System

Abstract

An object of a system according to an embodiment is to analyze meal records and body weight data of a user and propose an appropriate amount of exercise and an appropriate meal plan.SOLUTION: A system according to an embodiment includes a meal record analysis unit, an exercise plan suggestion unit, a food ingredient information analysis unit, and a body weight prediction visualization unit. The meal record analysis unit analyzes meal records and body weight data of the user. The exercise plan proposing unit proposes an appropriate amount of exercise and an activity plan based on the data analyzed by the meal record analyzing unit. The food material information analysis part provides a meal plan by utilizing the food material information in season based on the exercise plan proposed by the exercise plan proposal part. The weight prediction visualization unit predicts a future weight and a future body shape from the weight and the meal record on the basis of the meal plan provided by the food ingredient information analysis unit, and visualizes the prediction result as a video of the user.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 do not adequately suggest appropriate exercise amounts and meal plans based on a user's food records and weight data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's dietary records and weight data, and to propose an appropriate amount of exercise and meal plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal record analysis unit, an exercise plan proposal unit, an ingredient information analysis unit, and a weight prediction visualization unit. The meal record analysis unit analyzes the user's meal record and weight data. The exercise plan proposal unit proposes an appropriate amount of exercise and an activity plan based on the data analyzed by the meal record analysis unit. The ingredient information analysis unit provides a meal plan utilizing seasonal ingredient information based on the exercise plan proposed by the exercise plan proposal unit. The weight prediction visualization unit predicts future weight and body shape from the weight and meal record based on the meal plan provided by the ingredient information analysis unit, and visualizes the prediction results using an image of the user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a user's dietary records and weight data, and suggest appropriate exercise amounts and meal plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The health management system according to an embodiment of the present invention uses a generation AI to analyze a user's dietary records and weight data, and proposes optimal meal menus and exercise / activity plans. This allows the health management system to comprehensively support the user's health management.

[0029] A health management system according to an embodiment includes a meal record analysis unit, an exercise plan proposal unit, an ingredient information analysis unit, and a weight prediction visualization unit. The meal record analysis unit analyzes a user's meal record and weight data. For example, the meal record analysis unit records the menu items the user eats each day and inputs the data to the generation AI. The meal record analysis unit can also collect the user's weight data, which the generation AI can analyze. The exercise plan proposal unit proposes an appropriate amount of exercise and an activity plan based on the data analyzed by the meal record analysis unit. For example, the exercise plan proposal unit records the user's daily exercise amount and inputs the data to the generation AI. The exercise plan proposal unit can also use the data to propose an exercise plan suitable for the user. The ingredient information analysis unit provides a meal plan using seasonal ingredient information based on the exercise plan proposed by the exercise plan proposal unit. For example, the ingredient information analysis unit proposes menus using fresh vegetables and fruits in spring and menus that emphasize cold dishes and hydration in summer. The weight prediction visualization unit predicts future weight and body shape from the weight and meal records based on the meal plan provided by the ingredient information analysis unit, and visualizes the prediction results using a video of the user. For example, the weight prediction visualization unit allows the user to record their current weight and meal contents and input that data into the generation AI. The generation AI predicts future weight and body shape based on that data and visualizes the prediction results using a video of the user. This allows the health management system according to the embodiment to comprehensively support the user's health management. For example, the user can receive meal menu suggestions based on their eating habits and implement an appropriate exercise plan. Furthermore, predicting and visualizing future weight and body shape makes it easier for the user to maintain motivation to work toward their goals.

[0030] The food record analysis unit analyzes the user's past eating history and subsequent changes in physical condition, evaluates the impact of specific ingredients and dishes on the user, and can suggest optimal menus. For example, the generation AI in the food record analysis unit analyzes the user's eating history over the past year and subsequent changes in physical condition to evaluate the impact of specific ingredients and dishes on the user. For example, if the user tends to feel better after consuming a specific ingredient, the generation AI can suggest a menu that includes that ingredient. The food record analysis unit can also evaluate the impact of a specific dish on the user based on the user's eating history and changes in physical condition. For example, if the user's physical condition worsens after eating a specific dish, the generation AI can suggest that the user avoid that dish. This makes it possible to suggest optimal meal menus based on the user's health condition.

[0031] The exercise plan proposal unit can analyze the user's muscle fatigue level and heart rate data in real time and propose optimal exercise intensity and rest time. In the exercise plan proposal unit, for example, the generation AI analyzes the user's muscle fatigue level in real time and proposes optimal exercise intensity and rest time. For example, if the muscle fatigue level is high, lighter exercise is proposed. The exercise plan proposal unit can also analyze the user's heart rate data in real time and propose optimal exercise intensity. For example, if the heart rate is high, it suggests lowering the exercise intensity. In addition, the exercise plan proposal unit can propose optimal rest time based on the user's muscle fatigue level and heart rate data. For example, appropriately setting rest time after exercise promotes muscle recovery. This makes it possible to propose an exercise plan that suits the user's physical condition.

[0032] The ingredient information analysis unit can analyze information on seasonal ingredients for each region and propose meal plans that utilize local specialties. For example, the generation AI can analyze information on seasonal ingredients for each region and propose meal plans that utilize local specialties. For example, it can propose menus using fresh seafood to a user in Hokkaido. The ingredient information analysis unit can also propose meal plans that match the season based on the ingredient information for each region. For example, it can propose menus using fresh vegetables and fruits in spring, and menus that emphasize cold dishes and hydration in summer. This makes it possible to propose meal plans that utilize local specialties.

[0033] The weight prediction visualization unit can analyze the user's weight fluctuation data and predict in detail the impact of specific meals and exercise on weight. In the weight prediction visualization unit, for example, the generation AI analyzes the user's weight fluctuation data and predicts in detail the impact of specific meals on weight. For example, it predicts the impact of high-calorie meals on weight gain. The weight prediction visualization unit can also predict in detail the impact of specific exercises on weight based on the user's exercise data. For example, it predicts the impact of jogging on weight loss. The weight prediction visualization unit can also combine the user's diet and exercise data to comprehensively predict the impact on weight. For example, it predicts the impact of a specific combination of meals and exercise on weight. This makes it possible to predict the user's weight fluctuation in detail.

[0034] The weight prediction visualization unit analyzes the user's hormone balance and metabolic data to make more accurate weight and body type predictions. In the weight prediction visualization unit, for example, the generation AI analyzes the user's hormone balance data to make more accurate weight predictions. For example, it predicts the impact of fluctuations in hormone balance on weight. The weight prediction visualization unit can also analyze the user's metabolic data to make more accurate body type predictions. For example, it predicts the impact of basal metabolic rate on body type. The weight prediction visualization unit can also combine the user's hormone balance and metabolic data to make more accurate weight and body type predictions. For example, it predicts the impact of fluctuations in hormone balance and metabolism on weight and body type. This allows for more accurate weight and body type predictions.

[0035] The weight prediction visualization unit can specifically visualize changes in appearance when predicting weight and body type, taking into account the user's clothing size and fashion style. For example, the weight prediction visualization unit specifically visualizes changes in appearance when the generation AI predicts weight and body type, taking into account the user's clothing size. For example, it displays how clothes fit after weight loss. The weight prediction visualization unit can also specifically visualize changes in appearance when the generation AI predicts weight and body type based on the user's fashion style. For example, it displays changes in body type that match the user's preferred fashion style. The weight prediction visualization unit can also specifically visualize changes in appearance when the generation AI predicts weight and body type, combining the user's clothing size and fashion style. For example, it simultaneously displays how clothes fit and changes in fashion style after weight loss. This makes it possible to specifically visualize changes in the user's appearance.

[0036] The weight prediction visualization unit can consider the user's health risks and suggest preventive measures when predicting their weight and body type. For example, when the generation AI predicts a user's weight and body type, the weight prediction visualization unit considers the risk of diabetes and suggests preventive measures. For example, it suggests a meal menu with low sugar intake. The weight prediction visualization unit can also suggest preventive measures when the generation AI predicts their weight and body type based on the user's health risk data. For example, it considers the risk of high blood pressure and suggests a meal menu with low salt intake. The weight prediction visualization unit can also analyze the user's health risk data in real time and suggest preventive measures when predicting their weight and body type. For example, it considers the risk of heart disease and suggests an appropriate exercise plan. This makes it possible to suggest preventive measures that take the user's health risks into account.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The health management system can also include a sleep analysis unit that analyzes the user's sleep data and suggests an optimal sleep schedule. For example, the sleep analysis unit records the user's sleep patterns, and the generation AI analyzes the data. The sleep analysis unit can also evaluate the user's sleep quality and provide advice for improvement. For example, if the user's sleep quality is low, the unit can suggest relaxation methods and appropriate bedtimes. This provides more comprehensive support for the user's health management.

[0039] The health management system can also include a stress analysis unit that analyzes the user's stress level and provides advice for stress reduction. For example, the stress analysis unit records the user's heart rate and breathing patterns, and the generation AI analyzes the data. The stress analysis unit can also evaluate the user's stress level and suggest relaxation methods and exercises for stress reduction. For example, it can suggest relaxation methods such as deep breathing and yoga. This can support the user's stress management.

[0040] The health management system can also include a hydration analysis unit that analyzes the user's water intake and suggests appropriate times to hydrate. For example, the hydration analysis unit records the user's water intake, and the generation AI analyzes the data. The hydration analysis unit can also suggest appropriate times to hydrate based on the user's activity level and temperature. For example, it can suggest hydration after exercise or on hot days. This can support the user's hydration management.

[0041] The health management system can also include a preference analysis unit that analyzes the user's food preferences and proposes meal menus tailored to each individual preference. For example, the preference analysis unit uses a generation AI to analyze the user's preferences based on the user's past meal records. The preference analysis unit can also customize meal menus based on the user's preferences. For example, if a user likes a particular ingredient, it can propose a menu using that ingredient. This can increase the user's satisfaction with their meal.

[0042] The health management system may further include an exercise history analysis unit that analyzes the user's exercise history and optimizes the exercise plan based on past exercise performance. For example, the exercise history analysis unit records the user's past exercise data, and the generation AI analyzes the data. The exercise history analysis unit may also evaluate the user's exercise performance and provide advice for improvement. For example, if a particular exercise was effective, the analysis unit may suggest continuing that exercise. This can more effectively support the user's exercise plan.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The food record analysis unit analyzes the user's food record and weight data. For example, the user records the menu items they eat each day and inputs that data into the generation AI. The generation AI can also collect the user's weight data and analyze it. Step 2: The exercise plan suggestion unit suggests an appropriate amount of exercise and activity plan based on the data analyzed by the meal record analysis unit. For example, the unit records the user's daily exercise amount and inputs that data into the generation AI. The generation AI can also suggest an exercise plan suitable for the user based on that data. Step 3: The food information analysis unit uses seasonal food information to provide a meal plan based on the exercise plan proposed by the exercise plan proposal unit. For example, it suggests menus using fresh vegetables and fruits in spring, and menus that emphasize cold dishes and hydration in summer. Step 4: The weight prediction visualization unit predicts future weight and body shape from the weight and meal records based on the meal plan provided by the food ingredient information analysis unit, and visualizes the prediction results using a video of the user. For example, the user records their current weight and meal contents and inputs that data into the generation AI. The generation AI predicts future weight and body shape based on that data, and visualizes the prediction results using a video of the user.

[0045] (Example 2) The health management system according to an embodiment of the present invention uses a generation AI to analyze a user's dietary records and weight data, and proposes optimal meal menus and exercise / activity plans. This allows the health management system to comprehensively support the user's health management.

[0046] A health management system according to an embodiment includes a meal record analysis unit, an exercise plan proposal unit, an ingredient information analysis unit, and a weight prediction visualization unit. The meal record analysis unit analyzes a user's meal record and weight data. For example, the meal record analysis unit records the menu items the user eats each day and inputs the data to the generation AI. The meal record analysis unit can also collect the user's weight data, which the generation AI can analyze. The exercise plan proposal unit proposes an appropriate amount of exercise and an activity plan based on the data analyzed by the meal record analysis unit. For example, the exercise plan proposal unit records the user's daily exercise amount and inputs the data to the generation AI. The exercise plan proposal unit can also use the data to propose an exercise plan suitable for the user. The ingredient information analysis unit provides a meal plan using seasonal ingredient information based on the exercise plan proposed by the exercise plan proposal unit. For example, the ingredient information analysis unit proposes menus using fresh vegetables and fruits in spring and menus that emphasize cold dishes and hydration in summer. The weight prediction visualization unit predicts future weight and body shape from the weight and meal records based on the meal plan provided by the ingredient information analysis unit, and visualizes the prediction results using a video of the user. For example, the weight prediction visualization unit allows the user to record their current weight and meal contents and input that data into the generation AI. The generation AI predicts future weight and body shape based on that data and visualizes the prediction results using a video of the user. This allows the health management system according to the embodiment to comprehensively support the user's health management. For example, the user can receive meal menu suggestions based on their eating habits and implement an appropriate exercise plan. Furthermore, predicting and visualizing future weight and body shape makes it easier for the user to maintain motivation to work toward their goals.

[0047] The food record analysis unit analyzes the user's past eating history and subsequent changes in physical condition, evaluates the impact of specific ingredients and dishes on the user, and can suggest optimal menus. For example, the generation AI in the food record analysis unit analyzes the user's eating history over the past year and subsequent changes in physical condition to evaluate the impact of specific ingredients and dishes on the user. For example, if the user tends to feel better after consuming a specific ingredient, the generation AI can suggest a menu that includes that ingredient. The food record analysis unit can also evaluate the impact of a specific dish on the user based on the user's eating history and changes in physical condition. For example, if the user's physical condition worsens after eating a specific dish, the generation AI can suggest that the user avoid that dish. This makes it possible to suggest optimal meal menus based on the user's health condition.

[0048] The exercise plan proposal unit can analyze the user's muscle fatigue level and heart rate data in real time and propose optimal exercise intensity and rest time. In the exercise plan proposal unit, for example, the generation AI analyzes the user's muscle fatigue level in real time and proposes optimal exercise intensity and rest time. For example, if the muscle fatigue level is high, lighter exercise is proposed. The exercise plan proposal unit can also analyze the user's heart rate data in real time and propose optimal exercise intensity. For example, if the heart rate is high, it suggests lowering the exercise intensity. In addition, the exercise plan proposal unit can propose optimal rest time based on the user's muscle fatigue level and heart rate data. For example, appropriately setting rest time after exercise promotes muscle recovery. This makes it possible to propose an exercise plan that suits the user's physical condition.

[0049] The ingredient information analysis unit can analyze information on seasonal ingredients for each region and propose meal plans that utilize local specialties. For example, the generation AI can analyze information on seasonal ingredients for each region and propose meal plans that utilize local specialties. For example, it can propose menus using fresh seafood to a user in Hokkaido. The ingredient information analysis unit can also propose meal plans that match the season based on the ingredient information for each region. For example, it can propose menus using fresh vegetables and fruits in spring, and menus that emphasize cold dishes and hydration in summer. This makes it possible to propose meal plans that utilize local specialties.

[0050] The weight prediction visualization unit can analyze the user's weight fluctuation data and predict in detail the impact of specific meals and exercise on weight. In the weight prediction visualization unit, for example, the generation AI analyzes the user's weight fluctuation data and predicts in detail the impact of specific meals on weight. For example, it predicts the impact of high-calorie meals on weight gain. The weight prediction visualization unit can also predict in detail the impact of specific exercises on weight based on the user's exercise data. For example, it predicts the impact of jogging on weight loss. The weight prediction visualization unit can also combine the user's diet and exercise data to comprehensively predict the impact on weight. For example, it predicts the impact of a specific combination of meals and exercise on weight. This makes it possible to predict the user's weight fluctuation in detail.

[0051] The weight prediction visualization unit analyzes the user's hormone balance and metabolic data to make more accurate weight and body type predictions. In the weight prediction visualization unit, for example, the generation AI analyzes the user's hormone balance data to make more accurate weight predictions. For example, it predicts the impact of fluctuations in hormone balance on weight. The weight prediction visualization unit can also analyze the user's metabolic data to make more accurate body type predictions. For example, it predicts the impact of basal metabolic rate on body type. The weight prediction visualization unit can also combine the user's hormone balance and metabolic data to make more accurate weight and body type predictions. For example, it predicts the impact of fluctuations in hormone balance and metabolism on weight and body type. This allows for more accurate weight and body type predictions.

[0052] The weight prediction visualization unit can use the emotion estimation function to analyze the user's emotions about their future body shape and visualize prediction results that elicit positive emotions. For example, the weight prediction visualization unit can use the emotion estimation function to analyze the user's emotions about their future body shape and visualize prediction results that elicit positive emotions. For example, it can display prediction results for when the ideal body shape is achieved. The weight prediction visualization unit can also generate prediction results that elicit positive emotions based on the user's emotion data using the generation AI. For example, it can predict the emotions the user will have when they achieve their target weight and visualize the results. The weight prediction visualization unit can also analyze the user's emotion data in real time using the generation AI to provide feedback that elicits positive emotions. For example, it can display encouraging messages to help the user maintain motivation to work toward their goal. This makes it possible to visualize positive prediction results to increase the user's motivation.

[0053] The weight prediction visualization unit can specifically visualize changes in appearance when predicting weight and body type, taking into account the user's clothing size and fashion style. For example, the weight prediction visualization unit specifically visualizes changes in appearance when the generation AI predicts weight and body type, taking into account the user's clothing size. For example, it displays how clothes fit after weight loss. The weight prediction visualization unit can also specifically visualize changes in appearance when the generation AI predicts weight and body type based on the user's fashion style. For example, it displays changes in body type that match the user's preferred fashion style. The weight prediction visualization unit can also specifically visualize changes in appearance when the generation AI predicts weight and body type, combining the user's clothing size and fashion style. For example, it simultaneously displays how clothes fit and changes in fashion style after weight loss. This makes it possible to specifically visualize changes in the user's appearance.

[0054] The weight prediction visualization unit can consider the user's health risks and suggest preventive measures when predicting their weight and body type. For example, when the generation AI predicts a user's weight and body type, the weight prediction visualization unit considers the risk of diabetes and suggests preventive measures. For example, it suggests a meal menu with low sugar intake. The weight prediction visualization unit can also suggest preventive measures when the generation AI predicts their weight and body type based on the user's health risk data. For example, it considers the risk of high blood pressure and suggests a meal menu with low salt intake. The weight prediction visualization unit can also analyze the user's health risk data in real time and suggest preventive measures when predicting their weight and body type. For example, it considers the risk of heart disease and suggests an appropriate exercise plan. This makes it possible to suggest preventive measures that take the user's health risks into account.

[0055] The weight prediction visualization unit can use the emotion estimation function to monitor the user's emotions about the prediction results in real time and provide feedback to maintain motivation. The weight prediction visualization unit can, for example, use the emotion estimation function to monitor the user's emotions about the prediction results in real time and provide feedback to maintain motivation. For example, it can display an encouraging message that elicits positive emotions. The weight prediction visualization unit can also provide feedback to maintain motivation based on the user's emotion data using the generation AI. For example, it can display specific advice to help the user maintain motivation to work toward their goal. The weight prediction visualization unit can also analyze the user's emotion data in real time and provide feedback to maintain motivation. For example, it can suggest relaxation methods to reduce stress the user feels in the process of working toward their goal. This makes it possible to provide feedback to maintain the user's motivation.

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

[0057] The health management system can also include a sleep analysis unit that analyzes the user's sleep data and suggests an optimal sleep schedule. For example, the sleep analysis unit records the user's sleep patterns, and the generation AI analyzes the data. The sleep analysis unit can also evaluate the user's sleep quality and provide advice for improvement. For example, if the user's sleep quality is low, the unit can suggest relaxation methods and appropriate bedtimes. This provides more comprehensive support for the user's health management.

[0058] The health management system can also include a stress analysis unit that analyzes the user's stress level and provides advice for stress reduction. For example, the stress analysis unit records the user's heart rate and breathing patterns, and the generation AI analyzes the data. The stress analysis unit can also evaluate the user's stress level and suggest relaxation methods and exercises for stress reduction. For example, it can suggest relaxation methods such as deep breathing and yoga. This can support the user's stress management.

[0059] The health management system can also include a hydration analysis unit that analyzes the user's water intake and suggests appropriate times to hydrate. For example, the hydration analysis unit records the user's water intake, and the generation AI analyzes the data. The hydration analysis unit can also suggest appropriate times to hydrate based on the user's activity level and temperature. For example, it can suggest hydration after exercise or on hot days. This can support the user's hydration management.

[0060] The health management system can also include a preference analysis unit that analyzes the user's food preferences and proposes meal menus tailored to each individual preference. For example, the preference analysis unit uses a generation AI to analyze the user's preferences based on the user's past meal records. The preference analysis unit can also customize meal menus based on the user's preferences. For example, if a user likes a particular ingredient, it can propose a menu using that ingredient. This can increase the user's satisfaction with their meal.

[0061] The health management system may further include an exercise history analysis unit that analyzes the user's exercise history and optimizes the exercise plan based on past exercise performance. For example, the exercise history analysis unit records the user's past exercise data, and the generation AI analyzes the data. The exercise history analysis unit may also evaluate the user's exercise performance and provide advice for improvement. For example, if a particular exercise was effective, the analysis unit may suggest continuing that exercise. This can more effectively support the user's exercise plan.

[0062] The health management system may further include an emotion analysis unit that estimates the user's emotions and suggests a meal menu based on the estimated emotions. For example, the emotion analysis unit may suggest a menu using ingredients that have a relaxing effect if the user is under high stress, based on the user's emotion data. The emotion analysis unit may also analyze the user's emotion data in real time and suggest a menu using ingredients that will lift the user's spirits if the user is feeling depressed. This allows the system to provide a meal menu that matches the user's emotions.

[0063] The health management system may further include an emotion analysis unit that estimates the user's emotions and proposes an exercise plan based on the estimated emotions. For example, the emotion analysis unit may suggest exercises that have a relaxing effect when the user is under high stress based on the user's emotion data. The emotion analysis unit may also analyze the user's emotion data in real time and suggest exercises that will lift the user's spirits when the user is feeling depressed. This makes it possible to provide an exercise plan that matches the user's emotions.

[0064] The health management system may further include an emotion analysis unit that estimates the user's emotions and provides advice for improving sleep quality based on the estimated emotions. For example, the emotion analysis unit may suggest a pre-bedtime routine that is relaxing if the user is under high stress based on the user's emotion data. The emotion analysis unit may also analyze the user's emotion data in real time and suggest relaxation methods to improve mood if the user is feeling depressed. This makes it possible to provide measures to improve sleep quality according to the user's emotions.

[0065] The health management system may further include an emotion analysis unit that estimates the user's emotions and provides advice for reducing stress based on the estimated emotions. For example, the emotion analysis unit may suggest relaxation methods if the user is feeling high, based on the user's emotion data. The emotion analysis unit may also analyze the user's emotion data in real time and suggest activities to improve mood if the user is feeling depressed. This makes it possible to provide stress reduction measures tailored to the user's emotions.

[0066] The health management system may further include an emotion analysis unit that estimates the user's emotions and provides feedback to maintain motivation based on the estimated emotions. For example, the emotion analysis unit may display an encouraging message that elicits positive emotions based on the user's emotion data. The emotion analysis unit may also analyze the user's emotion data in real time and provide specific advice to maintain motivation. This makes it possible to provide motivation maintenance measures that correspond to the user's emotions.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The food record analysis unit analyzes the user's food record and weight data. For example, the user records the menu items they eat each day and inputs that data into the generation AI. The generation AI can also collect the user's weight data and analyze it. Step 2: The exercise plan suggestion unit suggests an appropriate amount of exercise and activity plan based on the data analyzed by the meal record analysis unit. For example, the unit records the user's daily exercise amount and inputs that data into the generation AI. The generation AI can also suggest an exercise plan suitable for the user based on that data. Step 3: The food information analysis unit uses seasonal food information to provide a meal plan based on the exercise plan proposed by the exercise plan proposal unit. For example, it suggests menus using fresh vegetables and fruits in spring, and menus that emphasize cold dishes and hydration in summer. Step 4: The weight prediction visualization unit predicts future weight and body shape from the weight and meal records based on the meal plan provided by the food ingredient information analysis unit, and visualizes the prediction results using a video of the user. For example, the user records their current weight and meal contents and inputs that data into the generation AI. The generation AI predicts future weight and body shape based on that data, and visualizes the prediction results using a video of the user.

[0069] 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.

[0070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0082] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0136] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a meal record analysis unit that analyzes the user's meal record and weight data; an exercise plan suggestion unit that suggests an appropriate amount of exercise and an activity plan based on the data analyzed by the diet record analysis unit; an ingredient information analysis unit that provides a meal plan using seasonal ingredient information based on the exercise plan proposed by the exercise plan proposal unit; and a weight prediction visualization unit that predicts the user's future weight and body shape from the weight and diet record based on the meal plan provided by the ingredient information analysis unit and visualizes the prediction result in an image of the user. A system characterized by:

2. The diet record analysis unit Analyze the user's past eating history and subsequent changes in physical condition, evaluate the effects of specific ingredients and dishes on the user, and propose optimal menus 2. The system of claim 1.

3. The exercise plan suggestion unit Analyzes the user's muscle fatigue level and heart rate data in real time and suggests optimal exercise intensity and rest time.

2. The system of claim 1.

4. The ingredient information analysis unit Analyze seasonal food information for each region and propose meal plans that utilize local specialties 2. The system of claim 1.

5. The weight prediction visualization unit Analyzing the weight fluctuation data of the user and predicting in detail the effects that specific diets and exercises will have on the weight 2. The system of claim 1.

6. The weight prediction visualization unit Analyze the user's hormone balance and metabolic data to predict weight and body shape with greater accuracy 2. The system of claim 1.

7. The weight prediction visualization unit Analyzing the feelings of the user regarding the future body shape, and visualizing the prediction result that elicits the positive feelings.

2. The system of claim 1.

8. The weight prediction visualization unit The user's feelings about the prediction results are monitored in real time, and feedback is provided to maintain motivation.

2. The system of claim 1.

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