System

Abstract

An object of a system according to an embodiment is to present an appropriate meal menu on the basis of a user's health condition and meal contents.SOLUTION: A system includes an analysis part, a presentation part, and a health condition consideration part. The analysis unit analyzes meal contents, exercise contents, and weight data of the user. The presentation unit presents a meal menu based on a result of the analysis by the analysis unit. The health condition consideration unit analyzes the examination data input by the user and presents a meal menu in consideration of a specific health condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately presented appropriate meal menus based on the user's health condition and dietary content, and there is room for improvement.

[0005] The system according to the embodiment aims to present an appropriate meal menu based on the user's health condition and dietary content. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a presentation unit, and a health condition consideration unit. The analysis unit analyzes the user's dietary details, exercise details, and weight data. The presentation unit presents a meal menu based on the results of the analysis by the analysis unit. The health condition consideration unit analyzes test data entered by the user and presents a meal menu that takes into account the user's specific health condition. [Effects of the Invention]

[0007] The system according to the embodiment can present an appropriate meal menu based on the user's health condition and dietary content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A diet support system according to an embodiment of the present invention analyzes a user's dietary, exercise, and weight data and presents an appropriate meal menu. The diet support system can also analyze test data entered by the user and present a meal menu that takes specific health conditions into consideration. For example, the diet support system allows the user to register meals, exercise, and weight. For example, the user enters detailed information about breakfast, lunch, and dinner, and records their exercise and weight. The diet support system then analyzes the user's registered data and presents an appropriate meal menu. For example, the diet support system proposes meal menus including home-cooked meals, restaurant meals, and convenience store meals. Furthermore, the diet support system presents meal menus that take into consideration health conditions such as diabetes and kidney disorders based on the user's test data. For example, a meal menu for blood sugar level management is proposed based on diabetes test data. This allows the diet support system to present meal menus tailored to the user's health condition and lifestyle, thereby supporting effective dieting. This allows the diet support system to present meal menus tailored to the user's health condition and lifestyle, thereby supporting effective dieting. For example, even if the user is too busy to cook at home, the system suggests healthy meal menus that can be purchased at a nearby convenience store. When dining out, the app also suggests healthy options to order at restaurants, allowing users to choose meals that are optimal for their health and maximize the effectiveness of their diet.

[0029] A diet support system according to an embodiment includes an analysis unit, a presentation unit, and a health condition consideration unit. The analysis unit analyzes a user's dietary content, exercise content, and weight data. For example, the analysis unit analyzes calorie intake and nutrient balance based on the dietary content entered by the user. The analysis unit can also analyze calories burned based on the user's exercise content. The analysis unit can also analyze weight fluctuations based on the user's weight data. For example, the analysis unit calculates calorie intake based on the user's dietary content and evaluates nutrient balance. The analysis unit can also calculate calories burned based on the user's exercise content and evaluate the effectiveness of the exercise. The analysis unit can also analyze weight fluctuations based on the user's weight data and evaluate the progress of the diet. The presentation unit presents meal menus based on the results of the analysis by the analysis unit. For example, the presentation unit suggests home-cooked meals, restaurant meals, and convenience store meals based on the user's health condition and diet progress. The presentation unit can also suggest healthy meal menus that can be purchased at a nearby convenience store if the user is too busy to cook at home. Furthermore, when eating out, the presentation unit can also suggest healthy menus that can be ordered at restaurants. For example, the presentation unit can suggest low-calorie, nutritionally balanced meal menus based on the user's health condition. The presentation unit can also suggest easy-to-prepare meal menus that suit the user's lifestyle. The health condition consideration unit analyzes test data entered by the user and presents meal menus that take into account a specific health condition. For example, the health condition consideration unit can suggest a meal menu for managing blood sugar levels based on diabetes test data entered by the user. The health condition consideration unit can also suggest a meal menu that does not put a strain on the kidneys based on kidney failure test data entered by the user. For example, the health condition consideration unit can suggest a meal menu that limits the intake of specific nutrients based on the user's test data. The health condition consideration unit can also suggest a meal menu that uses specific ingredients based on the user's health condition. As a result, the diet support system according to the embodiment can present meal menus that suit the user's health condition and lifestyle, thereby supporting effective dieting.

[0030] The analysis unit analyzes the user's dietary details, exercise details, and weight data to determine the user's health condition and diet progress. The analysis unit, for example, analyzes the calorie intake and nutrient balance based on the user's dietary details. For example, the analysis unit calculates the calorie intake and evaluates the nutrient balance based on the dietary details entered by the user. The analysis unit can also analyze the calories burned based on the user's exercise details. For example, the analysis unit calculates the calories burned based on the type and duration of exercise performed by the user and evaluates the effectiveness of the exercise. The analysis unit can also analyze weight fluctuations based on the user's weight data. For example, the analysis unit analyzes weight fluctuations based on the weight data entered by the user and evaluates the diet progress. This allows the user's health condition and diet progress to be accurately understood. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform analysis using an AI model that inputs the user's dietary details, exercise details, and weight data and outputs the user's health condition and diet progress.

[0031] The presentation unit can present a meal menu, including home-cooked meals, meals from restaurants, and meals from convenience stores, based on the results of the analysis unit. The presentation unit can suggest home-cooked meals, meals from restaurants, and meals from convenience stores, for example, depending on the user's health condition and diet progress. For example, if the user is too busy to cook at home, the presentation unit can suggest healthy meal menus that can be purchased at a nearby convenience store. In addition, if the user is eating out, the presentation unit can also suggest healthy menus that can be ordered at a restaurant. For example, the presentation unit can suggest low-calorie, nutritionally balanced meal menus depending on the user's health condition. In addition, the presentation unit can suggest meal menus that are easy to prepare to suit the user's lifestyle. This makes it possible to provide a meal menu that suits the user's lifestyle. Some or all of the above-described processing by the presentation unit may be performed, for example, using AI, or may be performed without AI. For example, the presentation unit can present a meal menu using an AI model that inputs the results of the analysis unit and outputs a meal menu.

[0032] The health condition consideration unit can analyze test data entered by the user and present a meal menu that takes into account specific health conditions such as diabetes and kidney damage. For example, the health condition consideration unit can propose a meal menu for managing blood glucose levels based on diabetes test data entered by the user. For example, the health condition consideration unit can propose a low-carbohydrate meal menu that stabilizes blood glucose levels based on the user's blood glucose level data. The health condition consideration unit can also propose a meal menu that does not burden the kidneys based on kidney damage test data entered by the user. For example, the health condition consideration unit can propose a low-protein, kidney-friendly meal menu based on the user's creatinine level and GFR value. This makes it possible to provide a meal menu tailored to a specific health condition. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without AI. For example, the health condition consideration unit can present a meal menu using an AI model that inputs the user's test data and outputs a meal menu that takes the user's health condition into consideration.

[0033] The presentation unit can suggest healthy meal menus that can be purchased at a nearby convenience store when the user is unable to cook at home. For example, when the user is too busy to cook at home, the presentation unit can suggest healthy meal menus that can be purchased at a nearby convenience store. For example, the presentation unit can suggest low-calorie, nutritionally balanced meal menus that can be purchased at a convenience store. The presentation unit can also suggest meal menus that contain specific nutrients depending on the user's health condition. For example, the presentation unit can suggest low-carbohydrate meal menus that can be purchased at a convenience store. This makes it possible to provide healthy meal menus even to busy users. Some or all of the above-mentioned processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can suggest meal menus using an AI model that inputs the user's situation and outputs meal menus that can be purchased at a convenience store.

[0034] In a situation where the user is eating out, the presentation unit can suggest healthy menu items that can be ordered at a restaurant. For example, when the user is eating out, the presentation unit suggests healthy menu items that can be ordered at a restaurant. For example, the presentation unit suggests low-calorie, nutritionally balanced menu items served at restaurants. The presentation unit can also suggest menu items containing specific nutrients depending on the user's health condition. For example, the presentation unit suggests low-carbohydrate menu items served at restaurants. This makes it possible to provide healthy meal menu items even when eating out. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can suggest meal menu items using an AI model that inputs the user's situation and outputs meal menu items that can be ordered at a restaurant.

[0035] The analysis unit can analyze the user's past dietary details, exercise details, and weight data and select an analysis method. The analysis unit, for example, analyzes the calorie balance based on the user's past calorie intake and exercise volume. For example, the analysis unit calculates the calorie balance based on the user's past dietary details and exercise details. The analysis unit can also analyze the user's past weight fluctuations and grasp weight management trends. For example, the analysis unit analyzes weight fluctuations based on the user's past weight data and grasps weight management trends. The analysis unit can also analyze the nutritional balance based on the user's past dietary details. For example, the analysis unit evaluates the nutrient balance based on the user's past dietary details. This allows for selecting the optimal analysis method based on the user's past data, thereby providing more accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can select an analysis method using an AI model that inputs the user's past data and selects an analysis method.

[0036] During analysis, the analysis unit can perform filtering based on the user's current living situation and health condition. For example, when the user inputs their current living situation, the analysis unit performs analysis based on that information. For example, the analysis unit selects an appropriate analysis method based on the user's current living situation. The analysis unit can also select an appropriate analysis method taking the user's health condition into consideration. For example, the analysis unit selects an appropriate analysis method based on the user's health condition. The analysis unit can also adjust the analysis results to match the user's lifestyle. For example, the analysis unit adjusts the analysis results based on the user's lifestyle. This makes it possible to provide analysis results that match the user's current situation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's current living situation and health condition and perform filtering using an AI model that performs filtering.

[0037] The analysis unit can select the optimal analysis means depending on the user's input method during analysis. For example, when the user inputs by voice, the analysis unit performs analysis using voice recognition technology. For example, when the user inputs by voice, the analysis unit performs analysis using voice recognition technology. Furthermore, when the user inputs by text, the analysis unit can also perform analysis using natural language processing technology. For example, when the user inputs by text, the analysis unit can perform analysis using natural language processing technology. Furthermore, when the user inputs by image, the analysis unit can also perform analysis using image recognition technology. For example, when the user inputs by image, the analysis unit can perform analysis using image recognition technology. This allows for selecting the optimal analysis means depending on the user's input method, thereby providing more accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can select the analysis means using an AI model that uses the user's input method as input and selects the optimal analysis means.

[0038] During analysis, the analysis unit can prioritize highly relevant data by taking into account the user's geographical location information. For example, when the user inputs their current location, the analysis unit performs analysis by taking into account local ingredients. For example, the analysis unit performs analysis based on the user's current location, taking into account local ingredients. The analysis unit can also analyze menus of nearby restaurants and convenience stores based on the user's location information. For example, the analysis unit analyzes menus of nearby restaurants and convenience stores based on the user's location information. The analysis unit can also perform analysis based on the user's location information, taking into account regional eating habits. For example, the analysis unit performs analysis based on the user's location information, taking into account regional eating habits. This makes it possible to provide more relevant analysis results by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs the user's geographical location information and prioritizes analysis of highly relevant data.

[0039] During the analysis, the analysis unit can analyze the user's social media activity and analyze related data. The analysis unit, for example, analyzes the meal details posted by the user on social media. For example, the analysis unit analyzes the meal details based on the user's social media posts. The analysis unit can also analyze the user's exercise records on social media. For example, the analysis unit analyzes the exercise details based on the user's exercise records on social media. The analysis unit can also analyze the user's weight fluctuations on social media. For example, the analysis unit analyzes weight data based on the user's weight fluctuations on social media. This makes it possible to provide more relevant analysis results by taking the user's social media activity into consideration. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the user's social media activity and analyzes related data.

[0040] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. The analysis unit, for example, adjusts the analysis method based on feedback provided by the user in the past. For example, the analysis unit adjusts the analysis method based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis results by reflecting the user's past feedback. For example, the analysis unit improves the accuracy of the analysis results based on the user's past feedback. The analysis unit can also adjust the analysis priority based on the user's feedback. For example, the analysis unit adjusts the analysis priority based on the user's feedback. In this way, by reflecting the user's past feedback, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can customize the analysis method using an AI model that uses the user's past feedback as input and customizes the analysis method.

[0041] The presentation unit can adjust the level of detail of the presentation based on the importance of the meal menu when presenting the menu. For example, the presentation unit presents detailed nutritional information and cooking methods for an important meal menu. For example, the presentation unit presents detailed nutritional information and cooking methods for an important meal menu. The presentation unit can also present concise information for a meal menu with low importance. For example, the presentation unit presents concise information for a meal menu with low importance. The presentation unit can also present detailed analysis results for a meal menu that affects the user's health condition. For example, the presentation unit presents detailed analysis results for a meal menu that affects the user's health condition. This makes it possible to provide optimal information to the user by presenting the meal menu with a level of detail according to its importance. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can adjust the level of detail using an AI model that inputs the importance of the meal menu and adjusts the level of detail of the presentation.

[0042] The presentation unit can apply different presentation algorithms depending on the category of the meal menu when presenting the information. For example, in the case of a home-cooked meal menu, the presentation unit presents detailed recipes and cooking instructions. For example, in the case of a home-cooked meal menu, the presentation unit presents detailed recipes and cooking instructions. In addition, in the case of a restaurant menu, the presentation unit can also present the location of the restaurant and details of the menu. For example, in the case of a restaurant menu, the presentation unit presents the location of the restaurant and details of the menu. In addition, in the case of a convenience store meal, the presentation unit can also present where to purchase the meal and nutritional information. For example, in the case of a convenience store meal, the presentation unit presents where to purchase the meal and nutritional information. In this way, by applying a presentation algorithm depending on the category of the meal menu, more appropriate information can be provided. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can apply the presentation algorithm using an AI model that inputs the category of the meal menu and applies the presentation algorithm.

[0043] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. For example, the presentation unit presents a menu that matches the user's preferences based on menus selected in the past. For example, the presentation unit presents a menu that matches the user's preferences based on the user's past selection history. The presentation unit can also adjust the presentation content by reflecting the user's past feedback. For example, the presentation unit adjusts the presentation content based on the user's past feedback. The presentation unit can also suggest an optimal menu based on the user's past selection history. For example, the presentation unit suggests an optimal menu based on the user's past selection history. This makes it possible to provide information with higher accuracy by referring to the user's past presentation results. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can improve accuracy by using an AI model that uses the user's past presentation results as input and improves the presentation accuracy.

[0044] The presentation unit, when presenting the menu, can determine a presentation priority based on the time of submission of the meal menu. For example, in the case of a breakfast menu, the presentation unit prioritizes presentation in the morning. For example, in the case of a breakfast menu, the presentation unit prioritizes presentation in the morning. The presentation unit can also prioritize presentation in the case of a lunch menu during the afternoon. For example, the presentation unit prioritizes presentation in the case of a lunch menu during the afternoon. The presentation unit can also prioritize presentation in the case of a dinner menu during the evening. For example, the presentation unit prioritizes presentation in the case of a dinner menu during the evening. In this way, by determining the priority based on the time of submission of the meal menu, information can be provided at a more appropriate time. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can determine the priority using an AI model that determines the presentation priority using the time of submission of the meal menu as input.

[0045] The presentation unit can adjust the order of presentation based on the relevance of the meal menus when presenting them. For example, the presentation unit prioritizes presenting a menu that is most relevant to the user's health condition. For example, the presentation unit prioritizes presenting a menu that is most relevant to the user's health condition. The presentation unit can also prioritize presenting a menu that is highly relevant based on the user's past selection history. For example, the presentation unit prioritizes presenting a menu that is highly relevant based on the user's past selection history. The presentation unit can also prioritize presenting a menu that is highly relevant based on the user's current living situation. For example, the presentation unit prioritizes presenting a menu that is highly relevant based on the user's current living situation. By adjusting the order based on the relevance of the meal menus, more relevant information can be provided. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the order using an AI model that uses the relevance of the meal menus as input and adjusts the presentation order.

[0046] The presentation unit can adjust the use of technical terms used in the presentation depending on the user's level of expertise. For example, if the user has technical expertise, the presentation unit uses detailed technical terms to present the information. For example, the presentation unit uses detailed technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the presentation unit can provide explanations in simple terms. For example, the presentation unit provides explanations in simple terms based on the user's level of expertise. Furthermore, the presentation unit can select and present appropriate technical terms depending on the user's level of knowledge. For example, the presentation unit selects and presents appropriate technical terms based on the user's level of knowledge. This allows for the provision of information that is easier to understand by adjusting the use of technical terms depending on the user's level of expertise. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can adjust the use of technical terms using an AI model that uses the user's level of expertise as input.

[0047] When analyzing the health condition, the health condition consideration unit can predict the current health condition by referring to past test data. The health condition consideration unit, for example, predicts the current blood glucose level based on the user's past blood glucose level data. For example, the health condition consideration unit predicts the current blood glucose level based on the user's past blood glucose level data. The health condition consideration unit can also predict the current renal function based on the user's past renal function data. For example, the health condition consideration unit predicts the current renal function based on the user's past renal function data. The health condition consideration unit can also predict the current blood pressure based on the user's past blood pressure data. For example, the health condition consideration unit predicts the current blood pressure based on the user's past blood pressure data. In this way, by referring to the past test data, the current health condition can be predicted more accurately. Some or all of the above-mentioned processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can predict the health condition using an AI model that uses past test data as input and predicts the current health condition.

[0048] When analyzing the health condition, the health condition consideration unit can apply different analysis methods depending on the user's health condition. For example, if the user has diabetes, the health condition consideration unit applies an analysis method specialized for blood glucose level management. For example, if the user has diabetes, the health condition consideration unit applies an analysis method specialized for blood glucose level management. Furthermore, if the user has kidney failure, the health condition consideration unit can also apply an analysis method specialized for renal function management. For example, if the user has kidney failure, the health condition consideration unit applies an analysis method specialized for renal function management. Furthermore, if the user has high blood pressure, the health condition consideration unit can also apply an analysis method specialized for blood pressure management. For example, if the user has high blood pressure, the health condition consideration unit applies an analysis method specialized for blood pressure management. In this way, by applying an analysis method according to the user's health condition, more appropriate health condition analysis is possible. Some or all of the above-mentioned processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can apply an analysis method using an AI model that inputs the user's health condition and applies an appropriate analysis method.

[0049] The health condition consideration unit can perform analysis by taking into account the user's attribute information when analyzing the health condition. The health condition consideration unit selects an appropriate analysis method, for example, by taking into account the user's age. For example, the health condition consideration unit selects an appropriate analysis method based on the user's age. The health condition consideration unit can also select an appropriate analysis method by taking into account the user's gender. For example, the health condition consideration unit selects an appropriate analysis method based on the user's gender. The health condition consideration unit can also select an appropriate analysis method by taking into account the user's lifestyle habits. For example, the health condition consideration unit selects an appropriate analysis method based on the user's lifestyle habits. This enables more accurate analysis of the health condition by taking into account the user's attribute information. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can perform analysis using an AI model that uses the user's attribute information as input and performs analysis.

[0050] When analyzing the health status, the health status consideration unit can analyze changes in the analysis based on the timing of test data submission. The health status consideration unit, for example, analyzes changes in the health status based on test data periodically submitted by the user. For example, the health status consideration unit analyzes changes in the health status based on test data periodically submitted by the user. The health status consideration unit can also analyze changes in the health status based on test data submitted by the user after a specific event. For example, the health status consideration unit analyzes changes in the health status based on test data submitted by the user after a specific event. The health status consideration unit can also analyze trends in the health status based on test data submitted by the user over a long period of time. For example, the health status consideration unit analyzes trends in the health status based on test data submitted by the user over a long period of time. This enables more accurate analysis of the health status by analyzing changes in the analysis based on the timing of test data submission. Some or all of the above-described processing in the health status consideration unit may be performed using, for example, AI, or may be performed without AI. For example, the health status consideration unit can analyze changes using an AI model that uses the timing of test data submission as input and analyzes changes in the analysis.

[0051] The health condition consideration unit can perform the analysis by referring to related medical data when analyzing the health condition. The health condition consideration unit, for example, refers to the user's past medical records to analyze the health condition. For example, the health condition consideration unit analyzes the health condition based on the user's past medical records. The health condition consideration unit can also analyze the health condition by referring to information on medications the user is taking. For example, the health condition consideration unit analyzes the health condition based on information on medications the user is taking. The health condition consideration unit can also analyze genetic health risks by referring to the user's family history. For example, the health condition consideration unit analyzes genetic health risks based on the user's family history. This enables more accurate analysis of the health condition by referring to related medical data. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can perform the analysis using an AI model that uses related medical data as input and performs analysis.

[0052] The health condition consideration unit can perform the analysis by taking into account the user's lifestyle habits when analyzing the health condition. The health condition consideration unit, for example, analyzes the health condition by taking into account the user's eating habits. For example, the health condition consideration unit analyzes the health condition based on the user's eating habits. The health condition consideration unit can also analyze the health condition by taking into account the user's exercise habits. For example, the health condition consideration unit analyzes the health condition based on the user's exercise habits. The health condition consideration unit can also analyze the health condition by taking into account the user's sleeping habits. For example, the health condition consideration unit analyzes the health condition based on the user's sleeping habits. This enables a more accurate analysis of the health condition by taking the user's lifestyle habits into account. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can perform the analysis using an AI model that uses the user's lifestyle habits as input and performs analysis.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can not only analyze the user's dietary details, exercise details, and weight data, but also the user's sleep data. For example, the analysis unit can analyze sleep patterns based on the sleep duration and sleep quality input by the user. The analysis unit can also evaluate the effects of insufficient or excessive sleep based on the user's sleep data. This allows for a more comprehensive understanding of the user's overall health condition. Furthermore, the analysis unit can also suggest appropriate exercise and meal timings based on the user's sleep data. For example, the analysis unit can suggest exercises that the user should do after getting enough sleep or a meal menu that should be eaten before sleep. This enables health management that is tailored to the user's lifestyle.

[0055] The presentation unit not only presents meal menus based on the results of the analysis unit, but can also provide recipe videos that suit the user's preferences. For example, the presentation unit can suggest related recipe videos based on the user's preferred ingredients and types of dishes. The presentation unit can also provide simple recipe videos for beginners and detailed recipe videos for advanced cooks, depending on the user's cooking skill. This allows the user to enjoy cooking that suits their skill level. Furthermore, the presentation unit can also suggest recipe videos that can be made in a short amount of time or recipe videos that can be carefully tackled on the weekend, depending on the user's lifestyle. This makes it possible to provide meal menus that suit the user's lifestyle.

[0056] The health condition consideration unit not only analyzes the test data entered by the user, but can also suggest a meal menu taking into consideration the user's genetic information. For example, the health condition consideration unit can suggest when intake of specific nutrients is necessary or which ingredients should be avoided based on the user's genetic information. The health condition consideration unit can also suggest a meal menu for reducing genetic risks based on the user's genetic information. This enables health management that takes into consideration the user's genetic background. Furthermore, the health condition consideration unit can also suggest a meal menu that is effective in preventing specific diseases based on the user's genetic information. For example, if the user is genetically at risk of high blood pressure, the health condition consideration unit can suggest a low-salt meal menu. This enables health management that takes into consideration the user's genetic risks.

[0057] In a situation where the user is eating out, the presentation unit not only suggests healthy menu items that can be ordered at a restaurant, but can also customize the suggestions based on the user's dietary preferences. For example, the presentation unit suggests menu items from related restaurants based on the types of dishes and ingredients that the user prefers. The presentation unit can also suggest menu items that are safe to eat, taking into account the user's allergy information. This allows the user to enjoy eating out with peace of mind. Furthermore, the presentation unit can suggest affordable menu items based on the user's budget. For example, if the user inputs a budget, the presentation unit can suggest healthy menu items within that budget. This makes it possible to provide flexible meal menus that suit the user's financial situation.

[0058] The analysis unit not only analyzes the user's past dietary details, exercise details, and weight data, but can also select an analysis method taking the user's lifestyle into consideration. For example, if the user is a nocturnal person, the analysis unit can suggest a meal menu and exercise plan suitable for the night. Furthermore, if the user is a morning person, the analysis unit can suggest a meal menu and morning exercise plan suitable for breakfast. This enables health management that is tailored to the user's lifestyle. Furthermore, the analysis unit can suggest optimal sleep times and rest timings based on the user's lifestyle. For example, if the user is a nocturnal person, the analysis unit can suggest a nighttime rest time. This enables flexible health management that is tailored to the user's lifestyle.

[0059] During analysis, the analysis unit not only filters the data based on the user's current lifestyle and health status, but also customizes the analysis results according to the user's goals. For example, if the user's goal is weight loss, the analysis unit can suggest a calorie-restricted meal menu and a high-intensity exercise plan. Furthermore, if the user's goal is muscle building, the analysis unit can suggest a high-protein meal menu and a strength training plan. This enables health management according to the user's goals. Furthermore, the analysis unit can periodically evaluate the user's progress toward achieving their goals and adjust the analysis results as necessary. For example, if the user is approaching their goal, the analysis unit can suggest a maintenance plan for after achieving the goal. This enables flexible health management according to the user's goals.

[0060] During analysis, the analysis unit not only selects the optimal analysis means according to the user's input method, but can also adjust the analysis means according to the user's device environment. For example, if the user is using a smartphone, the analysis unit selects an analysis means optimized for the smartphone. Furthermore, if the user is using a wearable device, the analysis unit can also perform analysis based on data acquired from the wearable device. This enables flexible analysis according to the user's device environment. Furthermore, if the user is using multiple devices, the analysis unit can also integrate and analyze data acquired from each device. For example, if the user is using both a smartphone and a wearable device, the analysis unit can integrate and analyze data acquired from both devices. This enables comprehensive analysis according to the user's device environment.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The analysis unit analyzes the user's dietary details, exercise details, and weight data. For example, the analysis unit analyzes calorie intake and nutrient balance based on the dietary details entered by the user, analyzes calories burned based on the exercise details, and analyzes weight fluctuations based on the weight data. Step 2: The presentation unit presents a meal menu based on the results of the analysis by the analysis unit. For example, the presentation unit may suggest home-cooked meals, restaurant meals, and convenience store meal menus based on the user's health condition and diet progress. Step 3: The health condition consideration unit analyzes the test data entered by the user and presents a meal menu that takes into account the specific health condition. For example, the health condition consideration unit may propose a meal menu for managing blood sugar levels based on diabetes test data, or a meal menu that does not burden the kidneys based on kidney damage test data.

[0063] (Example 2) A diet support system according to an embodiment of the present invention analyzes a user's dietary, exercise, and weight data and presents an appropriate meal menu. The diet support system can also analyze test data entered by the user and present a meal menu that takes specific health conditions into consideration. For example, the diet support system allows the user to register meals, exercise, and weight. For example, the user enters detailed information about breakfast, lunch, and dinner, and records their exercise and weight. The diet support system then analyzes the user's registered data and presents an appropriate meal menu. For example, the diet support system proposes meal menus including home-cooked meals, restaurant meals, and convenience store meals. Furthermore, the diet support system presents meal menus that take into consideration health conditions such as diabetes and kidney disorders based on the user's test data. For example, a meal menu for blood sugar level management is proposed based on diabetes test data. This allows the diet support system to present meal menus tailored to the user's health condition and lifestyle, thereby supporting effective dieting. This allows the diet support system to present meal menus tailored to the user's health condition and lifestyle, thereby supporting effective dieting. For example, even if the user is too busy to cook at home, the system suggests healthy meal menus that can be purchased at a nearby convenience store. When dining out, the app also suggests healthy options to order at restaurants, allowing users to choose meals that are optimal for their health and maximize the effectiveness of their diet.

[0064] A diet support system according to an embodiment includes an analysis unit, a presentation unit, and a health condition consideration unit. The analysis unit analyzes a user's dietary content, exercise content, and weight data. For example, the analysis unit analyzes calorie intake and nutrient balance based on the dietary content entered by the user. The analysis unit can also analyze calories burned based on the user's exercise content. The analysis unit can also analyze weight fluctuations based on the user's weight data. For example, the analysis unit calculates calorie intake based on the user's dietary content and evaluates nutrient balance. The analysis unit can also calculate calories burned based on the user's exercise content and evaluate the effectiveness of the exercise. The analysis unit can also analyze weight fluctuations based on the user's weight data and evaluate the progress of the diet. The presentation unit presents meal menus based on the results of the analysis by the analysis unit. For example, the presentation unit suggests home-cooked meals, restaurant meals, and convenience store meals based on the user's health condition and diet progress. The presentation unit can also suggest healthy meal menus that can be purchased at a nearby convenience store if the user is too busy to cook at home. Furthermore, when eating out, the presentation unit can also suggest healthy menus that can be ordered at restaurants. For example, the presentation unit can suggest low-calorie, nutritionally balanced meal menus based on the user's health condition. The presentation unit can also suggest easy-to-prepare meal menus that suit the user's lifestyle. The health condition consideration unit analyzes test data entered by the user and presents meal menus that take into account a specific health condition. For example, the health condition consideration unit can suggest a meal menu for managing blood sugar levels based on diabetes test data entered by the user. The health condition consideration unit can also suggest a meal menu that does not put a strain on the kidneys based on kidney failure test data entered by the user. For example, the health condition consideration unit can suggest a meal menu that limits the intake of specific nutrients based on the user's test data. The health condition consideration unit can also suggest a meal menu that uses specific ingredients based on the user's health condition. As a result, the diet support system according to the embodiment can present meal menus that suit the user's health condition and lifestyle, thereby supporting effective dieting.

[0065] The analysis unit analyzes the user's dietary details, exercise details, and weight data to determine the user's health condition and diet progress. The analysis unit, for example, analyzes the calorie intake and nutrient balance based on the user's dietary details. For example, the analysis unit calculates the calorie intake and evaluates the nutrient balance based on the dietary details entered by the user. The analysis unit can also analyze the calories burned based on the user's exercise details. For example, the analysis unit calculates the calories burned based on the type and duration of exercise performed by the user and evaluates the effectiveness of the exercise. The analysis unit can also analyze weight fluctuations based on the user's weight data. For example, the analysis unit analyzes weight fluctuations based on the weight data entered by the user and evaluates the diet progress. This allows the user's health condition and diet progress to be accurately understood. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can perform analysis using an AI model that inputs the user's dietary details, exercise details, and weight data and outputs the user's health condition and diet progress.

[0066] The presentation unit can present a meal menu, including home-cooked meals, meals from restaurants, and meals from convenience stores, based on the results of the analysis unit. The presentation unit can suggest home-cooked meals, meals from restaurants, and meals from convenience stores, for example, depending on the user's health condition and diet progress. For example, if the user is too busy to cook at home, the presentation unit can suggest healthy meal menus that can be purchased at a nearby convenience store. In addition, if the user is eating out, the presentation unit can also suggest healthy menus that can be ordered at a restaurant. For example, the presentation unit can suggest low-calorie, nutritionally balanced meal menus depending on the user's health condition. In addition, the presentation unit can suggest meal menus that are easy to prepare to suit the user's lifestyle. This makes it possible to provide a meal menu that suits the user's lifestyle. Some or all of the above-described processing by the presentation unit may be performed, for example, using AI, or may be performed without AI. For example, the presentation unit can present a meal menu using an AI model that inputs the results of the analysis unit and outputs a meal menu.

[0067] The health condition consideration unit can analyze test data entered by the user and present a meal menu that takes into account specific health conditions such as diabetes and kidney damage. For example, the health condition consideration unit can propose a meal menu for managing blood glucose levels based on diabetes test data entered by the user. For example, the health condition consideration unit can propose a low-carbohydrate meal menu that stabilizes blood glucose levels based on the user's blood glucose level data. The health condition consideration unit can also propose a meal menu that does not burden the kidneys based on kidney damage test data entered by the user. For example, the health condition consideration unit can propose a low-protein, kidney-friendly meal menu based on the user's creatinine level and GFR value. This makes it possible to provide a meal menu tailored to a specific health condition. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without AI. For example, the health condition consideration unit can present a meal menu using an AI model that inputs the user's test data and outputs a meal menu that takes the user's health condition into consideration.

[0068] The presentation unit can suggest healthy meal menus that can be purchased at a nearby convenience store when the user is unable to cook at home. For example, when the user is too busy to cook at home, the presentation unit can suggest healthy meal menus that can be purchased at a nearby convenience store. For example, the presentation unit can suggest low-calorie, nutritionally balanced meal menus that can be purchased at a convenience store. The presentation unit can also suggest meal menus that contain specific nutrients depending on the user's health condition. For example, the presentation unit can suggest low-carbohydrate meal menus that can be purchased at a convenience store. This makes it possible to provide healthy meal menus even to busy users. Some or all of the above-mentioned processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can suggest meal menus using an AI model that inputs the user's situation and outputs meal menus that can be purchased at a convenience store.

[0069] In a situation where the user is eating out, the presentation unit can suggest healthy menu items that can be ordered at a restaurant. For example, when the user is eating out, the presentation unit suggests healthy menu items that can be ordered at a restaurant. For example, the presentation unit suggests low-calorie, nutritionally balanced menu items served at restaurants. The presentation unit can also suggest menu items containing specific nutrients depending on the user's health condition. For example, the presentation unit suggests low-carbohydrate menu items served at restaurants. This makes it possible to provide healthy meal menu items even when eating out. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can suggest meal menu items using an AI model that inputs the user's situation and outputs meal menu items that can be ordered at a restaurant.

[0070] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit delays the analysis until the user is relaxed. For example, the analysis unit estimates the user's emotions and delays the analysis when the user is feeling stressed. The analysis unit can also immediately perform the analysis and present the results when the user is relaxed. For example, the analysis unit estimates the user's emotions and immediately performs the analysis when the user is relaxed. The analysis unit can also quickly perform the analysis and provide the results in a short time when the user is in a hurry. For example, the analysis unit estimates the user's emotions and quickly performs the analysis when the user is in a hurry. This allows for adjusting the timing of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can adjust the timing of analysis using an AI model that takes user emotion data as input and outputs the timing of analysis.

[0071] The analysis unit can analyze the user's past dietary details, exercise details, and weight data and select an analysis method. The analysis unit, for example, analyzes the calorie balance based on the user's past calorie intake and exercise volume. For example, the analysis unit calculates the calorie balance based on the user's past dietary details and exercise details. The analysis unit can also analyze the user's past weight fluctuations and grasp weight management trends. For example, the analysis unit analyzes weight fluctuations based on the user's past weight data and grasps weight management trends. The analysis unit can also analyze the nutritional balance based on the user's past dietary details. For example, the analysis unit evaluates the nutrient balance based on the user's past dietary details. This allows for selecting the optimal analysis method based on the user's past data, thereby providing more accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can select an analysis method using an AI model that inputs the user's past data and selects an analysis method.

[0072] During analysis, the analysis unit can perform filtering based on the user's current living situation and health condition. For example, when the user inputs their current living situation, the analysis unit performs analysis based on that information. For example, the analysis unit selects an appropriate analysis method based on the user's current living situation. The analysis unit can also select an appropriate analysis method taking the user's health condition into consideration. For example, the analysis unit selects an appropriate analysis method based on the user's health condition. The analysis unit can also adjust the analysis results to match the user's lifestyle. For example, the analysis unit adjusts the analysis results based on the user's lifestyle. This makes it possible to provide analysis results that match the user's current situation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's current living situation and health condition and perform filtering using an AI model that performs filtering.

[0073] The analysis unit can select the optimal analysis means depending on the user's input method during analysis. For example, when the user inputs by voice, the analysis unit performs analysis using voice recognition technology. For example, when the user inputs by voice, the analysis unit performs analysis using voice recognition technology. Furthermore, when the user inputs by text, the analysis unit can also perform analysis using natural language processing technology. For example, when the user inputs by text, the analysis unit can perform analysis using natural language processing technology. Furthermore, when the user inputs by image, the analysis unit can also perform analysis using image recognition technology. For example, when the user inputs by image, the analysis unit can perform analysis using image recognition technology. This allows for selecting the optimal analysis means depending on the user's input method, thereby providing more accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can select the analysis means using an AI model that uses the user's input method as input and selects the optimal analysis means.

[0074] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes presenting meal menus that have a relaxing effect. For example, the analysis unit estimates the user's emotions and prioritizes presenting meal menus that have a relaxing effect when the user is feeling stressed. The analysis unit can also prioritize presenting nutritionally balanced meal menus when the user is relaxed. For example, the analysis unit estimates the user's emotions and prioritizes presenting nutritionally balanced meal menus when the user is relaxed. The analysis unit can also prioritize presenting easy-to-prepare meal menus when the user is in a hurry. For example, the analysis unit estimates the user's emotions and prioritizes presenting easy-to-prepare meal menus when the user is in a hurry. This allows the analysis results to be prioritized according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data and determine priorities using an AI model that determines the priorities of analysis results.

[0075] During analysis, the analysis unit can prioritize highly relevant data by taking into account the user's geographical location information. For example, when the user inputs their current location, the analysis unit performs analysis by taking into account local ingredients. For example, the analysis unit performs analysis based on the user's current location, taking into account local ingredients. The analysis unit can also analyze menus of nearby restaurants and convenience stores based on the user's location information. For example, the analysis unit analyzes menus of nearby restaurants and convenience stores based on the user's location information. The analysis unit can also perform analysis based on the user's location information, taking into account regional eating habits. For example, the analysis unit performs analysis based on the user's location information, taking into account regional eating habits. This makes it possible to provide more relevant analysis results by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs the user's geographical location information and prioritizes analysis of highly relevant data.

[0076] During the analysis, the analysis unit can analyze the user's social media activity and analyze related data. The analysis unit, for example, analyzes the meal details posted by the user on social media. For example, the analysis unit analyzes the meal details based on the user's social media posts. The analysis unit can also analyze the user's exercise records on social media. For example, the analysis unit analyzes the exercise details based on the user's exercise records on social media. The analysis unit can also analyze the user's weight fluctuations on social media. For example, the analysis unit analyzes weight data based on the user's weight fluctuations on social media. This makes it possible to provide more relevant analysis results by taking the user's social media activity into consideration. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the user's social media activity and analyzes related data.

[0077] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. The analysis unit, for example, adjusts the analysis method based on feedback provided by the user in the past. For example, the analysis unit adjusts the analysis method based on the user's past feedback. The analysis unit can also improve the accuracy of the analysis results by reflecting the user's past feedback. For example, the analysis unit improves the accuracy of the analysis results based on the user's past feedback. The analysis unit can also adjust the analysis priority based on the user's feedback. For example, the analysis unit adjusts the analysis priority based on the user's feedback. In this way, by reflecting the user's past feedback, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can customize the analysis method using an AI model that uses the user's past feedback as input and customizes the analysis method.

[0078] The presentation unit can estimate the user's emotions and adjust the presentation expression method based on the estimated user's emotions. For example, if the user is feeling stressed, the presentation unit employs a presentation method that is simple and visually relaxing. For example, the presentation unit estimates the user's emotions and employs a presentation method that is simple and visually relaxing when the user is feeling stressed. The presentation unit can also employ a presentation method that includes detailed information when the user is relaxed. For example, the presentation unit estimates the user's emotions and employs a presentation method that includes detailed information when the user is relaxed. The presentation unit can also employ a concise presentation method that focuses on the main points when the user is in a hurry. For example, the presentation unit estimates the user's emotions and employs a concise presentation method that focuses on the main points when the user is in a hurry. This enables more effective information provision by presenting information in an expression method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may adjust the presentation method using an AI model that receives user emotion data as input and adjusts the presentation method.

[0079] The presentation unit can adjust the level of detail of the presentation based on the importance of the meal menu when presenting the menu. For example, the presentation unit presents detailed nutritional information and cooking methods for an important meal menu. For example, the presentation unit presents detailed nutritional information and cooking methods for an important meal menu. The presentation unit can also present concise information for a meal menu with low importance. For example, the presentation unit presents concise information for a meal menu with low importance. The presentation unit can also present detailed analysis results for a meal menu that affects the user's health condition. For example, the presentation unit presents detailed analysis results for a meal menu that affects the user's health condition. This makes it possible to provide optimal information to the user by presenting the meal menu with a level of detail according to its importance. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can adjust the level of detail using an AI model that inputs the importance of the meal menu and adjusts the level of detail of the presentation.

[0080] The presentation unit can apply different presentation algorithms depending on the category of the meal menu when presenting the information. For example, in the case of a home-cooked meal menu, the presentation unit presents detailed recipes and cooking instructions. For example, in the case of a home-cooked meal menu, the presentation unit presents detailed recipes and cooking instructions. In addition, in the case of a restaurant menu, the presentation unit can also present the location of the restaurant and details of the menu. For example, in the case of a restaurant menu, the presentation unit presents the location of the restaurant and details of the menu. In addition, in the case of a convenience store meal, the presentation unit can also present where to purchase the meal and nutritional information. For example, in the case of a convenience store meal, the presentation unit presents where to purchase the meal and nutritional information. In this way, by applying a presentation algorithm depending on the category of the meal menu, more appropriate information can be provided. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can apply the presentation algorithm using an AI model that inputs the category of the meal menu and applies the presentation algorithm.

[0081] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. For example, the presentation unit presents a menu that matches the user's preferences based on menus selected in the past. For example, the presentation unit presents a menu that matches the user's preferences based on the user's past selection history. The presentation unit can also adjust the presentation content by reflecting the user's past feedback. For example, the presentation unit adjusts the presentation content based on the user's past feedback. The presentation unit can also suggest an optimal menu based on the user's past selection history. For example, the presentation unit suggests an optimal menu based on the user's past selection history. This makes it possible to provide information with higher accuracy by referring to the user's past presentation results. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can improve accuracy by using an AI model that uses the user's past presentation results as input and improves the presentation accuracy.

[0082] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated user emotions. For example, if the user is feeling stressed, the presentation unit provides a short, concise presentation. For example, if the user is feeling stressed, the presentation unit can provide a short, concise presentation. Furthermore, if the user is relaxed, the presentation unit can provide a longer presentation including detailed information. For example, if the user is feeling relaxed, the presentation unit can provide a longer presentation including detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a shorter presentation to quickly provide information. For example, if the user is in a hurry, the presentation unit can provide a shorter presentation to quickly provide information. This allows for more effective information provision by adjusting the length of the presentation according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may adjust the length using an AI model that uses user emotion data as input and adjusts the presentation length.

[0083] The presentation unit, when presenting the menu, can determine a presentation priority based on the time of submission of the meal menu. For example, in the case of a breakfast menu, the presentation unit prioritizes presentation in the morning. For example, in the case of a breakfast menu, the presentation unit prioritizes presentation in the morning. The presentation unit can also prioritize presentation in the case of a lunch menu during the afternoon. For example, the presentation unit prioritizes presentation in the case of a lunch menu during the afternoon. The presentation unit can also prioritize presentation in the case of a dinner menu during the evening. For example, the presentation unit prioritizes presentation in the case of a dinner menu during the evening. In this way, by determining the priority based on the time of submission of the meal menu, information can be provided at a more appropriate time. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can determine the priority using an AI model that determines the presentation priority using the time of submission of the meal menu as input.

[0084] The presentation unit can adjust the order of presentation based on the relevance of the meal menus when presenting them. For example, the presentation unit prioritizes presenting a menu that is most relevant to the user's health condition. For example, the presentation unit prioritizes presenting a menu that is most relevant to the user's health condition. The presentation unit can also prioritize presenting a menu that is highly relevant based on the user's past selection history. For example, the presentation unit prioritizes presenting a menu that is highly relevant based on the user's past selection history. The presentation unit can also prioritize presenting a menu that is highly relevant based on the user's current living situation. For example, the presentation unit prioritizes presenting a menu that is highly relevant based on the user's current living situation. By adjusting the order based on the relevance of the meal menus, more relevant information can be provided. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the order using an AI model that uses the relevance of the meal menus as input and adjusts the presentation order.

[0085] The presentation unit can adjust the use of technical terms used in the presentation depending on the user's level of expertise. For example, if the user has technical expertise, the presentation unit uses detailed technical terms to present the information. For example, the presentation unit uses detailed technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the presentation unit can provide explanations in simple terms. For example, the presentation unit provides explanations in simple terms based on the user's level of expertise. Furthermore, the presentation unit can select and present appropriate technical terms depending on the user's level of knowledge. For example, the presentation unit selects and presents appropriate technical terms based on the user's level of knowledge. This allows for the provision of information that is easier to understand by adjusting the use of technical terms depending on the user's level of expertise. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can adjust the use of technical terms using an AI model that uses the user's level of expertise as input.

[0086] The health condition consideration unit can estimate the user's emotions and adjust the analysis method of the health condition based on the estimated user emotions. For example, if the user is feeling stressed, the health condition consideration unit selects an analysis method that is effective for stress reduction. For example, the health condition consideration unit estimates the user's emotions and, if the user is feeling stressed, selects an analysis method that is effective for stress reduction. The health condition consideration unit can also perform a detailed analysis of the health condition if the user is relaxed. For example, the health condition consideration unit estimates the user's emotions and, if the user is relaxed, performs a detailed analysis of the health condition. The health condition consideration unit can also quickly analyze the health condition if the user is in a hurry. For example, the health condition consideration unit estimates the user's emotions and, if the user is in a hurry, quickly analyzes the health condition. This enables a more appropriate analysis of the health condition by adjusting the analysis method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit may adjust the analysis method using an AI model that inputs user emotion data and adjusts the analysis method.

[0087] When analyzing the health condition, the health condition consideration unit can predict the current health condition by referring to past test data. The health condition consideration unit, for example, predicts the current blood glucose level based on the user's past blood glucose level data. For example, the health condition consideration unit predicts the current blood glucose level based on the user's past blood glucose level data. The health condition consideration unit can also predict the current renal function based on the user's past renal function data. For example, the health condition consideration unit predicts the current renal function based on the user's past renal function data. The health condition consideration unit can also predict the current blood pressure based on the user's past blood pressure data. For example, the health condition consideration unit predicts the current blood pressure based on the user's past blood pressure data. In this way, by referring to the past test data, the current health condition can be predicted more accurately. Some or all of the above-mentioned processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can predict the health condition using an AI model that uses past test data as input and predicts the current health condition.

[0088] When analyzing the health condition, the health condition consideration unit can apply different analysis methods depending on the user's health condition. For example, if the user has diabetes, the health condition consideration unit applies an analysis method specialized for blood glucose level management. For example, if the user has diabetes, the health condition consideration unit applies an analysis method specialized for blood glucose level management. Furthermore, if the user has kidney failure, the health condition consideration unit can also apply an analysis method specialized for renal function management. For example, if the user has kidney failure, the health condition consideration unit applies an analysis method specialized for renal function management. Furthermore, if the user has high blood pressure, the health condition consideration unit can also apply an analysis method specialized for blood pressure management. For example, if the user has high blood pressure, the health condition consideration unit applies an analysis method specialized for blood pressure management. In this way, by applying an analysis method according to the user's health condition, more appropriate health condition analysis is possible. Some or all of the above-mentioned processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can apply an analysis method using an AI model that inputs the user's health condition and applies an appropriate analysis method.

[0089] The health condition consideration unit can perform analysis by taking into account the user's attribute information when analyzing the health condition. The health condition consideration unit selects an appropriate analysis method, for example, by taking into account the user's age. For example, the health condition consideration unit selects an appropriate analysis method based on the user's age. The health condition consideration unit can also select an appropriate analysis method by taking into account the user's gender. For example, the health condition consideration unit selects an appropriate analysis method based on the user's gender. The health condition consideration unit can also select an appropriate analysis method by taking into account the user's lifestyle habits. For example, the health condition consideration unit selects an appropriate analysis method based on the user's lifestyle habits. This enables more accurate analysis of the health condition by taking into account the user's attribute information. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can perform analysis using an AI model that uses the user's attribute information as input and performs analysis.

[0090] The health condition consideration unit can estimate the user's emotions and adjust the importance of the health condition based on the estimated user's emotions. For example, if the user is feeling stressed, the health condition consideration unit prioritizes analyzing health conditions to reduce stress. For example, the health condition consideration unit estimates the user's emotions and prioritizes analyzing health conditions to reduce stress when the user is feeling stressed. The health condition consideration unit can also analyze the user's overall health condition in detail when the user is relaxed. For example, the health condition consideration unit estimates the user's emotions and, when the user is relaxed, analyzes the user's overall health condition in detail. The health condition consideration unit can also quickly analyze the user's health condition by focusing on important health conditions when the user is in a hurry. For example, the health condition consideration unit estimates the user's emotions and, when the user is in a hurry, quickly analyzes the user's health condition by focusing on important health conditions. This allows for more appropriate analysis of the health condition by adjusting the importance of the health condition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit may input user emotion data and adjust the importance of the health condition using an AI model that adjusts the importance of the health condition.

[0091] When analyzing the health status, the health status consideration unit can analyze changes in the analysis based on the timing of test data submission. The health status consideration unit, for example, analyzes changes in the health status based on test data periodically submitted by the user. For example, the health status consideration unit analyzes changes in the health status based on test data periodically submitted by the user. The health status consideration unit can also analyze changes in the health status based on test data submitted by the user after a specific event. For example, the health status consideration unit analyzes changes in the health status based on test data submitted by the user after a specific event. The health status consideration unit can also analyze trends in the health status based on test data submitted by the user over a long period of time. For example, the health status consideration unit analyzes trends in the health status based on test data submitted by the user over a long period of time. This enables more accurate analysis of the health status by analyzing changes in the analysis based on the timing of test data submission. Some or all of the above-described processing in the health status consideration unit may be performed using, for example, AI, or may be performed without AI. For example, the health status consideration unit can analyze changes using an AI model that uses the timing of test data submission as input and analyzes changes in the analysis.

[0092] The health condition consideration unit can perform the analysis by referring to related medical data when analyzing the health condition. The health condition consideration unit, for example, refers to the user's past medical records to analyze the health condition. For example, the health condition consideration unit analyzes the health condition based on the user's past medical records. The health condition consideration unit can also analyze the health condition by referring to information on medications the user is taking. For example, the health condition consideration unit analyzes the health condition based on information on medications the user is taking. The health condition consideration unit can also analyze genetic health risks by referring to the user's family history. For example, the health condition consideration unit analyzes genetic health risks based on the user's family history. This enables more accurate analysis of the health condition by referring to related medical data. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can perform the analysis using an AI model that uses related medical data as input and performs analysis.

[0093] The health condition consideration unit can perform the analysis by taking into account the user's lifestyle habits when analyzing the health condition. The health condition consideration unit, for example, analyzes the health condition by taking into account the user's eating habits. For example, the health condition consideration unit analyzes the health condition based on the user's eating habits. The health condition consideration unit can also analyze the health condition by taking into account the user's exercise habits. For example, the health condition consideration unit analyzes the health condition based on the user's exercise habits. The health condition consideration unit can also analyze the health condition by taking into account the user's sleeping habits. For example, the health condition consideration unit analyzes the health condition based on the user's sleeping habits. This enables a more accurate analysis of the health condition by taking the user's lifestyle habits into account. Some or all of the above-described processing in the health condition consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the health condition consideration unit can perform the analysis using an AI model that uses the user's lifestyle habits as input and performs analysis. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, presentation unit, and health condition consideration unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the user's dietary details, exercise details, and weight data. The presentation unit is realized, for example, by the display 40A of the smart device 14 and presents a meal menu based on the analysis results. The health condition consideration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's test data and presents a meal menu that takes into account a specific health condition. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, presentation unit, and health condition consideration unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the user's dietary details, exercise details, and weight data. The presentation unit is realized, for example, by the display of the smart glasses 214 and presents a meal menu based on the analysis results. The health condition consideration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's examination data and presents a meal menu that takes into account a specific health condition. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, presentation unit, and health condition consideration unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the user's dietary details, exercise details, and weight data. The presentation unit is realized, for example, by the display 343 of the headset type terminal 314 and presents a meal menu based on the analysis results. The health condition consideration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's examination data and presents a meal menu that takes into consideration a specific health condition. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, presentation unit, and health condition consideration unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the user's dietary details, exercise details, and weight data. The presentation unit is realized, for example, by the display of the robot 414 and presents a meal menu based on the analysis results. The health condition consideration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's examination data and presents a meal menu that takes into consideration a specific health condition.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The analysis unit can not only analyze the user's dietary details, exercise details, and weight data, but also the user's sleep data. For example, the analysis unit can analyze sleep patterns based on the sleep duration and sleep quality input by the user. The analysis unit can also evaluate the effects of insufficient or excessive sleep based on the user's sleep data. This allows for a more comprehensive understanding of the user's overall health condition. Furthermore, the analysis unit can also suggest appropriate exercise and meal timings based on the user's sleep data. For example, the analysis unit can suggest exercises that the user should do after getting enough sleep or a meal menu that should be eaten before sleep. This enables health management that is tailored to the user's lifestyle.

[0096] The analysis unit can also take the user's emotional data into consideration when analyzing the user's dietary details, exercise details, and weight data. For example, the analysis unit can analyze the user's stress and happiness levels based on the emotional data input by the user. The analysis unit can also evaluate the impact of emotional fluctuations on diet and exercise based on the user's emotional data. This enables health management that takes the user's emotional state into consideration. Furthermore, the analysis unit can also suggest exercises and meal menus to reduce stress based on the user's emotional data. For example, if the user is feeling stressed, the analysis unit can suggest exercises and meal menus that have a relaxing effect. This enables health management that corresponds to the user's emotional state.

[0097] The presentation unit not only presents meal menus based on the results of the analysis unit, but can also provide recipe videos that suit the user's preferences. For example, the presentation unit can suggest related recipe videos based on the user's preferred ingredients and types of dishes. The presentation unit can also provide simple recipe videos for beginners and detailed recipe videos for advanced cooks, depending on the user's cooking skill. This allows the user to enjoy cooking that suits their skill level. Furthermore, the presentation unit can also suggest recipe videos that can be made in a short amount of time or recipe videos that can be carefully tackled on the weekend, depending on the user's lifestyle. This makes it possible to provide meal menus that suit the user's lifestyle.

[0098] The health condition consideration unit not only analyzes the test data entered by the user, but can also suggest a meal menu taking into consideration the user's genetic information. For example, the health condition consideration unit can suggest when intake of specific nutrients is necessary or which ingredients should be avoided based on the user's genetic information. The health condition consideration unit can also suggest a meal menu for reducing genetic risks based on the user's genetic information. This enables health management that takes into consideration the user's genetic background. Furthermore, the health condition consideration unit can also suggest a meal menu that is effective in preventing specific diseases based on the user's genetic information. For example, if the user is genetically at risk of high blood pressure, the health condition consideration unit can suggest a low-salt meal menu. This enables health management that takes into consideration the user's genetic risks.

[0099] When the user is unable to cook at home, the presentation unit not only suggests healthy meal menus that can be purchased at a nearby convenience store, but can also adjust the suggestions according to the user's emotional state. For example, when the user is feeling stressed, the presentation unit suggests a menu that includes ingredients that have a relaxing effect. Furthermore, when the user is relaxed, the presentation unit can also suggest a nutritionally balanced meal menu. This makes it possible to provide a meal menu that suits the user's emotional state. Furthermore, when the user is in a hurry, the presentation unit can also suggest a meal menu that can be purchased in a short time. For example, when the user is in a hurry, the presentation unit can suggest a simple meal menu that can be purchased at a convenience store. This makes it possible to provide a flexible meal menu that suits the user's situation.

[0100] In a situation where the user is eating out, the presentation unit not only suggests healthy menu items that can be ordered at a restaurant, but can also customize the suggestions based on the user's dietary preferences. For example, the presentation unit suggests menu items from related restaurants based on the types of dishes and ingredients that the user prefers. The presentation unit can also suggest menu items that are safe to eat, taking into account the user's allergy information. This allows the user to enjoy eating out with peace of mind. Furthermore, the presentation unit can suggest affordable menu items based on the user's budget. For example, if the user inputs a budget, the presentation unit can suggest healthy menu items within that budget. This makes it possible to provide flexible meal menus that suit the user's financial situation.

[0101] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user's emotions, as well as adjust the content of the analysis results. For example, if the user is feeling stressed, the analysis unit can prioritize presenting exercises and meal menus that are effective for stress reduction. Furthermore, if the user is relaxed, the analysis unit can present a nutritionally balanced meal menu and an effective exercise plan. This makes it possible to provide analysis results that correspond to the user's emotional state. Furthermore, if the user is in a hurry, the analysis unit can present exercises that can be performed in a short time and meal menus that can be easily prepared. For example, if the user is in a hurry, the analysis unit can present effective exercises that can be performed in a short time and meal menus that can be easily prepared. This makes it possible to provide flexible analysis results that correspond to the user's situation.

[0102] The analysis unit not only analyzes the user's past dietary details, exercise details, and weight data, but can also select an analysis method taking the user's lifestyle into consideration. For example, if the user is a nocturnal person, the analysis unit can suggest a meal menu and exercise plan suitable for the night. Furthermore, if the user is a morning person, the analysis unit can suggest a meal menu and morning exercise plan suitable for breakfast. This enables health management that is tailored to the user's lifestyle. Furthermore, the analysis unit can suggest optimal sleep times and rest timings based on the user's lifestyle. For example, if the user is a nocturnal person, the analysis unit can suggest a nighttime rest time. This enables flexible health management that is tailored to the user's lifestyle.

[0103] During analysis, the analysis unit not only filters the data based on the user's current lifestyle and health status, but also customizes the analysis results according to the user's goals. For example, if the user's goal is weight loss, the analysis unit can suggest a calorie-restricted meal menu and a high-intensity exercise plan. Furthermore, if the user's goal is muscle building, the analysis unit can suggest a high-protein meal menu and a strength training plan. This enables health management according to the user's goals. Furthermore, the analysis unit can periodically evaluate the user's progress toward achieving their goals and adjust the analysis results as necessary. For example, if the user is approaching their goal, the analysis unit can suggest a maintenance plan for after achieving the goal. This enables flexible health management according to the user's goals.

[0104] During analysis, the analysis unit not only selects the optimal analysis means according to the user's input method, but can also adjust the analysis means according to the user's device environment. For example, if the user is using a smartphone, the analysis unit selects an analysis means optimized for the smartphone. Furthermore, if the user is using a wearable device, the analysis unit can also perform analysis based on data acquired from the wearable device. This enables flexible analysis according to the user's device environment. Furthermore, if the user is using multiple devices, the analysis unit can also integrate and analyze data acquired from each device. For example, if the user is using both a smartphone and a wearable device, the analysis unit can integrate and analyze data acquired from both devices. This enables comprehensive analysis according to the user's device environment.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The analysis unit analyzes the user's dietary details, exercise details, and weight data. For example, the analysis unit analyzes calorie intake and nutrient balance based on the dietary details entered by the user, analyzes calories burned based on the exercise details, and analyzes weight fluctuations based on the weight data. Step 2: The presentation unit presents a meal menu based on the results of the analysis by the analysis unit. For example, the presentation unit may suggest home-cooked meals, restaurant meals, and convenience store meal menus based on the user's health condition and diet progress. Step 3: The health condition consideration unit analyzes the test data entered by the user and presents a meal menu that takes into account the specific health condition. For example, the health condition consideration unit may propose a meal menu for managing blood sugar levels based on diabetes test data, or a meal menu that does not burden the kidneys based on kidney damage test data.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] 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.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] 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.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] 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.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 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. an analysis unit that analyzes the user's dietary details, exercise details, and weight data; a presentation unit that presents a meal menu based on the results of the analysis by the analysis unit; and a health condition consideration unit that analyzes the test data entered by the user and presents a meal menu that takes into consideration the user's specific health condition. A system characterized by:

2. The analysis unit Analyzes the user's diet, exercise, and weight data to determine the user's health condition and diet progress 2. The system of claim 1.

3. The presentation unit Based on the results of the analysis, a meal menu including home-cooked meals, meals at restaurants, and meals from convenience stores is presented.

2. The system of claim 1.

4. The health condition consideration unit The system analyzes test data entered by the user and presents meal plans that take into account specific health conditions such as diabetes and kidney problems.

2. The system of claim 1.

5. The presentation unit If the user is unable to cook at home, the app suggests healthy meal options that can be purchased at a nearby convenience store.

2. The system of claim 1.

6. The presentation unit If you are dining out, suggest healthy options to order at the restaurant.

2. The system of claim 1.

7. The analysis unit Estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions.

2. The system of claim 1.

8. The analysis unit Analyze the user's past dietary, exercise, and weight data and select an analysis method 2. The system of claim 1.

9. The analysis unit During analysis, filtering is performed based on the user's current living situation and health status.

2. The system of claim 1.

Images