reward granting unit preferentially grants important rewards. In addition, when the user is relaxed, the reward granting unit can also grant detailed rewards in stages. Furthermore, when the user is excited, the reward granting unit can also preferentially grant visually attractive rewards. For example, the reward granting unit uses an emotion estimation algorithm to estimate the user's emotion. The emotion estimation algorithm is realized using, for example, facial expression recognition, voice analysis, text analysis, and other techniques. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate reward can be provided by determining the priority order of rewards according to the user's emotion. A part or all of the above-mentioned processing in the reward granting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reward granting unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotions and determine the priority of rewards.

[0104] The reward granting unit weights the reward based on the time of submission of the medical examination result when granting the reward. For example, if the medical examination result is recent, the reward granting unit weights the reward based on the result. In addition, if the medical examination result is old, the reward granting unit can also lighten the reward based on the result. Furthermore, the reward granting unit can adjust the weighting of the reward based on the time of submission of the medical examination result. For example, the reward granting unit evaluates the time of submission of the medical examination result and uses it for analysis. As a result, by weighting the reward based on the time of submission of the medical examination result, it is possible to provide a reward based on the latest data. Part or all of the above-mentioned processing in the reward granting unit may be performed, for example, using AI or may be performed without using AI. For example, the reward granting unit can input data on the time of submission of the medical examination result into an AI model and cause the AI ​​to perform analysis to adjust the weighting of the reward.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The health management system can also obtain the user's sleep data and manage the health based on this. For example, the system can monitor the user's sleep time and quality and adjust the meal plan and exercise menu based on this. For a user who is sleep-deprived, it can suggest a plan using ingredients with a relaxing effect and an exercise menu focusing on light stretching. For a user who is getting good quality sleep, it can suggest a plan that emphasizes energy replenishment and a high-intensity exercise menu. This makes it possible to manage the health according to the user's sleep state.

[0107] The health management system can also monitor the user's water intake and manage the health based on the monitoring. For example, if the user does not consume enough water, a notification is sent to encourage the user to hydrate. The system can also adjust the meal plan based on the water intake and suggest a plan that uses ingredients that help with hydration. Furthermore, the system can adjust the exercise menu according to the water intake and suggest exercises to prevent dehydration. This allows the system to manage the health according to the user's water intake.

[0108] The health management system can also monitor the user's stress level and manage the health of the user based on this. For example, if the user's stress level is high, it can suggest a menu using ingredients with a relaxing effect and an exercise menu aimed at relaxation. Also, if the stress level is low, it can suggest a menu that emphasizes energy replenishment and a high-intensity exercise menu. This makes it possible to manage the health of the user according to their stress level.

[0109] The health management system can also obtain allergy information about the user and manage the user's health based on this information. For example, if the user is allergic to a particular ingredient, a menu can be generated that avoids that ingredient. The system can also adjust the exercise menu based on the allergy information and suggest exercises that do not cause an allergic reaction. This makes it possible to manage the user's health based on their allergy information.

[0110] The health management system can also estimate the user's emotions and adjust the health management approach based on the estimated user's emotions. For example, if the user is feeling sad, the system can suggest a meal plan using ingredients that will lift the user's mood, or an exercise menu to help them change their mood. If the user is feeling happy, the system can suggest a meal plan or exercise menu to help them maintain that emotion. This makes it possible to manage health according to the user's emotions.

[0111] The health management system can also obtain the user's social activity data and manage the user's health based on this data. For example, if the user eats out frequently, the system can suggest healthy menus to choose when eating out. Also, if the user does not get enough exercise, the system can suggest exercise menus that are easy to incorporate into daily life. This makes it possible to manage the user's health according to their social activities.

[0112] The health management system can further estimate the user's emotions and adjust the health management feedback based on the estimated user's emotions. For example, if the user feels anxious, the system can provide feedback that gives a sense of security. Also, if the user feels confident, the system can provide feedback that reinforces the user's confidence. This allows feedback to be provided according to the user's emotions.

[0113] The health management system can also obtain the user's exercise history and perform health management based on this. For example, it can suggest an effective exercise menu based on the data of the user's past exercises. It can also adjust meal plans based on the exercise history and suggest meals that use ingredients that help replenish nutrients after exercise. This makes it possible to manage health according to the user's exercise history.

[0114] The health management system can further estimate the user's emotions and adjust the health management goal setting based on the estimated user's emotions. For example, if the user feels motivated, a challenging goal can be set. Also, if the user feels tired, a realistic and achievable goal can be set. This makes it possible to set goals according to the user's emotions.

[0115] The health management system can also obtain the user's dietary history and manage the user's health based on this. For example, a balanced meal plan can be proposed based on the data of the meals the user has eaten in the past. In addition, the system can adjust the exercise menu based on the dietary history and suggest exercises according to the calorie intake. This allows for health management based on the user's dietary history.

[0116] The process flow of the second embodiment will be briefly described below.

[0117] Step 1: The reception unit receives the results of the user's health check. The results of the user's health check include, but are not limited to, blood pressure, blood glucose level, cholesterol level, etc. The reception unit, for example, receives the results of the health check input by the user in digital format. The reception unit can also scan and digitize the results of the health check submitted on paper. For example, the reception unit stores the results of the health check input by the user in a database and uses them for later analysis. Step 2: The menu generation unit uses the generation AI to generate a meal menu for the user based on the information received by the reception unit. The generated meal menu is configured, for example, based on the menus for breakfast, lunch, and dinner and nutritional balance, but is not limited to such an example. For example, the menu generation unit uses a text generation AI (e.g., LLM) to generate a meal menu that is optimal for the user's health condition. The menu generation unit can also generate images and recipes for the meal menu using a multimodal generation AI. For example, the menu generation unit suggests low-salt meals and low-carbohydrate meals based on the user's health check results. Step 3: The exercise menu generation unit uses the generation AI to generate an exercise menu for the user based on the information received by the reception unit. The generated exercise menu includes, for example, aerobic exercise, strength training, stretching, etc., but is not limited to such examples. For example, the exercise menu generation unit uses a text generation AI to generate an exercise menu that is optimal for the user's health condition. In addition, the exercise menu generation unit can also generate videos and images of the exercise menu using a multimodal generation AI. For example, the exercise menu generation unit suggests an exercise menu centered on aerobic exercise and strength training based on the user's health check results. Step 4: The teaching unit uses the generation AI to teach the user's child about points to be careful about based on the information accepted by the acceptance unit. The contents of the instruction include, but are not limited to, points to be careful about regarding diet, exercise, and lifestyle habits. For example, the teaching unit uses the text generation AI to advise the user's child to limit their sugar intake. The teaching unit can also use the multimodal generation AI to generate videos and images of the points to be careful about. For example, the teaching unit advises the child to limit their salt intake based on the results of the user's health check.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0120] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0121] For example, the reception unit is realized by either the smart device 14 or the data processing device 12. For example, the reception device 38 of the smart device 14 is used to input the user's health check results, and the specific processing unit 290 of the data processing device 12 analyzes the data. For example, the menu generation unit is realized by the specific processing unit 290 of the data processing device 12, and the generated meal menu is provided to the user through the output device 40 of the smart device 14. For example, the exercise menu generation unit is also realized by the specific processing unit 290 of the data processing device 12, and the exercise menu is displayed on the output device 40 of the smart device 14. For example, the instruction unit is realized by the specific processing unit 290 of the data processing device 12, and the user's attention to the child is provided through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0129] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0137] For example, the reception unit is realized by either the smart glasses 214 or the data processing device 12. For example, the user's health check results are input using the microphone 238 of the smart glasses 214, and the specific processing unit 290 of the data processing device 12 analyzes the data. For example, the menu generation unit is realized by the specific processing unit 290 of the data processing device 12, and the generated meal menu is provided to the user through the speaker 240 of the smart glasses 214. For example, the exercise menu generation unit is also realized by the specific processing unit 290 of the data processing device 12, and the exercise menu is displayed on the speaker 240 of the smart glasses 214. For example, the instruction unit is realized by the specific processing unit 290 of the data processing device 12, and the user's attention to the child is provided through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] For example, the reception unit is realized by either the headset type terminal 314 or the data processing device 12. For example, the user's health check results are input using the microphone 238 of the headset type terminal 314, and the specific processing unit 290 of the data processing device 12 analyzes the data. For example, the menu generation unit is realized by the specific processing unit 290 of the data processing device 12, and the generated meal menu is provided to the user through the display 343 of the headset type terminal 314. For example, the exercise menu generation unit is also realized by the specific processing unit 290 of the data processing device 12, and the exercise menu is displayed on the display 343 of the headset type terminal 314. For example, the instruction unit is realized by the specific processing unit 290 of the data processing device 12, and the user's attention to the child is provided through the display 343 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0165] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] For example, the reception unit is realized by either the robot 414 or the data processing device 12. For example, the user's health check results are input using the microphone 238 of the robot 414, and the specific processing unit 290 of the data processing device 12 analyzes the data. For example, the menu generation unit is realized by the specific processing unit 290 of the data processing device 12, and the generated meal menu is provided to the user through the speaker 240 of the robot 414. For example, the exercise menu generation unit is also realized by the specific processing unit 290 of the data processing device 12, and the exercise menu is displayed on the speaker 240 of the robot 414. For example, the instruction unit is realized by the specific processing unit 290 of the data processing device 12, and the user's attention to the child is provided through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0172] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0176] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0178] 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.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0181] 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.

[0182] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0183] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0187] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0189] (Appendix 1) A reception unit that receives a result of a health check of a user; A menu creation unit that creates a meal menu for the user based on the information accepted by the accepting unit; an exercise menu generation unit that generates an exercise menu for the user based on the information received by the reception unit; and an instruction unit that instructs the user on points to be careful about based on the information received by the reception unit. A system characterized by: (Appendix 2) The menu generation unit is Equipped with a publishing section that publishes the generated meal menu on a recipe site 2. The system of claim 1, (Appendix 3) The disclosure section includes: A reward awarding unit that awards a reward to the user based on the number of times the meal plan is viewed on a recipe site. 3. The system of claim 2, (Appendix 4) The reception unit is A system that estimates a user's emotions and adjusts the timing of inputting health check results based on the estimated user's emotions. (Appendix 5) The reception unit is Analyze the user's past health checkup results and select the appropriate input method 2. The system of claim 1, (Appendix 6) The reception unit is When entering health check results, filtering is performed based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 7) The reception unit is A system that estimates a user's emotions and determines the priority of input health check results based on the estimated user's emotions. (Appendix 8) The reception unit is When entering medical examination results, the system takes into account the user's geographic location information and prioritizes the most relevant results. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's social media activity and input relevant results when entering health check results 2. The system of claim 1, (Appendix 10) The menu generation unit is Estimate the user's emotions and adjust the way the menu is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 11) The menu generation unit is When generating a menu, adjust the level of detail of the menu based on the importance of the health check results. 2. The system of claim 1, (Appendix 12) The menu generation unit is When generating a menu, apply different menu generation algorithms depending on the category of the health check result. 2. The system of claim 1, (Appendix 13) The menu generation unit is Estimate the user's emotions and adjust the length of the menu based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The menu generation unit is When generating menus, prioritize menus based on when health check results are submitted 2. The system of claim 1, (Appendix 15) The menu generation unit is When generating a menu, adjust the order of the menu based on the relevance of the health check results. 2. The system of claim 1, (Appendix 16) The exercise menu generation unit Estimating a user's emotions and adjusting the presentation of an exercise menu based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The exercise menu generation unit When generating an exercise menu, adjust the level of detail of the exercise menu based on the importance of the health check results. 2. The system of claim 1, (Appendix 18) The exercise menu generation unit When generating exercise menus, different exercise menu generation algorithms are applied depending on the category of health check results. 2. The system of claim 1, (Appendix 19) The exercise menu generation unit To estimate a user's emotion and adjust the length of an exercise menu based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The exercise menu generation unit When generating an exercise menu, determine the priority of the exercise menu based on the time of submission of the health checkup results. 2. The system of claim 1, (Appendix 21) The exercise menu generation unit When generating an exercise menu, adjust the order of the exercise menu based on the relevance of the health check results. 2. The system of claim 1, (Appendix 22) The teaching unit includes: To estimate a user's emotion and adjust a method of expressing instruction contents based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The teaching unit includes: During instruction, the level of detail of the instruction is adjusted based on the importance of the medical examination results. 2. The system of claim 1, (Appendix 24) The teaching unit includes: During training, different training algorithms are applied depending on the category of the medical examination result. 2. The system of claim 1, (Appendix 25) The teaching unit includes: To estimate a user's emotion and adjust the length of a teaching content based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The teaching unit includes: When issuing instructions, the priority of the instructions will be determined based on the time of submission of the medical examination results. 2. The system of claim 1, (Appendix 27) The teaching unit includes: During instruction, the order of instruction contents is adjusted based on the relevance of the medical examination results. 2. The system of claim 1, (Appendix 28) The disclosure section includes: Estimate the user's emotions and adjust the way the published content is expressed based on the estimated user's emotions 3. The system of claim 2, (Appendix 29) The disclosure section includes: When publishing, adjust the level of detail of the published content based on the importance of the health examination results. 3. The system of claim 2, (Appendix 30) The disclosure section includes: Estimate the user's emotions and adjust the length of the published content based on the estimated user emotions 3. The system of claim 2, (Appendix 31) The disclosure section includes: At the time of disclosure, the prioritization of disclosure contents will be determined based on the timing of submission of medical examination results. 3. The system of claim 2, (Appendix 32) The reward granting unit: Estimating a user's emotions and adjusting the reward method based on the estimated user's emotions 4. The system of claim 3. (Appendix 33) The reward granting unit: When awarding rewards, adjust the level of detail of the reward based on the importance of the health check results. 4. The system of claim 3. (Appendix 34) The reward granting unit: Estimate the user's emotions and prioritize rewards based on the estimated user emotions. 4. The system of claim 3. (Appendix 35) The reward granting unit: When awarding rewards, the rewards will be weighted based on the time of submission of the medical examination results. 4. The system of claim 3. [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives a result of a health check of a user; A menu creation unit that creates a meal menu for the user based on the information accepted by the accepting unit; an exercise menu generation unit that generates an exercise menu for the user based on the information received by the reception unit; and an instruction unit that instructs the user on points to be careful about based on the information received by the reception unit. A system characterized by:

2. The menu generation unit is Equipped with a publishing section that publishes the generated meal menu on a recipe site 2. The system of claim 1 .

3. The disclosure section includes: A reward awarding unit that awards a reward to the user based on the number of times the meal plan is viewed on a recipe site.

3. The system of claim 2.

4. The reception unit is The feeling of the user is estimated, and a timing for inputting a medical examination result is adjusted based on the estimated feeling of the user.

2. The system of claim 1 .

5. The reception unit is Analyzing the user's past health checkup results and selecting an appropriate input method 2. The system of claim 1 .

6. The reception unit is When entering health checkup results, filtering is performed based on the user's current living situation and areas of interest.

2. The system of claim 1 .

7. The reception unit is Estimating the user's emotion and determining a priority order of the medical examination results to be input based on the estimated emotion of the user.

2. The system of claim 1 .

8. The reception unit is When inputting the medical examination results, the user's geographical location information is taken into consideration to preferentially input the results that are highly relevant.

2. The system of claim 1 .

9. The reception unit is When the health check results are entered, the social media activity of the user is analyzed and relevant results are entered.

2. The system of claim 1 .

10. The menu generation unit is The user's emotion is estimated, and a method of expressing the menu is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

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