System

Abstract

An object of a system according to an embodiment is to propose an appropriate exercise menu or stretch moving image according to the purpose, body shape, and age of a user.SOLUTION: A system includes a collection part, a proposal part, a reminder part, and a form analysis part. The collection unit collects information on a user. The suggestion unit analyzes the information collected by the collection unit and suggests an appropriate stretch moving image or exercise menu to the user. The reminding unit reminds the user of the timing of the exercise suggested by the suggesting unit. The form analyzing section points out a difference and an improvement point of the form when the exercise reminded by the reminding section is carried out.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 propose appropriate exercise menus and stretching videos based on the user's goals, body type, and age, and there is room for improvement.

[0005] The system according to the embodiment aims to propose appropriate exercise menus and stretching videos according to the user's goals, body type, and age. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a suggestion unit, a reminder unit, and a form analysis unit. The collection unit collects user information. The suggestion unit analyzes the information collected by the collection unit and suggests appropriate stretching videos and exercise menus to the user. The reminder unit reminds the user of the timing to perform the exercises suggested by the suggestion unit. The form analysis unit points out differences in form and areas for improvement when performing the exercises reminded by the reminder unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose appropriate exercise menus and stretching videos according to the user's goals, body type, and age. [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 system according to an embodiment of the present invention proposes optimal stretching videos and exercise menus based on a user's goals, body type, and age. This system collects user information, analyzes it using a generation AI, proposes optimal stretching videos and exercise menus, reminds users when to exercise, and points out differences in form and areas for improvement. This allows the system to propose optimal stretching videos and exercise menus based on the user's goals, body type, and age, and support continued exercise. For example, based on information entered by the user, the generation AI proposes stretching videos to prevent stiff shoulders and exercise menus for muscle training. Furthermore, the system reminds users when to exercise and points out differences in form and areas for improvement when the user stretches or trains. This allows the user to exercise with correct form and achieve effective training. The system also provides advice on meal plans and supplements, supporting comprehensive health management. This allows the system to improve the user's health.

[0029] A health management system according to an embodiment includes a collection unit, a suggestion unit, a reminder unit, and a form analysis unit. The collection unit collects user information. The user information includes, but is not limited to, health status, exercise history, and dietary details. The collection unit monitors the user's health status using, for example, a sensor. The collection unit can also store the information entered by the user in a database. The suggestion unit analyzes the information collected by the collection unit using a generation AI and suggests appropriate stretching videos and exercise menus to the user. The suggestion unit generates an optimal menu based on the user's body type and age using, for example, a machine learning algorithm. The suggestion unit can also advise on meal menus and supplements based on the user's goals. The reminder unit reminds the user of the timing of the exercise suggested by the suggestion unit. The reminder unit notifies the user of the timing of the exercise using, for example, a notification system. The reminder unit can also adjust the timing of the reminder to match the user's lifestyle. The form analysis unit analyzes videos taken by the user and identifies differences in form and areas for improvement. The form analysis unit analyzes the user's exercise form using, for example, an image analysis algorithm. The form analysis unit can also use a feedback system to notify the user of areas for improvement. As a result, the health management system according to the embodiment can support the user's exercise by collecting user information, suggesting optimal stretching videos and exercise menus, reminding the user of exercise timing, and pointing out differences in form and areas for improvement.

[0030] The suggestion unit can use a generation AI to suggest appropriate stretching videos and exercise menus according to the user's body type and age. For example, the suggestion unit uses a generation AI to suggest optimal stretching videos and exercise menus according to the user's body type and age. The generation AI generates an optimal menu based on the user's body type and age, for example, using a machine learning algorithm. The suggestion unit can also adjust exercise intensity and frequency according to the user's body type and age. For example, the suggestion unit suggests an exercise menu suitable for the user based on BMI and exercise intensity for each age group. This makes it possible to provide individually optimized exercise by suggesting optimal stretching videos and exercise menus according to the user's body type and age.

[0031] The reminding unit can remind the user when to exercise in accordance with their lifestyle. For example, the reminding unit reminds the user when to exercise in accordance with their lifestyle. The reminding unit can analyze the user's lifestyle and set the optimal reminder timing. For example, the reminding unit reminds the user when to exercise based on the user's sleep patterns and daily activity times. The reminding unit can also adjust the frequency of reminders based on the user's exercise habits. This can support the user in continuing to exercise by reminding the user when to exercise in accordance with their lifestyle.

[0032] The form analysis unit can analyze videos taken by the user and point out differences in form and areas for improvement. For example, the form analysis unit analyzes videos taken by the user and points out differences in form and areas for improvement. The form analysis unit uses an image analysis algorithm to analyze the user's exercise form. For example, the form analysis unit analyzes the correct form based on the resolution and shooting angle of the taken video. The form analysis unit can also use a feedback system to notify the user of areas for improvement. For example, the form analysis unit points out differences in the user's form and shows the correct form. The form analysis unit can also change the exercise menu as needed. In this way, by analyzing the videos taken by the user and pointing out differences in form and areas for improvement, it is possible to encourage the user to exercise with the correct form.

[0033] The suggestion unit can use generation AI to advise on meal menus and supplements that suit the user's goals, body type, and age. For example, the suggestion unit uses generation AI to advise on meal menus and supplements that suit the user's goals, body type, and age. The generation AI suggests optimal meal menus and supplements based on the user's goals, body type, and age. For example, the suggestion unit can suggest a meal menu that takes into account calorie restriction and nutritional balance to a user who is dieting. It can also suggest a meal menu and supplements that are high in protein to a user who is trying to build muscle. This makes it possible to support comprehensive health management by advising on meal menus and supplements that suit the user's goals, body type, age, etc.

[0034] The suggestion unit can use generation AI to advise on meal menus and supplements based on the user's allergy information and preferences. For example, the suggestion unit uses generation AI to advise on meal menus and supplements based on the user's allergy information and preferences. The generation AI suggests optimal meal menus and supplements based on the user's allergy information and preferences. For example, the suggestion unit suggests meal menus that avoid allergies based on allergy test results and user preference surveys. The suggestion unit can also suggest meal menus and supplements that suit the user's preferences. This makes it possible to provide individually optimized health management by advising on meal menus and supplements based on the user's allergy information and preferences.

[0035] The reminder unit can be set to match the user's lifestyle rhythm. The reminder unit can be set to match the user's lifestyle rhythm, for example. The reminder unit can analyze the user's lifestyle rhythm and set the optimal reminder timing. For example, the reminder unit reminds the user to exercise based on the user's schedule and daily activity patterns. The reminder unit can also adjust the frequency of reminders based on the user's lifestyle rhythm. This allows for flexible settings to match the user's lifestyle rhythm, thereby supporting continued exercise.

[0036] The form analysis unit can check whether the stretching is being performed with the correct form and change the menu as necessary. For example, the form analysis unit can check whether the stretching is being performed with the correct form and change the menu as necessary. The form analysis unit uses an image analysis algorithm to analyze the user's exercise form. For example, the form analysis unit can analyze the correct form based on the resolution and shooting angle of the captured video. The form analysis unit can also use a feedback system to notify the user of areas for improvement. For example, the form analysis unit can point out differences in the user's form and show the correct form. The form analysis unit can also change the exercise menu as necessary. This allows the user to check whether the stretching is being performed with the correct form and change the menu as necessary, thereby supporting effective exercise.

[0037] The suggestion unit can use the generation AI to suggest features that promote character settings and connections with other users. For example, the suggestion unit uses the generation AI to suggest features that promote character settings and connections with other users. The generation AI suggests optimal character settings and connections based on user information. For example, the suggestion unit suggests avatar customization and social functions. The suggestion unit can also suggest features that promote competition and mutual encouragement between users. This can support continued exercise by promoting character settings and connections with other users.

[0038] The suggestion unit can suggest chat functions and ranking functions using the generation AI. The suggestion unit, for example, suggests chat functions and ranking functions using the generation AI. The generation AI suggests optimal chat functions and ranking functions based on user information. For example, the suggestion unit suggests real-time chat and ranking update frequency. The suggestion unit can also suggest functions that promote communication between users. In this way, by suggesting chat functions and ranking functions, it is possible to improve user motivation.

[0039] The collection unit can analyze the user's past exercise history and select the optimal information collection method. For example, the collection unit analyzes the user's past exercise history and selects the optimal information collection method. The collection unit saves and analyzes the user's exercise log and history data. For example, the collection unit selects the optimal information collection method based on the frequency and type of exercise the user has performed in the past. The collection unit can also collect information for suggesting an effective exercise menu from the user's past exercise history. For example, the collection unit analyzes the user's exercise history and collects information for maximizing the effects of exercise. In this way, the optimal information collection method can be selected by analyzing the user's past exercise history.

[0040] The collection unit can perform filtering based on the user's current health condition and lifestyle habits when collecting information. For example, the collection unit performs filtering based on the user's current health condition and lifestyle habits when collecting information. The collection unit collects and analyzes the user's health checkup results and daily dietary details. For example, the collection unit collects information for proposing an appropriate exercise menu based on the user's current health condition. The collection unit can also collect information for proposing a reasonable exercise menu taking into account the user's lifestyle habits. For example, the collection unit collects information for minimizing risks based on the user's health condition and lifestyle habits. In this way, more appropriate information can be collected by filtering information based on the user's current health condition and lifestyle habits.

[0041] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. When the user provides information by methods such as voice input, text input, and image input, the collection unit selects the appropriate collection means for each method. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Also, when the user uses text input, the collection unit can collect information using natural language processing technology. Furthermore, when the user uses image input, the collection unit can collect information using image analysis technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0042] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The collection unit collects the user's geographical location information using GPS data or a location information service. For example, when the user is in their current location, the collection unit prioritizes collecting exercise menus related to the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting exercise menus that can be performed at the user's travel destination. Furthermore, the collection unit can also prioritize collecting region-specific health information based on the user's geographical location information. In this way, by prioritizing collecting highly relevant information by taking into account the user's geographical location information, more appropriate information can be provided.

[0043] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit analyzes the user's social media activity and collects related information when collecting information. The collection unit collects and analyzes the content of the user's social media posts and the number of likes. For example, the collection unit collects related information based on exercise menus shared by the user on social media. The collection unit can also analyze the user's social media activity and collect exercise menus that the user may be interested in. Furthermore, the collection unit can also collect related information by referring to the activity of the user's friends on social media. In this way, related information can be collected efficiently by analyzing the user's social media activity.

[0044] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. The collection unit customizes the type of information to be collected based on feedback provided by the user in the past. For example, the collection unit collects and analyzes survey results and user comments. The collection unit can also improve the collection method by reflecting the user's past feedback. Furthermore, the collection unit can improve the accuracy of the information to be collected based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and more appropriate information can be collected.

[0045] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the exercise menu when suggesting the exercise menu. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the exercise menu when suggesting the exercise menu. The suggestion unit evaluates the importance of the exercise menu and adjusts the level of detail of the suggestion. For example, the suggestion unit makes a suggestion including a detailed explanation for an important exercise menu. Also, the suggestion unit can make a suggestion with a concise explanation for an easy exercise menu. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance according to the user's purpose. In this way, by adjusting the level of detail of the suggestion based on the importance of the exercise menu, it is possible to provide optimal information for the user.

[0046] The suggestion unit can apply different suggestion algorithms depending on the category of the exercise menu when suggesting the menu. For example, the suggestion unit applies different suggestion algorithms depending on the category of the exercise menu when suggesting the menu. The suggestion unit classifies the categories of exercise menus and applies a suggestion algorithm appropriate for each. For example, the suggestion unit applies a suggestion algorithm specialized for improving flexibility to a stretching menu. The suggestion unit can also apply a suggestion algorithm specialized for improving muscle strength to a muscle training menu. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for correcting posture to a posture improvement menu. In this way, by applying different suggestion algorithms depending on the category of the exercise menu, more effective suggestions can be made.

[0047] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit collects and analyzes the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on the results of suggestions the user has received in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. Furthermore, the suggestion unit can reflect the user's feedback and improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0048] The suggestion unit can determine the priority of the suggestions based on the time of submission of the exercise menu when making the suggestions. For example, the suggestion unit determines the priority of the suggestions based on the time of submission of the exercise menu when making the suggestions. The suggestion unit evaluates the time of submission of the exercise menu and determines the priority of the suggestions. For example, the suggestion unit prioritizes suggestions for exercise menus with high urgency. The suggestion unit can also prioritize suggestions for exercise menus with upcoming submission dates. Furthermore, the suggestion unit can also determine the priority of the suggestions based on the user's schedule. In this way, by determining the priority of the suggestions based on the time of submission of the exercise menu, suggestions can be made at the optimal time for the user.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the exercise menus when suggesting them. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the exercise menus when suggesting them. The suggestion unit evaluates the relevance of the exercise menus and adjusts the order of suggestions. For example, the suggestion unit preferentially suggests highly relevant exercise menus. The suggestion unit can also suggest highly relevant exercise menus according to the user's purpose. Furthermore, the suggestion unit can also suggest highly relevant exercise menus based on the user's past exercise history. In this way, by adjusting the order of suggestions based on the relevance of the exercise menus, suggestions can be made in the order optimal for the user.

[0050] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit evaluates the user's level of expertise and adjusts the use of technical terms in the proposal. For example, if the user is a beginner, the suggestion unit can make a proposal that avoids technical terms. Also, if the user is an intermediate user, the suggestion unit can make a proposal that uses appropriate technical terms. Furthermore, if the user is an advanced user, the suggestion unit can make a proposal that uses a lot of technical terms. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to make a proposal that is easier to understand.

[0051] The reminding unit can select the optimal reminder method by referring to the user's past exercise history when reminding. For example, the reminding unit selects the optimal reminder method by referring to the user's past exercise history when reminding. The reminding unit saves and analyzes the user's exercise log and history data. For example, the reminding unit selects the optimal reminder method based on the frequency and type of exercise the user has done in the past. The reminding unit can also select an effective reminder method from the user's past exercise history. Furthermore, the reminding unit can analyze the user's exercise history and select a reminder method that maximizes the effect of exercise. In this way, the optimal reminder method can be selected by referring to the user's past exercise history.

[0052] The reminding unit can analyze the user's lifestyle rhythm at the time of a reminder and suggest the optimal reminder time. For example, the reminding unit can analyze the user's lifestyle rhythm at the time of a reminder and suggest the optimal reminder time. The reminding unit analyzes the user's lifestyle rhythm and sets the optimal reminder timing. For example, the reminding unit can suggest an appropriate reminder time based on the user's sleep patterns and daily activity times. The reminding unit can also analyze the user's lifestyle rhythm and suggest a reasonable reminder time. Furthermore, the reminding unit can adjust the timing of the reminder based on the user's lifestyle rhythm. In this way, the optimal reminder time can be suggested by analyzing the user's lifestyle rhythm.

[0053] The reminding unit can improve the reminding method by reflecting user feedback at the time of reminding. For example, the reminding unit improves the reminding method by reflecting user feedback at the time of reminding. The reminding unit improves the reminding method based on feedback previously provided by the user. For example, the reminding unit collects and analyzes survey results and user comments. The reminding unit can also adjust the timing of reminders by reflecting user feedback. Furthermore, the reminding unit can customize the content of reminders based on user feedback. In this way, the reminding method can be improved by reflecting user feedback, and more appropriate reminders can be provided.

[0054] The reminding unit can select the optimal reminding method by taking into account the user's geographical location information when giving a reminder. For example, the reminding unit selects the optimal reminding method by taking into account the user's geographical location information when giving a reminder. The reminding unit collects the user's geographical location information using GPS data or a location information service. For example, if the user is in their current location, the reminding unit can give a reminder related to that area. Also, if the user is traveling, the reminding unit can give a reminder that can be carried out at the user's travel destination. Furthermore, the reminding unit can give a region-specific reminder based on the user's geographical location information. This makes it possible to select the optimal reminding method by taking into account the user's geographical location information.

[0055] The reminder unit can analyze the user's social media activity at the time of a reminder and suggest a means of reminding. For example, the reminder unit analyzes the user's social media activity at the time of a reminder and suggests a means of reminding. The reminder unit collects and analyzes the content of the user's social media posts and the number of likes. For example, the reminder unit can suggest a means of reminding based on the exercise menu the user shared on social media. The reminder unit can also analyze the user's social media activity and suggest a means of reminding that may be of interest to the user. Furthermore, the reminder unit can also suggest a means of reminding based on the activity of the user's friends on social media. In this way, the optimal means of reminding can be suggested by analyzing the user's social media activity.

[0056] The reminding unit can customize the reminding method by reflecting the user's past feedback when reminding. For example, the reminding unit customizes the reminding method by reflecting the user's past feedback when reminding. The reminding unit customizes the reminding method based on feedback provided by the user in the past. For example, the reminding unit collects and analyzes survey results and user comments. The reminding unit can also adjust the timing of reminders by reflecting the user's feedback. Furthermore, the reminding unit can customize the content of reminders based on the user's feedback. In this way, the reminding method can be customized by reflecting the user's past feedback, allowing for more appropriate reminders.

[0057] The form analysis unit can analyze the user's past exercise form and select the optimal analysis method when analyzing the form. For example, the form analysis unit analyzes the user's past exercise form and selects the optimal analysis method when analyzing the form. The form analysis unit saves and analyzes the user's exercise log and history data. For example, the form analysis unit selects the optimal analysis method based on the user's past exercise form. The form analysis unit can also analyze the user's past exercise form and perform effective form analysis. Furthermore, the form analysis unit can also point out areas for improvement in the form based on the user's exercise form. In this way, the optimal analysis method can be selected by analyzing the user's past exercise form.

[0058] The form analysis unit can customize the analysis means based on the user's current health condition when analyzing the form. For example, the form analysis unit customizes the analysis means based on the user's current health condition when analyzing the form. The form analysis unit collects and analyzes the user's health checkup results and daily dietary details. For example, the form analysis unit performs appropriate form analysis based on the user's current health condition. The form analysis unit can also perform reasonable form analysis taking the user's health condition into consideration. Furthermore, the form analysis unit can perform form analysis to minimize risks based on the user's health condition. In this way, by customizing the analysis means based on the user's current health condition, more appropriate form analysis can be performed.

[0059] The form analysis unit can improve the analysis method by reflecting user feedback when analyzing a form. For example, the form analysis unit improves the analysis method by reflecting user feedback when analyzing a form. The form analysis unit improves the form analysis method based on feedback previously provided by the user. For example, the form analysis unit collects and analyzes survey results and user comments. The form analysis unit can also improve the accuracy of form analysis by reflecting user feedback. Furthermore, the form analysis unit can customize the content of the form analysis based on user feedback. In this way, the analysis method can be improved by reflecting user feedback, and more appropriate form analysis can be performed.

[0060] The form analysis unit can select the optimal analysis method by taking into account the user's geographical location information when analyzing a form. For example, the form analysis unit selects the optimal analysis method by taking into account the user's geographical location information when analyzing a form. The form analysis unit collects the user's geographical location information using GPS data or location information services. For example, if the user is in their current location, the form analysis unit performs form analysis related to that area. Also, if the user is traveling, the form analysis unit can perform form analysis that can be performed at the user's travel destination. Furthermore, the form analysis unit can perform region-specific form analysis based on the user's geographical location information. This makes it possible to select the optimal analysis method by taking into account the user's geographical location information.

[0061] The form analysis unit can analyze the user's social media activity and suggest a means of analysis when analyzing a form. For example, the form analysis unit analyzes the user's social media activity and suggests a means of analysis when analyzing a form. The form analysis unit collects and analyzes the content of the user's social media posts and the number of likes. For example, the form analysis unit can suggest a means of analysis based on the exercise form the user shared on social media. The form analysis unit can also analyze the user's social media activity and suggest a means of form analysis that may be of interest to the user. Furthermore, the form analysis unit can also suggest a means of form analysis based on the activity of the user's friends on social media. In this way, the optimal means of analysis can be suggested by analyzing the user's social media activity.

[0062] The form analysis unit can customize the analysis method by reflecting the user's past feedback when analyzing a form. For example, the form analysis unit customizes the analysis method by reflecting the user's past feedback when analyzing a form. The form analysis unit customizes the form analysis method based on feedback provided by the user in the past. For example, the form analysis unit collects and analyzes survey results and user comments. The form analysis unit can also reflect the user's feedback to improve the accuracy of the form analysis. Furthermore, the form analysis unit can customize the content of the form analysis based on the user's feedback. In this way, the analysis method can be customized by reflecting the user's past feedback, and more appropriate form analysis can be performed.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can also analyze the user's past exercise history and select the optimal information collection method. For example, the optimal information collection method is selected based on the frequency and type of exercise the user has performed in the past. The collection unit can also collect information from the user's past exercise history to suggest an effective exercise menu. Furthermore, the collection unit can analyze the user's exercise history and collect information to maximize the effectiveness of exercise. In this way, the optimal information collection method can be selected by analyzing the user's past exercise history.

[0065] The collection unit can also filter information based on the user's current health condition and lifestyle habits when collecting information. For example, the collection unit can collect and analyze the user's health checkup results and daily dietary information. The collection unit can also collect information for proposing an appropriate exercise menu based on the user's current health condition. Furthermore, the collection unit can also collect information for proposing a reasonable exercise menu taking into account the user's lifestyle habits. In this way, by filtering information based on the user's current health condition and lifestyle habits, more appropriate information can be collected.

[0066] The reminding unit can also refer to the user's past exercise history to select the optimal reminder method when reminding. For example, the optimal reminder method can be selected based on the frequency and type of exercise the user has done in the past. The reminding unit can also select an effective reminder method from the user's past exercise history. Furthermore, the reminder unit can analyze the user's exercise history and select a reminder method that maximizes the effect of exercise. In this way, the optimal reminder method can be selected by referring to the user's past exercise history.

[0067] The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the exercise menu when making the suggestion. For example, in the case of an important exercise menu, the suggestion unit can make a suggestion including a detailed explanation. In addition, in the case of an easy exercise menu, the suggestion unit can make a suggestion with a simple explanation. Furthermore, the level of detail of the suggestion based on the importance can be adjusted according to the user's purpose. In this way, by adjusting the level of detail of the suggestion based on the importance of the exercise menu, it is possible to provide the user with information that is optimal for the user.

[0068] When collecting information, the collection unit can also select the optimal collection means depending on the user's input method. For example, when the user provides information by voice input, text input, image input, or other methods, the collection unit selects the appropriate collection means for each. Furthermore, when the user uses voice input, information can be collected using voice recognition technology. Furthermore, when the user uses text input, information can be collected using natural language processing technology. This allows for efficient information collection by selecting the optimal collection means depending on the user's input method.

[0069] When analyzing form, the form analysis unit can also analyze the user's past exercise form and select the optimal analysis method. For example, the optimal analysis method is selected based on the user's past exercise form. It can also analyze the user's past exercise form and perform effective form analysis. It can also point out areas for form improvement based on the user's exercise form. This makes it possible to select the optimal analysis method by analyzing the user's past exercise form.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The collection unit collects user information, including health status, exercise history, dietary details, etc. The collection unit can also monitor the user's health status using sensors and store the information entered by the user in a database. Step 2: The suggestion unit uses the generation AI to analyze the information collected by the collection unit and propose appropriate stretching videos and exercise menus to the user. The suggestion unit uses a machine learning algorithm to generate optimal menus based on the user's body type and age, and can also provide advice on meal menus and supplements based on the user's goals. Step 3: The reminding unit reminds the user of the timing to perform the exercise suggested by the suggestion unit. The reminding unit uses a notification system to inform the user of the timing to exercise, and can also adjust the timing of the reminder to suit the user's daily rhythm. Step 4: The form analysis unit analyzes the video taken by the user and points out any differences in form and areas for improvement. The form analysis unit uses an image analysis algorithm to analyze the user's exercise form and can also use a feedback system to notify the user of areas for improvement.

[0072] (Example 2) A system according to an embodiment of the present invention proposes optimal stretching videos and exercise menus based on a user's goals, body type, and age. This system collects user information, analyzes it using a generation AI, proposes optimal stretching videos and exercise menus, reminds users when to exercise, and points out differences in form and areas for improvement. This allows the system to propose optimal stretching videos and exercise menus based on the user's goals, body type, and age, and support continued exercise. For example, based on information entered by the user, the generation AI proposes stretching videos to prevent stiff shoulders and exercise menus for muscle training. Furthermore, the system reminds users when to exercise and points out differences in form and areas for improvement when the user stretches or trains. This allows the user to exercise with correct form and achieve effective training. The system also provides advice on meal plans and supplements, supporting comprehensive health management. This allows the system to improve the user's health.

[0073] A health management system according to an embodiment includes a collection unit, a suggestion unit, a reminder unit, and a form analysis unit. The collection unit collects user information. The user information includes, but is not limited to, health status, exercise history, and dietary details. The collection unit monitors the user's health status using, for example, a sensor. The collection unit can also store the information entered by the user in a database. The suggestion unit analyzes the information collected by the collection unit using a generation AI and suggests appropriate stretching videos and exercise menus to the user. The suggestion unit generates an optimal menu based on the user's body type and age using, for example, a machine learning algorithm. The suggestion unit can also advise on meal menus and supplements based on the user's goals. The reminder unit reminds the user of the timing of the exercise suggested by the suggestion unit. The reminder unit notifies the user of the timing of the exercise using, for example, a notification system. The reminder unit can also adjust the timing of the reminder to match the user's lifestyle. The form analysis unit analyzes videos taken by the user and identifies differences in form and areas for improvement. The form analysis unit analyzes the user's exercise form using, for example, an image analysis algorithm. The form analysis unit can also use a feedback system to notify the user of areas for improvement. As a result, the health management system according to the embodiment can support the user's exercise by collecting user information, suggesting optimal stretching videos and exercise menus, reminding the user of exercise timing, and pointing out differences in form and areas for improvement.

[0074] The suggestion unit can use a generation AI to suggest appropriate stretching videos and exercise menus according to the user's body type and age. For example, the suggestion unit uses a generation AI to suggest optimal stretching videos and exercise menus according to the user's body type and age. The generation AI generates an optimal menu based on the user's body type and age, for example, using a machine learning algorithm. The suggestion unit can also adjust exercise intensity and frequency according to the user's body type and age. For example, the suggestion unit suggests an exercise menu suitable for the user based on BMI and exercise intensity for each age group. This makes it possible to provide individually optimized exercise by suggesting optimal stretching videos and exercise menus according to the user's body type and age.

[0075] The reminding unit can remind the user when to exercise in accordance with their lifestyle. For example, the reminding unit reminds the user when to exercise in accordance with their lifestyle. The reminding unit can analyze the user's lifestyle and set the optimal reminder timing. For example, the reminding unit reminds the user when to exercise based on the user's sleep patterns and daily activity times. The reminding unit can also adjust the frequency of reminders based on the user's exercise habits. This can support the user in continuing to exercise by reminding the user when to exercise in accordance with their lifestyle.

[0076] The form analysis unit can analyze videos taken by the user and point out differences in form and areas for improvement. For example, the form analysis unit analyzes videos taken by the user and points out differences in form and areas for improvement. The form analysis unit uses an image analysis algorithm to analyze the user's exercise form. For example, the form analysis unit analyzes the correct form based on the resolution and shooting angle of the taken video. The form analysis unit can also use a feedback system to notify the user of areas for improvement. For example, the form analysis unit points out differences in the user's form and shows the correct form. The form analysis unit can also change the exercise menu as needed. In this way, by analyzing the videos taken by the user and pointing out differences in form and areas for improvement, it is possible to encourage the user to exercise with the correct form.

[0077] The suggestion unit can use generation AI to advise on meal menus and supplements that suit the user's goals, body type, and age. For example, the suggestion unit uses generation AI to advise on meal menus and supplements that suit the user's goals, body type, and age. The generation AI suggests optimal meal menus and supplements based on the user's goals, body type, and age. For example, the suggestion unit can suggest a meal menu that takes into account calorie restriction and nutritional balance to a user who is dieting. It can also suggest a meal menu and supplements that are high in protein to a user who is trying to build muscle. This makes it possible to support comprehensive health management by advising on meal menus and supplements that suit the user's goals, body type, age, etc.

[0078] The suggestion unit can use generation AI to advise on meal menus and supplements based on the user's allergy information and preferences. For example, the suggestion unit uses generation AI to advise on meal menus and supplements based on the user's allergy information and preferences. The generation AI suggests optimal meal menus and supplements based on the user's allergy information and preferences. For example, the suggestion unit suggests meal menus that avoid allergies based on allergy test results and user preference surveys. The suggestion unit can also suggest meal menus and supplements that suit the user's preferences. This makes it possible to provide individually optimized health management by advising on meal menus and supplements based on the user's allergy information and preferences.

[0079] The reminder unit can be set to match the user's lifestyle rhythm. The reminder unit can be set to match the user's lifestyle rhythm, for example. The reminder unit can analyze the user's lifestyle rhythm and set the optimal reminder timing. For example, the reminder unit reminds the user to exercise based on the user's schedule and daily activity patterns. The reminder unit can also adjust the frequency of reminders based on the user's lifestyle rhythm. This allows for flexible settings to match the user's lifestyle rhythm, thereby supporting continued exercise.

[0080] The form analysis unit can check whether the stretching is being performed with the correct form and change the menu as necessary. For example, the form analysis unit can check whether the stretching is being performed with the correct form and change the menu as necessary. The form analysis unit uses an image analysis algorithm to analyze the user's exercise form. For example, the form analysis unit can analyze the correct form based on the resolution and shooting angle of the captured video. The form analysis unit can also use a feedback system to notify the user of areas for improvement. For example, the form analysis unit can point out differences in the user's form and show the correct form. The form analysis unit can also change the exercise menu as necessary. This allows the user to check whether the stretching is being performed with the correct form and change the menu as necessary, thereby supporting effective exercise.

[0081] The suggestion unit can use the generation AI to suggest features that promote character settings and connections with other users. For example, the suggestion unit uses the generation AI to suggest features that promote character settings and connections with other users. The generation AI suggests optimal character settings and connections based on user information. For example, the suggestion unit suggests avatar customization and social functions. The suggestion unit can also suggest features that promote competition and mutual encouragement between users. This can support continued exercise by promoting character settings and connections with other users.

[0082] The suggestion unit can suggest chat functions and ranking functions using the generation AI. The suggestion unit, for example, suggests chat functions and ranking functions using the generation AI. The generation AI suggests optimal chat functions and ranking functions based on user information. For example, the suggestion unit suggests real-time chat and ranking update frequency. The suggestion unit can also suggest functions that promote communication between users. In this way, by suggesting chat functions and ranking functions, it is possible to improve user motivation.

[0083] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user's emotions. The collection unit estimates the user's emotions using an emotion estimation algorithm. For example, the collection unit estimates the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the collection unit adjusts the timing of information collection based on the user's emotions. For example, the collection unit collects information during a time period when the user is relaxed. Furthermore, if the user is feeling stressed, the collection unit can collect information during a time period when the user can relax. In this way, by adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time.

[0084] The collection unit can analyze the user's past exercise history and select the optimal information collection method. For example, the collection unit analyzes the user's past exercise history and selects the optimal information collection method. The collection unit saves and analyzes the user's exercise log and history data. For example, the collection unit selects the optimal information collection method based on the frequency and type of exercise the user has performed in the past. The collection unit can also collect information for suggesting an effective exercise menu from the user's past exercise history. For example, the collection unit analyzes the user's exercise history and collects information for maximizing the effects of exercise. In this way, the optimal information collection method can be selected by analyzing the user's past exercise history.

[0085] The collection unit can perform filtering based on the user's current health condition and lifestyle habits when collecting information. For example, the collection unit performs filtering based on the user's current health condition and lifestyle habits when collecting information. The collection unit collects and analyzes the user's health checkup results and daily dietary details. For example, the collection unit collects information for proposing an appropriate exercise menu based on the user's current health condition. The collection unit can also collect information for proposing a reasonable exercise menu taking into account the user's lifestyle habits. For example, the collection unit collects information for minimizing risks based on the user's health condition and lifestyle habits. In this way, more appropriate information can be collected by filtering information based on the user's current health condition and lifestyle habits.

[0086] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. When the user provides information by methods such as voice input, text input, and image input, the collection unit selects the appropriate collection means for each method. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Also, when the user uses text input, the collection unit can collect information using natural language processing technology. Furthermore, when the user uses image input, the collection unit can collect information using image analysis technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0087] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. The collection unit estimates the user's emotions using an emotion estimation algorithm. For example, the collection unit estimates the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit determines the priority of information to be collected based on the user's emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting information that can help the user relax. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting detailed information. In this way, by determining the priority of information to be collected based on the user's emotions, more appropriate information can be collected preferentially.

[0088] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The collection unit collects the user's geographical location information using GPS data or a location information service. For example, when the user is in their current location, the collection unit prioritizes collecting exercise menus related to the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting exercise menus that can be performed at the user's travel destination. Furthermore, the collection unit can also prioritize collecting region-specific health information based on the user's geographical location information. In this way, by prioritizing collecting highly relevant information by taking into account the user's geographical location information, more appropriate information can be provided.

[0089] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit analyzes the user's social media activity and collects related information when collecting information. The collection unit collects and analyzes the content of the user's social media posts and the number of likes. For example, the collection unit collects related information based on exercise menus shared by the user on social media. The collection unit can also analyze the user's social media activity and collect exercise menus that the user may be interested in. Furthermore, the collection unit can also collect related information by referring to the activity of the user's friends on social media. In this way, related information can be collected efficiently by analyzing the user's social media activity.

[0090] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. The collection unit customizes the type of information to be collected based on feedback provided by the user in the past. For example, the collection unit collects and analyzes survey results and user comments. The collection unit can also improve the collection method by reflecting the user's past feedback. Furthermore, the collection unit can improve the accuracy of the information to be collected based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and more appropriate information can be collected.

[0091] The suggestion unit can estimate the user's emotion and adjust the way in which the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the way in which the suggestion is expressed based on the estimated user's emotion. The suggestion unit estimates the user's emotion using an emotion estimation algorithm. For example, the suggestion unit estimates the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit adjusts the way in which the suggestion is expressed based on the user's emotion. For example, if the user is relaxed, the suggestion unit makes the suggestion using calm expression. Also, if the user is feeling stressed, the suggestion unit can make the suggestion using simple and easy-to-understand expression. In this way, by adjusting the way in which the suggestion is expressed based on the user's emotion, more appropriate suggestions can be made.

[0092] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the exercise menu when suggesting the exercise menu. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the exercise menu when suggesting the exercise menu. The suggestion unit evaluates the importance of the exercise menu and adjusts the level of detail of the suggestion. For example, the suggestion unit makes a suggestion including a detailed explanation for an important exercise menu. Also, the suggestion unit can make a suggestion with a concise explanation for an easy exercise menu. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance according to the user's purpose. In this way, by adjusting the level of detail of the suggestion based on the importance of the exercise menu, it is possible to provide optimal information for the user.

[0093] The suggestion unit can apply different suggestion algorithms depending on the category of the exercise menu when suggesting the menu. For example, the suggestion unit applies different suggestion algorithms depending on the category of the exercise menu when suggesting the menu. The suggestion unit classifies the categories of exercise menus and applies a suggestion algorithm appropriate for each. For example, the suggestion unit applies a suggestion algorithm specialized for improving flexibility to a stretching menu. The suggestion unit can also apply a suggestion algorithm specialized for improving muscle strength to a muscle training menu. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for correcting posture to a posture improvement menu. In this way, by applying different suggestion algorithms depending on the category of the exercise menu, more effective suggestions can be made.

[0094] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit collects and analyzes the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on the results of suggestions the user has received in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. Furthermore, the suggestion unit can reflect the user's feedback and improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0095] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit estimates the user's emotion using an emotion estimation algorithm. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit adjusts the length of the suggestion based on the user's emotion. For example, if the user is in a hurry, the suggestion unit can make a short, to-the-point suggestion. Also, if the user is relaxed, the suggestion unit can make a longer suggestion including detailed explanations. In this way, by adjusting the length of the suggestion based on the user's emotion, more appropriate suggestions can be made.

[0096] The suggestion unit can determine the priority of the suggestions based on the time of submission of the exercise menu when making the suggestions. For example, the suggestion unit determines the priority of the suggestions based on the time of submission of the exercise menu when making the suggestions. The suggestion unit evaluates the time of submission of the exercise menu and determines the priority of the suggestions. For example, the suggestion unit prioritizes suggestions for exercise menus with high urgency. The suggestion unit can also prioritize suggestions for exercise menus with upcoming submission dates. Furthermore, the suggestion unit can also determine the priority of the suggestions based on the user's schedule. In this way, by determining the priority of the suggestions based on the time of submission of the exercise menu, suggestions can be made at the optimal time for the user.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the exercise menus when suggesting them. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the exercise menus when suggesting them. The suggestion unit evaluates the relevance of the exercise menus and adjusts the order of suggestions. For example, the suggestion unit preferentially suggests highly relevant exercise menus. The suggestion unit can also suggest highly relevant exercise menus according to the user's purpose. Furthermore, the suggestion unit can also suggest highly relevant exercise menus based on the user's past exercise history. In this way, by adjusting the order of suggestions based on the relevance of the exercise menus, suggestions can be made in the order optimal for the user.

[0098] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit evaluates the user's level of expertise and adjusts the use of technical terms in the proposal. For example, if the user is a beginner, the suggestion unit can make a proposal that avoids technical terms. Also, if the user is an intermediate user, the suggestion unit can make a proposal that uses appropriate technical terms. Furthermore, if the user is an advanced user, the suggestion unit can make a proposal that uses a lot of technical terms. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to make a proposal that is easier to understand.

[0099] The reminding unit can estimate the user's emotions and adjust the timing of the reminder based on the estimated user's emotions. The reminding unit, for example, estimates the user's emotions and adjusts the timing of the reminder based on the estimated user's emotions. The reminding unit estimates the user's emotions using an emotion estimation algorithm. For example, the reminding unit estimates the user's emotions using facial expression recognition technology. The reminding unit can also estimate the user's emotions using voice analysis technology. Furthermore, the reminding unit adjusts the timing of the reminder based on the user's emotions. For example, if the user is feeling stressed, the reminder unit will remind the user during a time when the user can relax. Furthermore, if the user is relaxed, the reminding unit can also provide a detailed reminder. In this way, by adjusting the timing of the reminder based on the user's emotions, the reminder can be provided at a more appropriate time.

[0100] The reminding unit can select the optimal reminder method by referring to the user's past exercise history when reminding. For example, the reminding unit selects the optimal reminder method by referring to the user's past exercise history when reminding. The reminding unit saves and analyzes the user's exercise log and history data. For example, the reminding unit selects the optimal reminder method based on the frequency and type of exercise the user has done in the past. The reminding unit can also select an effective reminder method from the user's past exercise history. Furthermore, the reminding unit can analyze the user's exercise history and select a reminder method that maximizes the effect of exercise. In this way, the optimal reminder method can be selected by referring to the user's past exercise history.

[0101] The reminding unit can analyze the user's lifestyle rhythm at the time of a reminder and suggest the optimal reminder time. For example, the reminding unit can analyze the user's lifestyle rhythm at the time of a reminder and suggest the optimal reminder time. The reminding unit analyzes the user's lifestyle rhythm and sets the optimal reminder timing. For example, the reminding unit can suggest an appropriate reminder time based on the user's sleep patterns and daily activity times. The reminding unit can also analyze the user's lifestyle rhythm and suggest a reasonable reminder time. Furthermore, the reminding unit can adjust the timing of the reminder based on the user's lifestyle rhythm. In this way, the optimal reminder time can be suggested by analyzing the user's lifestyle rhythm.

[0102] The reminding unit can improve the reminding method by reflecting user feedback at the time of reminding. For example, the reminding unit improves the reminding method by reflecting user feedback at the time of reminding. The reminding unit improves the reminding method based on feedback previously provided by the user. For example, the reminding unit collects and analyzes survey results and user comments. The reminding unit can also adjust the timing of reminders by reflecting user feedback. Furthermore, the reminding unit can customize the content of reminders based on user feedback. In this way, the reminding method can be improved by reflecting user feedback, and more appropriate reminders can be provided.

[0103] The reminding unit can estimate the user's emotions and determine the priority of reminders based on the estimated user emotions. The reminding unit, for example, estimates the user's emotions and determines the priority of reminders based on the estimated user emotions. The reminding unit estimates the user's emotions using an emotion estimation algorithm. For example, the reminding unit estimates the user's emotions using facial expression recognition technology. The reminding unit can also estimate the user's emotions using voice analysis technology. Furthermore, the reminding unit determines the priority of reminders based on the user's emotions. For example, if the user is feeling stressed, the reminding unit prioritizes relaxing reminders. Furthermore, if the user is relaxed, the reminding unit can also prioritize detailed reminders. In this way, by determining the priority of reminders based on the user's emotions, more appropriate reminders can be provided.

[0104] The reminding unit can select the optimal reminding method by taking into account the user's geographical location information when giving a reminder. For example, the reminding unit selects the optimal reminding method by taking into account the user's geographical location information when giving a reminder. The reminding unit collects the user's geographical location information using GPS data or a location information service. For example, if the user is in their current location, the reminding unit can give a reminder related to that area. Also, if the user is traveling, the reminding unit can give a reminder that can be carried out at the user's travel destination. Furthermore, the reminding unit can give a region-specific reminder based on the user's geographical location information. This makes it possible to select the optimal reminding method by taking into account the user's geographical location information.

[0105] The reminder unit can analyze the user's social media activity at the time of a reminder and suggest a means of reminding. For example, the reminder unit analyzes the user's social media activity at the time of a reminder and suggests a means of reminding. The reminder unit collects and analyzes the content of the user's social media posts and the number of likes. For example, the reminder unit can suggest a means of reminding based on the exercise menu the user shared on social media. The reminder unit can also analyze the user's social media activity and suggest a means of reminding that may be of interest to the user. Furthermore, the reminder unit can also suggest a means of reminding based on the activity of the user's friends on social media. In this way, the optimal means of reminding can be suggested by analyzing the user's social media activity.

[0106] The reminding unit can customize the reminding method by reflecting the user's past feedback when reminding. For example, the reminding unit customizes the reminding method by reflecting the user's past feedback when reminding. The reminding unit customizes the reminding method based on feedback provided by the user in the past. For example, the reminding unit collects and analyzes survey results and user comments. The reminding unit can also adjust the timing of reminders by reflecting the user's feedback. Furthermore, the reminding unit can customize the content of reminders based on the user's feedback. In this way, the reminding method can be customized by reflecting the user's past feedback, allowing for more appropriate reminders.

[0107] The form analysis unit can estimate the user's emotions and adjust the form analysis method based on the estimated user's emotions. For example, the form analysis unit estimates the user's emotions and adjusts the form analysis method based on the estimated user's emotions. The form analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the form analysis unit estimates the user's emotions using facial expression recognition technology. The form analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the form analysis unit adjusts the form analysis method based on the user's emotions. For example, the form analysis unit performs detailed form analysis when the user is relaxed. The form analysis unit can also perform concise and easy-to-understand form analysis when the user is feeling stressed. In this way, by adjusting the form analysis method based on the user's emotions, more appropriate form analysis can be performed.

[0108] The form analysis unit can analyze the user's past exercise form and select the optimal analysis method when analyzing the form. For example, the form analysis unit analyzes the user's past exercise form and selects the optimal analysis method when analyzing the form. The form analysis unit saves and analyzes the user's exercise log and history data. For example, the form analysis unit selects the optimal analysis method based on the user's past exercise form. The form analysis unit can also analyze the user's past exercise form and perform effective form analysis. Furthermore, the form analysis unit can also point out areas for improvement in the form based on the user's exercise form. In this way, the optimal analysis method can be selected by analyzing the user's past exercise form.

[0109] The form analysis unit can customize the analysis means based on the user's current health condition when analyzing the form. For example, the form analysis unit customizes the analysis means based on the user's current health condition when analyzing the form. The form analysis unit collects and analyzes the user's health checkup results and daily dietary details. For example, the form analysis unit performs appropriate form analysis based on the user's current health condition. The form analysis unit can also perform reasonable form analysis taking the user's health condition into consideration. Furthermore, the form analysis unit can perform form analysis to minimize risks based on the user's health condition. In this way, by customizing the analysis means based on the user's current health condition, more appropriate form analysis can be performed.

[0110] The form analysis unit can improve the analysis method by reflecting user feedback when analyzing a form. For example, the form analysis unit improves the analysis method by reflecting user feedback when analyzing a form. The form analysis unit improves the form analysis method based on feedback previously provided by the user. For example, the form analysis unit collects and analyzes survey results and user comments. The form analysis unit can also improve the accuracy of form analysis by reflecting user feedback. Furthermore, the form analysis unit can customize the content of the form analysis based on user feedback. In this way, the analysis method can be improved by reflecting user feedback, and more appropriate form analysis can be performed.

[0111] The form analysis unit can estimate the user's emotions and determine the priority of form analysis based on the estimated user's emotions. The form analysis unit, for example, estimates the user's emotions and determines the priority of form analysis based on the estimated user's emotions. The form analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the form analysis unit estimates the user's emotions using facial expression recognition technology. The form analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the form analysis unit determines the priority of form analysis based on the user's emotions. For example, if the user is feeling stressed, the form analysis unit can prioritize form analysis that will help the user relax. Furthermore, if the user is relaxed, the form analysis unit can also prioritize detailed form analysis. In this way, by determining the priority of form analysis based on the user's emotions, more appropriate form analysis can be performed.

[0112] The form analysis unit can select the optimal analysis method by taking into account the user's geographical location information when analyzing a form. For example, the form analysis unit selects the optimal analysis method by taking into account the user's geographical location information when analyzing a form. The form analysis unit collects the user's geographical location information using GPS data or location information services. For example, if the user is in their current location, the form analysis unit performs form analysis related to that area. Also, if the user is traveling, the form analysis unit can perform form analysis that can be performed at the user's travel destination. Furthermore, the form analysis unit can perform region-specific form analysis based on the user's geographical location information. This makes it possible to select the optimal analysis method by taking into account the user's geographical location information.

[0113] The form analysis unit can analyze the user's social media activity and suggest a means of analysis when analyzing a form. For example, the form analysis unit analyzes the user's social media activity and suggests a means of analysis when analyzing a form. The form analysis unit collects and analyzes the content of the user's social media posts and the number of likes. For example, the form analysis unit can suggest a means of analysis based on the exercise form the user shared on social media. The form analysis unit can also analyze the user's social media activity and suggest a means of form analysis that may be of interest to the user. Furthermore, the form analysis unit can also suggest a means of form analysis based on the activity of the user's friends on social media. In this way, the optimal means of analysis can be suggested by analyzing the user's social media activity.

[0114] The form analysis unit can customize the analysis method by reflecting the user's past feedback when analyzing a form. For example, the form analysis unit customizes the analysis method by reflecting the user's past feedback when analyzing a form. The form analysis unit customizes the form analysis method based on feedback provided by the user in the past. For example, the form analysis unit collects and analyzes survey results and user comments. The form analysis unit can also reflect the user's feedback to improve the accuracy of the form analysis. Furthermore, the form analysis unit can customize the content of the form analysis based on the user's feedback. In this way, the analysis method can be customized by reflecting the user's past feedback, and more appropriate form analysis can be performed. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, suggestion unit, reminder unit, and form analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit monitors the user's health condition using the sensors and camera 42 of the smart device 14 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to suggest appropriate stretching videos and exercise menus to the user. The reminder unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user of exercise timing using a notification system. The form analysis unit, for example, analyzes videos taken using the camera 42 of the smart device 14 by the specific processing unit 290 of the data processing device 12 and points out differences in form and areas for improvement. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, reminder unit, and form analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit monitors the user's health condition using the sensors and camera 42 of the smart glasses 214 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate stretching videos and exercise menus to the user using a generation AI. The reminder unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user of exercise timing using a notification system. The form analysis unit, for example, analyzes videos taken using the camera 42 of the smart glasses 214 by the specific processing unit 290 of the data processing device 12 and points out differences in form and areas for improvement. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, suggestion unit, reminder unit, and form analysis unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit monitors the user's health condition using the sensors and camera 42 of the headset terminal 314 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to suggest appropriate stretching videos and exercise menus to the user. The reminder unit is realized, for example, by the control unit 46A of the headset terminal 314 and notifies the user of exercise timing using a notification system. The form analysis unit, for example, analyzes videos taken using the camera 42 of the headset terminal 314 by the specific processing unit 290 of the data processing device 12 and points out differences in form and areas for improvement. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, suggestion unit, reminder unit, and form analysis unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit monitors the user's health condition using the robot 414's sensors and camera 42 and analyzes the collected information by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses generative AI to suggest appropriate stretching videos and exercise menus to the user. The reminder unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user of exercise timing using a notification system. The form analysis unit, for example, analyzes videos taken using the robot 414's camera 42 by the specific processing unit 290 of the data processing device 12 and points out differences in form and areas for improvement.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The suggestion unit can also estimate the user's emotions and adjust the content of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest a stretching video that has a relaxing effect. If the user wants to increase their motivation, it can also suggest an energetic exercise menu. Furthermore, if the user is feeling tired, it can also suggest a lighter exercise menu. In this way, it is possible to make optimal suggestions according to the user's emotions.

[0117] The collection unit can also analyze the user's past exercise history and select the optimal information collection method. For example, the optimal information collection method is selected based on the frequency and type of exercise the user has performed in the past. The collection unit can also collect information from the user's past exercise history to suggest an effective exercise menu. Furthermore, the collection unit can analyze the user's exercise history and collect information to maximize the effectiveness of exercise. In this way, the optimal information collection method can be selected by analyzing the user's past exercise history.

[0118] The reminder unit can also estimate the user's emotions and adjust the timing of reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder can be set during a time when the user is able to relax. Also, if the user is relaxed, a detailed reminder can be set. Furthermore, if the user wants to increase their motivation, a reminder including an encouraging message can be set. In this way, by adjusting the timing of reminders based on the user's emotions, reminders can be set at more appropriate times.

[0119] The form analysis unit can also estimate the user's emotions and adjust the form analysis method based on the estimated user emotions. For example, if the user is relaxed, detailed form analysis can be performed. Alternatively, if the user is feeling stressed, concise and easy-to-understand form analysis can be performed. Furthermore, if the user wants to increase their motivation, feedback including an encouraging message can be provided. This allows for more appropriate form analysis by adjusting the form analysis method based on the user's emotions.

[0120] The suggestion unit can also estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make suggestions using gentle expressions. If the user is feeling stressed, the suggestion unit can make suggestions using simple and easy-to-understand expressions. Furthermore, if the user wants to increase their motivation, the suggestion unit can make suggestions that include encouraging messages. In this way, by adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be made.

[0121] The collection unit can also filter information based on the user's current health condition and lifestyle habits when collecting information. For example, the collection unit can collect and analyze the user's health checkup results and daily dietary information. The collection unit can also collect information for proposing an appropriate exercise menu based on the user's current health condition. Furthermore, the collection unit can also collect information for proposing a reasonable exercise menu taking into account the user's lifestyle habits. In this way, by filtering information based on the user's current health condition and lifestyle habits, more appropriate information can be collected.

[0122] The reminding unit can also refer to the user's past exercise history to select the optimal reminder method when reminding. For example, the optimal reminder method can be selected based on the frequency and type of exercise the user has done in the past. The reminding unit can also select an effective reminder method from the user's past exercise history. Furthermore, the reminder unit can analyze the user's exercise history and select a reminder method that maximizes the effect of exercise. In this way, the optimal reminder method can be selected by referring to the user's past exercise history.

[0123] The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the exercise menu when making the suggestion. For example, in the case of an important exercise menu, the suggestion unit can make a suggestion including a detailed explanation. In addition, in the case of an easy exercise menu, the suggestion unit can make a suggestion with a simple explanation. Furthermore, the level of detail of the suggestion based on the importance can be adjusted according to the user's purpose. In this way, by adjusting the level of detail of the suggestion based on the importance of the exercise menu, it is possible to provide the user with information that is optimal for the user.

[0124] When collecting information, the collection unit can also select the optimal collection means depending on the user's input method. For example, when the user provides information by voice input, text input, image input, or other methods, the collection unit selects the appropriate collection means for each. Furthermore, when the user uses voice input, information can be collected using voice recognition technology. Furthermore, when the user uses text input, information can be collected using natural language processing technology. This allows for efficient information collection by selecting the optimal collection means depending on the user's input method.

[0125] When analyzing form, the form analysis unit can also analyze the user's past exercise form and select the optimal analysis method. For example, the optimal analysis method is selected based on the user's past exercise form. It can also analyze the user's past exercise form and perform effective form analysis. It can also point out areas for form improvement based on the user's exercise form. This makes it possible to select the optimal analysis method by analyzing the user's past exercise form.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The collection unit collects user information, including health status, exercise history, dietary details, etc. The collection unit can also monitor the user's health status using sensors and store the information entered by the user in a database. Step 2: The suggestion unit uses the generation AI to analyze the information collected by the collection unit and propose appropriate stretching videos and exercise menus to the user. The suggestion unit uses a machine learning algorithm to generate optimal menus based on the user's body type and age, and can also provide advice on meal menus and supplements based on the user's goals. Step 3: The reminding unit reminds the user of the timing to perform the exercise suggested by the suggestion unit. The reminding unit uses a notification system to inform the user of the timing to exercise, and can also adjust the timing of the reminder to suit the user's daily rhythm. Step 4: The form analysis unit analyzes the video taken by the user and points out any differences in form or areas for improvement. The form analysis unit uses an image analysis algorithm to analyze the user's exercise form and can also use a feedback system to notify the user of areas for improvement.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Explanation of symbols]

[0200] 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 collection unit that collects user information; a suggestion unit that analyzes the information collected by the collection unit and suggests appropriate stretching videos and exercise menus to the user; a reminding unit that reminds the user of the timing of performing the exercise suggested by the suggestion unit; and a form analysis unit that points out differences in form and areas for improvement when performing the exercise reminded by the reminding unit. A system characterized by:

2. The proposal unit Using AI, we suggest appropriate stretching videos and exercise menus based on the user's body type and age.

2. The system of claim 1.

3. The reminding unit Reminding users when to exercise based on their daily rhythm 2. The system of claim 1.

4. The form analysis unit Analyze videos taken by users and point out differences in form and areas for improvement 2. The system of claim 1.

5. The proposal unit AI generates meal menus and supplements tailored to goals, body type, and age, providing advice.

2. The system of claim 1.

6. The proposal unit Generates meal menus and supplements based on the user's allergy information and preferences, and provides advice using AI 2. The system of claim 1.

7. The reminding unit Can be set to suit the user's lifestyle 2. The system of claim 1.

8. The form analysis unit Check that you are using proper form and modify your stretches as needed.

2. The system of claim 1.

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