System

Abstract

A system according to an embodiment aims to provide an optimal training plan for an individual user, track progress, and provide feedback.SOLUTION: A system includes a collection unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects data of a lifestyle, a training habit, a pace of muscle increase, and a meal tendency of the user. The generation unit analyzes the data collected by the collection unit and generates a training plan suitable for the user. The tracking unit tracks the progress of the user based on the training plan generated by the generation unit. The feedback unit provides feedback of the achievement level based on the progress tracked by the tracking unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide optimal training plans for individual users, track progress, and provide feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal training plan for each individual user, track progress, and provide feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects data on a user's lifestyle, training habits, muscle growth rate, and dietary habits. The generation unit analyzes the data collected by the collection unit and generates a training plan suited to the user. The tracking unit tracks the user's progress based on the training plan generated by the generation unit. The feedback unit provides achievement feedback based on the progress tracked by the tracking unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal training plan for each individual user, track progress, and provide feedback. [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) An AI-based personal trainer system according to an embodiment of the present invention provides a personalized training plan based on a user's lifestyle, training habits, muscle growth rate, dietary habits, and other factors. The AI-based personal trainer system provides a personalized training plan based on the user's lifestyle and training habits, and tracks progress and provides feedback on achievement to help motivate the user and support effective training. For example, the AI-based personal trainer system collects data on the user's lifestyle, training habits, muscle growth rate, dietary habits, and other factors. The AI ​​then analyzes the collected data and generates an optimal training plan for the user. This training plan is customized based on the user's goals and current fitness level. Furthermore, the AI ​​tracks the user's progress and provides feedback on achievement. For example, the AI ​​displays the user's progress toward their set goals in real time and provides advice to help motivate the user. It also provides reminders to encourage the user to continue training and a reward system for achieving goals. This service can be integrated with the health and fitness functions of a body composition scale or a smartwatch. This allows the system to acquire data such as the user's body composition, heart rate, and calorie consumption in real time, enabling the system to provide more accurate training plans. For example, when a user uses a body composition scale to measure their weight and body fat percentage, the data is automatically sent to the AI, which then updates their training plan. Additionally, by monitoring their heart rate and calorie consumption using a smartwatch, they can adjust the intensity and content of their training in real time. In this way, the AI-based personal trainer system provides personalized training plans tailored to the user's lifestyle and training habits, and by tracking progress and providing feedback on achievements, it helps to motivate users and support effective training.This allows the AI-based personal trainer system to provide personalized training plans based on the user's lifestyle and training habits, and by tracking progress and providing feedback on achievement, it can keep users motivated and support effective training.

[0029] An AI-based personal trainer system according to an embodiment includes a collection unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects data on a user's lifestyle, training habits, muscle growth rate, and dietary trends. For example, the collection unit can collect data on the user's lifestyle, such as the user's daily activity level, sleep patterns, and type of work. The collection unit can also collect data on the user's training habits, such as the number of times the user trains per week and the type of training they do. The collection unit can also collect data on the user's muscle growth rate, such as the user's monthly muscle mass gain rate and changes in body fat percentage. The collection unit can also collect data on the user's dietary trends, such as the user's calorie intake, nutritional balance, and meal frequency. The generation unit analyzes the data collected by the collection unit and generates a training plan suitable for the user. For example, the generation unit generates a training plan based on the user's goals and current fitness level. The generation unit can customize the training plan based on the user's short-term goals, long-term goals, specific numerical goals, and the like. The generation unit may also generate a training plan based on the user's fitness level, such as the results of a physical fitness test or past training history. The tracking unit tracks the user's progress based on the training plan generated by the generation unit. For example, the tracking unit may track the user's progress, such as training achievement, weight change, and muscle mass increase, in real time. The tracking unit may track progress based on real-time criteria, such as data update frequency and latency. The feedback unit provides achievement feedback based on the progress tracked by the tracking unit. For example, the feedback unit may provide motivational advice based on the user's progress. The feedback unit may provide motivational advice, such as encouraging messages and rewards for achieving goals. The feedback unit may also provide the user with reminders to continue training and a reward system for achieving goals.The feedback unit can provide reminders based on reminder settings, such as notification timing and notification method (e.g., email, app notification, etc.). The feedback unit can also provide rewards based on a reward system, such as specific reward content and reward method for achieved goals. As a result, the AI-based personal trainer system according to the embodiment can provide a personalized training plan based on the user's lifestyle and training habits, track progress, and provide feedback on achievement, thereby maintaining the user's motivation and supporting effective training.

[0030] The collection unit can collect data from a body composition scale or a smartwatch. The collection unit can collect data such as body fat percentage and muscle mass from a body composition scale. A body composition scale has measurable items such as body fat percentage and muscle mass and can provide data with high accuracy. The collection unit can also collect data such as heart rate and calorie consumption from a smartwatch. A smartwatch has functions such as heart rate measurement, GPS, and activity tracker, and can monitor a user's health condition and activity level in detail. By collecting data from the body composition scale or smartwatch, a more accurate training plan can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from a body composition scale or smartwatch into a generation AI and have the generation AI analyze the data.

[0031] The generation unit can generate a training plan based on the user's goals and current fitness level. The generation unit can customize the training plan based on, for example, the user's short-term goals, long-term goals, specific numerical goals, etc. The generation unit can plan the training content and frequency necessary to achieve the user's goals. The generation unit can also generate a training plan based on the user's fitness level, such as the results of a physical fitness test or past training history. The generation unit can provide an optimal training plan based on the user's current physical strength and training habits. As a result, by generating a training plan based on the user's goals and current fitness level, an optimal training plan can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's goals and fitness level into the generation AI and cause the generation AI to generate a training plan.

[0032] The tracking unit can track the user's progress in real time. For example, the tracking unit can track the user's progress, such as the user's training achievement, weight change, and muscle mass increase, in real time. The tracking unit can track the progress based on real-time criteria, such as data update frequency and delay time. For example, the tracking unit can display the user's training achievement in real time to understand how far the user is progressing toward their goal. The tracking unit can also monitor the user's weight change and muscle mass increase in real time to track the user's progress in detail. This allows the user's training status to be constantly tracked by tracking the user's progress in real time. Some or all of the above-described processing in the tracking unit can be performed, for example, using AI or without AI. For example, the tracking unit can input the user's progress data to the generation AI and cause the generation AI to track the progress.

[0033] The feedback unit can provide advice to maintain the user's motivation based on the user's progress. The feedback unit can provide advice to maintain the user's motivation, such as encouraging messages and rewards for achieving goals, based on the user's progress. The feedback unit can display the user's progress toward a set goal in real time and provide advice to maintain the user's motivation. The feedback unit can also provide reminders to encourage the user to continue training and a reward system for achieved goals. The feedback unit can provide reminders based on the reminder setting method, such as the timing and method of notification (email, app notification, etc.). The feedback unit can also provide rewards based on a reward system, such as specific reward content and reward method for achieved goals. This makes it easier for the user to continue training by providing advice to maintain motivation based on the user's progress. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's progress data into a generation AI and cause the generation AI to generate advice to maintain motivation.

[0034] The feedback unit can provide a user with a reminder to continue training and a reward system for achieved goals. For example, the feedback unit can provide a reminder to continue training. The feedback unit can provide the reminder based on a reminder setting method, such as the timing and method of notification (email, app notification, etc.). The feedback unit can also provide a reward based on a reward system, such as specific reward content and reward method for achieved goals. For example, the feedback unit can provide rewards such as points or badges when the user achieves a set goal. The feedback unit can also send periodic reminders to keep the user motivated to continue training. This can maintain the user's motivation by providing a reminder to continue training and a reward system for achieved goals. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's progress data into a generation AI and cause the generation AI to generate reminders and a reward system.

[0035] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can preferentially suggest data collection methods (such as voice and text) that the user has frequently used in the past. The collection unit can also suggest the optimal collection method for a specific time period based on the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit can filter data based on the user's current health condition and activity level when collecting data. For example, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. The collection unit can also collect detailed data when the user is at a high activity level. Furthermore, if the user's health condition is poor, the collection unit can refrain from data collection and resume it after the user's health condition improves. This allows for more appropriate data to be collected by filtering data according to the user's health condition and activity level. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's health condition and activity level into a generation AI and have the generation AI perform data filtering.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user prefers text input, the collection unit can prioritize collecting text data. Furthermore, if the user prefers image input, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0039] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data regarding locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the collection method, for example, based on feedback provided by the user in the past. The collection unit can also select the optimal collection method from the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and customize the collection method. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the user's level of goal achievement. For example, if the user is approaching the goal, the generation unit can provide a detailed training plan. Also, if the user is far from the goal, the generation unit can provide a concise training plan. Furthermore, the generation unit can adjust the level of detail of the training plan according to the user's level of goal achievement. In this way, by adjusting the level of detail of the plan based on the user's level of goal achievement, it is possible to provide an optimal training plan for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's level of goal achievement into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0042] When generating a training plan, the generation unit can apply different generation algorithms depending on the user's fitness category. For example, if the user emphasizes strength training, the generation unit can apply a generation algorithm specialized for strength training. Furthermore, if the user emphasizes aerobic exercise, the generation unit can apply a generation algorithm specialized for aerobic exercise. Furthermore, if the user emphasizes flexibility, the generation unit can apply a generation algorithm specialized for flexibility. In this way, by applying different generation algorithms depending on the user's fitness category, it is possible to provide an optimal training plan for the user. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's fitness category into the generation AI and cause the generation AI to apply the generation algorithm.

[0043] When generating a training plan, the generation unit can improve the accuracy of the plan by referring to the user's past training results. The generation unit can, for example, analyze the user's past training results and generate an optimal training plan. The generation unit can also generate an effective training plan from the user's past training results. Furthermore, the generation unit can improve the accuracy of the training plan by referring to the user's past training results. In this way, the accuracy of the training plan can be improved by referring to the user's past training results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's past training results into the generation AI and cause the generation AI to improve the accuracy of the plan.

[0044] When generating a training plan, the generation unit can determine the priority of the plan based on the user's training history. For example, the generation unit can prioritize training that the user has frequently performed in the past into the plan. The generation unit can also prioritize effective training based on the user's training history into the plan. Furthermore, the generation unit can analyze the user's training history and determine the priority of the optimal training plan. This makes it possible to provide the optimal training plan for the user by determining the priority of the plan based on the user's training history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's training history into the generation AI and have the generation AI determine the priority of the plans.

[0045] When generating a training plan, the generation unit can adjust the order of the plan based on the user's related data. The generation unit can adjust the optimal order of the training plan based on the user's body composition data, for example. The generation unit can also adjust the order of the training plan based on the user's heart rate data. Furthermore, the generation unit can adjust the order of the training plan based on the user's calorie consumption data. In this way, by adjusting the order of the plans based on the user's related data, it is possible to provide an optimal training plan for the user. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's related data into the generation AI and have the generation AI adjust the order of the plans.

[0046] When generating a training plan, the generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user is a beginner, the generation unit can generate a training plan using simple technical terminology. Furthermore, if the user is an intermediate user, the generation unit can generate a training plan using appropriate technical terminology. Furthermore, if the user is an advanced user, the generation unit can generate a training plan using detailed technical terminology. By adjusting the use of technical terminology in the plan according to the user's level of expertise, a training plan that is easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The tracking unit can improve the accuracy of progress tracking by taking into account the user's training history. For example, the tracking unit can analyze the user's past training history to improve the accuracy of progress tracking. The tracking unit can also select an effective progress tracking method from the user's training history. Furthermore, the tracking unit can improve the accuracy of progress tracking by referring to the user's training history. In this way, the accuracy of progress tracking can be improved by taking the user's training history into consideration. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input data of the user's training history into the generation AI and cause the generation AI to improve the accuracy of progress tracking.

[0048] The tracking unit can take into account the user's attribute information when tracking progress. The tracking unit can adjust the progress tracking criteria, for example, by taking into account the user's age. The tracking unit can also adjust the progress tracking criteria by taking into account the user's gender. Furthermore, the tracking unit can adjust the progress tracking criteria by taking into account the user's weight and height. This allows for more appropriate progress tracking by taking into account the user's attribute information. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input data on the user's attribute information into the generation AI and cause the generation AI to adjust the progress tracking criteria.

[0049] The tracking unit can weight the progress tracking based on the user's training frequency when tracking the progress. For example, if the user trains frequently, the tracking unit can increase the weighting of the progress tracking. Also, if the user trains infrequently, the tracking unit can decrease the weighting of the progress tracking. Furthermore, the tracking unit can adjust the weighting of the progress tracking according to the user's training frequency. In this way, more appropriate progress tracking can be achieved by weighting the tracking based on the user's training frequency. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input data on the user's training frequency to the generation AI and cause the generation AI to adjust the weighting.

[0050] The tracking unit can take the user's geographical distribution into consideration when tracking progress. For example, when the user is in a specific area, the tracking unit can prioritize tracking progress related to that area. Furthermore, when the user is traveling, the tracking unit can prioritize tracking progress related to the travel destination. Furthermore, when the user is at home, the tracking unit can prioritize tracking progress related to the home. This allows for more appropriate progress tracking by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input data on the user's geographical distribution to the generation AI and cause the generation AI to adjust the progress tracking.

[0051] The tracking unit can improve the accuracy of progress tracking by referring to the user's related literature when tracking progress. The tracking unit can improve the accuracy of progress tracking, for example, based on literature referenced by the user. The tracking unit can also select an effective progress tracking method from the user's related literature. Furthermore, the tracking unit can improve the accuracy of progress tracking by referring to the user's related literature. In this way, the accuracy of progress tracking can be improved by referring to the user's related literature. Some or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI or without AI. For example, the tracking unit can input data on the user's related literature into the generation AI and cause the generation AI to improve the accuracy of progress tracking.

[0052] The tracking unit can take the user's market value into consideration when tracking progress. For example, if the user's market value is high, the tracking unit can increase the accuracy of progress tracking. Also, if the user's market value is low, the tracking unit can adjust the accuracy of progress tracking. Furthermore, the tracking unit can adjust the criteria for progress tracking according to the user's market value. This allows for more appropriate progress tracking by taking the user's market value into consideration. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input data on the user's market value into the generation AI and cause the generation AI to adjust the progress tracking.

[0053] When providing feedback, the feedback unit can optimize the current feedback by referring to past feedback data. The feedback unit can provide optimal feedback based on, for example, the user's past feedback data. The feedback unit can also select an effective feedback method from the user's past feedback data. Furthermore, the feedback unit can also optimize the feedback by referring to the user's past feedback data. In this way, the current feedback can be optimized by referring to the past feedback data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI and cause the generation AI to optimize the current feedback.

[0054] The feedback unit can apply different feedback techniques to each user category when providing feedback. For example, if the user is a beginner, the feedback unit can apply a simple feedback technique. Furthermore, if the user is an intermediate player, the feedback unit can apply a moderate feedback technique. Furthermore, if the user is an advanced player, the feedback unit can apply a detailed feedback technique. In this way, by applying different feedback techniques to each user category, optimal feedback can be provided to the user. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user category data into a generation AI and cause the generation AI to apply the feedback technique.

[0055] When providing feedback, the feedback unit can provide the feedback taking into consideration the user's attribute information. The feedback unit can provide the feedback taking into consideration, for example, the user's age. The feedback unit can also provide the feedback taking into consideration the user's gender. Furthermore, the feedback unit can provide the feedback taking into consideration the user's weight and height. In this way, by taking the user's attribute information into consideration, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input data of the user's attribute information into the generation AI and cause the generation AI to provide the feedback.

[0056] When providing feedback, the feedback unit can analyze changes in feedback based on the user's training history. The feedback unit can, for example, analyze changes in feedback based on the user's training history. The feedback unit can also select an effective feedback method from the user's training history. Furthermore, the feedback unit can analyze changes in feedback with reference to the user's training history. In this way, more effective feedback can be provided by analyzing changes in feedback based on the user's training history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data of the user's training history into a generation AI and cause the generation AI to analyze changes in feedback.

[0057] When providing feedback, the feedback unit can analyze the feedback by referring to market data related to the user. For example, the feedback unit can analyze the feedback based on the market data related to the user. The feedback unit can also select an effective feedback method from the market data related to the user. Furthermore, the feedback unit can analyze the feedback by referring to the market data related to the user. In this way, more effective feedback can be provided by referring to the market data related to the user. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the market data related to the user into a generation AI and cause the generation AI to analyze the feedback.

[0058] When providing feedback, the feedback unit can analyze the feedback taking into account the user's technical maturity. For example, if the user is a beginner, the feedback unit can provide simple feedback. Furthermore, if the user is an intermediate user, the feedback unit can provide moderate feedback. Furthermore, if the user is an advanced user, the feedback unit can provide detailed feedback. This allows for more appropriate feedback to be provided by taking the user's technical maturity into consideration. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the user's technical maturity into the generation AI and cause the generation AI to analyze the feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can collect data on the user's hobbies and interests in addition to the user's lifestyle and training habits. For example, the collection unit can collect data such as the user's preferred sports and activities, the type of music the user listens to, and the types of movies and TV shows the user watches. This allows the generation unit to generate a more enjoyable and sustainable training plan based on the user's hobbies and interests. For example, if the user likes to listen to music while training, the generation unit can incorporate the user's favorite music into the training plan. Also, if the user is interested in a particular sport, the generation unit can include training related to that sport in the plan. Furthermore, if the user likes to watch movies and TV shows, the generation unit can suggest content that can be viewed while training. This allows the training plan to be customized based on the user's hobbies and interests, thereby improving the enjoyment of training.

[0061] When generating a training plan for a user, the generation unit can adjust the plan based on the user's sociability and communication style. For example, if the user is sociable, the generation unit can recommend group training or partner training. Alternatively, if the user prefers training alone, the generation unit can provide an individual training plan. Furthermore, if the user prefers interacting with online communities, the generation unit can provide opportunities for the user to interact with other users through online forums or chat groups. By adjusting the training plan according to the user's sociability and communication style, the generation unit can support the user in continuing their training more comfortably.

[0062] The collection unit can collect data on the user's occupation and work schedule in addition to the user's lifestyle and training habits. For example, the collection unit can collect data such as the user's working hours, commuting time, and work stress level. This allows the generation unit to generate a training plan tailored to the user's work schedule. For example, if the user has a busy job, a short and effective training plan can be provided. Also, if the user works night shifts, a training plan that can be done at night can be suggested. Furthermore, if the user works a desk job, a training plan including simple exercises that can be done at the office can be provided. This makes it easier for the user to continue training by customizing the training plan based on the user's occupation and work schedule.

[0063] When generating a training plan for a user, the generation unit can adjust the plan based on the user's health condition and medical history. For example, if the user has a previously injured part of the body or has a chronic illness, the generation unit can provide a training plan that does not put strain on that part of the body. Also, if the user has a specific medical condition, the generation unit can provide a training plan that is suited to that condition. Furthermore, if the user is undergoing rehabilitation, the generation unit can provide a training plan that is suited to rehabilitation. In this way, by adjusting the training plan based on the user's health condition and medical history, it is possible to support the user in training safely.

[0064] The collection unit can collect data on the user's dietary preferences and allergy information in addition to the user's lifestyle and training habits. For example, the collection unit can collect data such as the user's preferred ingredients and allergies. This allows the generation unit to provide a more appropriate meal plan based on the user's dietary preferences and allergy information. For example, if the user prefers a particular ingredient, a meal plan including that ingredient can be suggested. Also, if the user is allergic to a particular ingredient, a meal plan that avoids that ingredient can be suggested. Furthermore, if the user needs to consume a particular nutrient, a meal plan including that nutrient can be suggested. This makes it easier for the user to maintain a healthy diet by customizing the meal plan based on the user's dietary preferences and allergy information.

[0065] The generation unit can reflect user feedback in real time when generating the user's training plan. For example, if the user provides feedback about dissatisfaction or areas for improvement felt during training, the generation unit can immediately reflect that feedback and adjust the training plan. Also, if the user prefers a particular training style, the generation unit can incorporate that training style into the plan. Furthermore, if the user provides feedback regarding the intensity and frequency of training, the generation unit can adjust the training plan based on that feedback. In this way, by reflecting user feedback in real time, it is possible to provide the user with an optimal training plan.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data on the user's lifestyle, training habits, muscle growth rate, and dietary trends. For example, the collection unit can collect data on the user's lifestyle, such as the user's daily activity level, sleep patterns, and type of work. The collection unit can also collect data on the user's training habits, such as how many times a week the user trains and what type of training the user does. Furthermore, the collection unit can collect data on the user's muscle growth rate, such as the user's monthly muscle mass gain rate and changes in body fat percentage. The collection unit can also collect data on the user's dietary trends, such as the user's calorie intake, nutritional balance, and meal frequency. Step 2: The generation unit analyzes the data collected by the collection unit and generates a training plan suitable for the user. For example, the generation unit generates a training plan based on the user's goals and current fitness level. The generation unit can customize the training plan based on the user's short-term goals, long-term goals, specific numerical goals, etc. The generation unit can also generate a training plan based on the user's fitness level, such as the results of a physical fitness test or past training history. Step 3: The tracking unit tracks the user's progress based on the training plan generated by the generation unit. For example, the tracking unit can track the user's progress in real time, such as training achievement, weight change, muscle mass increase, etc. The tracking unit can track the progress based on real-time criteria, such as data update frequency and latency. Step 4: The feedback unit provides achievement feedback based on the progress tracked by the tracking unit. For example, the feedback unit provides advice to maintain motivation based on the user's progress. The feedback unit can provide advice to maintain motivation, such as encouraging messages or rewards for achieving goals. The feedback unit can also provide reminders to encourage the user to continue training and a reward system for achieved goals. The feedback unit can provide reminders based on reminder settings, such as notification timing and notification method (email, app notification, etc.). The feedback unit can also provide rewards based on a reward system, such as specific reward content for achieved goals and a reward award method.

[0068] (Example 2) An AI-based personal trainer system according to an embodiment of the present invention provides a personalized training plan based on a user's lifestyle, training habits, muscle growth rate, dietary habits, and other factors. The AI-based personal trainer system provides a personalized training plan based on the user's lifestyle and training habits, and tracks progress and provides feedback on achievement to help motivate the user and support effective training. For example, the AI-based personal trainer system collects data on the user's lifestyle, training habits, muscle growth rate, dietary habits, and other factors. The AI ​​then analyzes the collected data and generates an optimal training plan for the user. This training plan is customized based on the user's goals and current fitness level. Furthermore, the AI ​​tracks the user's progress and provides feedback on achievement. For example, the AI ​​displays the user's progress toward their set goals in real time and provides advice to help motivate the user. It also provides reminders to encourage the user to continue training and a reward system for achieving goals. This service can be integrated with the health and fitness functions of a body composition scale or a smartwatch. This allows the system to acquire data such as the user's body composition, heart rate, and calorie consumption in real time, enabling the system to provide more accurate training plans. For example, when a user uses a body composition scale to measure their weight and body fat percentage, the data is automatically sent to the AI, which then updates their training plan. Additionally, by monitoring their heart rate and calorie consumption using a smartwatch, they can adjust the intensity and content of their training in real time. In this way, the AI-based personal trainer system provides personalized training plans tailored to the user's lifestyle and training habits, and by tracking progress and providing feedback on achievements, it helps to motivate users and support effective training.This allows the AI-based personal trainer system to provide personalized training plans based on the user's lifestyle and training habits, and by tracking progress and providing feedback on achievement, it can keep users motivated and support effective training.

[0069] An AI-based personal trainer system according to an embodiment includes a collection unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects data on a user's lifestyle, training habits, muscle growth rate, and dietary trends. For example, the collection unit can collect data on the user's lifestyle, such as the user's daily activity level, sleep patterns, and type of work. The collection unit can also collect data on the user's training habits, such as the number of times the user trains per week and the type of training they do. The collection unit can also collect data on the user's muscle growth rate, such as the user's monthly muscle mass gain rate and changes in body fat percentage. The collection unit can also collect data on the user's dietary trends, such as the user's calorie intake, nutritional balance, and meal frequency. The generation unit analyzes the data collected by the collection unit and generates a training plan suitable for the user. For example, the generation unit generates a training plan based on the user's goals and current fitness level. The generation unit can customize the training plan based on the user's short-term goals, long-term goals, specific numerical goals, and the like. The generation unit may also generate a training plan based on the user's fitness level, such as the results of a physical fitness test or past training history. The tracking unit tracks the user's progress based on the training plan generated by the generation unit. For example, the tracking unit may track the user's progress, such as training achievement, weight change, and muscle mass increase, in real time. The tracking unit may track progress based on real-time criteria, such as data update frequency and latency. The feedback unit provides achievement feedback based on the progress tracked by the tracking unit. For example, the feedback unit may provide motivational advice based on the user's progress. The feedback unit may provide motivational advice, such as encouraging messages and rewards for achieving goals. The feedback unit may also provide the user with reminders to continue training and a reward system for achieving goals.The feedback unit can provide reminders based on reminder settings, such as notification timing and notification method (e.g., email, app notification, etc.). The feedback unit can also provide rewards based on a reward system, such as specific reward content and reward method for achieved goals. As a result, the AI-based personal trainer system according to the embodiment can provide a personalized training plan based on the user's lifestyle and training habits, track progress, and provide feedback on achievement, thereby maintaining the user's motivation and supporting effective training.

[0070] The collection unit can collect data from a body composition scale or a smartwatch. The collection unit can collect data such as body fat percentage and muscle mass from a body composition scale. A body composition scale has measurable items such as body fat percentage and muscle mass and can provide data with high accuracy. The collection unit can also collect data such as heart rate and calorie consumption from a smartwatch. A smartwatch has functions such as heart rate measurement, GPS, and activity tracker, and can monitor a user's health condition and activity level in detail. By collecting data from the body composition scale or smartwatch, a more accurate training plan can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from a body composition scale or smartwatch into a generation AI and have the generation AI analyze the data.

[0071] The generation unit can generate a training plan based on the user's goals and current fitness level. The generation unit can customize the training plan based on, for example, the user's short-term goals, long-term goals, specific numerical goals, etc. The generation unit can plan the training content and frequency necessary to achieve the user's goals. The generation unit can also generate a training plan based on the user's fitness level, such as the results of a physical fitness test or past training history. The generation unit can provide an optimal training plan based on the user's current physical strength and training habits. As a result, by generating a training plan based on the user's goals and current fitness level, an optimal training plan can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's goals and fitness level into the generation AI and cause the generation AI to generate a training plan.

[0072] The tracking unit can track the user's progress in real time. For example, the tracking unit can track the user's progress, such as the user's training achievement, weight change, and muscle mass increase, in real time. The tracking unit can track the progress based on real-time criteria, such as data update frequency and delay time. For example, the tracking unit can display the user's training achievement in real time to understand how far the user is progressing toward their goal. The tracking unit can also monitor the user's weight change and muscle mass increase in real time to track the user's progress in detail. This allows the user's training status to be constantly tracked by tracking the user's progress in real time. Some or all of the above-described processing in the tracking unit can be performed, for example, using AI or without AI. For example, the tracking unit can input the user's progress data to the generation AI and cause the generation AI to track the progress.

[0073] The feedback unit can provide advice to maintain the user's motivation based on the user's progress. The feedback unit can provide advice to maintain the user's motivation, such as encouraging messages and rewards for achieving goals, based on the user's progress. The feedback unit can display the user's progress toward a set goal in real time and provide advice to maintain the user's motivation. The feedback unit can also provide reminders to encourage the user to continue training and a reward system for achieved goals. The feedback unit can provide reminders based on the reminder setting method, such as the timing and method of notification (email, app notification, etc.). The feedback unit can also provide rewards based on a reward system, such as specific reward content and reward method for achieved goals. This makes it easier for the user to continue training by providing advice to maintain motivation based on the user's progress. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's progress data into a generation AI and cause the generation AI to generate advice to maintain motivation.

[0074] The feedback unit can provide a user with a reminder to continue training and a reward system for achieved goals. For example, the feedback unit can provide a reminder to continue training. The feedback unit can provide the reminder based on a reminder setting method, such as the timing and method of notification (email, app notification, etc.). The feedback unit can also provide a reward based on a reward system, such as specific reward content and reward method for achieved goals. For example, the feedback unit can provide rewards such as points or badges when the user achieves a set goal. The feedback unit can also send periodic reminders to keep the user motivated to continue training. This can maintain the user's motivation by providing a reminder to continue training and a reward system for achieved goals. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's progress data into a generation AI and cause the generation AI to generate reminders and a reward system.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection and collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. This allows data collection at more appropriate times by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0076] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can preferentially suggest data collection methods (such as voice and text) that the user has frequently used in the past. The collection unit can also suggest the optimal collection method for a specific time period based on the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0077] The collection unit can filter data based on the user's current health condition and activity level when collecting data. For example, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. The collection unit can also collect detailed data when the user is at a high activity level. Furthermore, if the user's health condition is poor, the collection unit can refrain from data collection and resume it after the user's health condition improves. This allows for more appropriate data to be collected by filtering data according to the user's health condition and activity level. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's health condition and activity level into a generation AI and have the generation AI perform data filtering.

[0078] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user prefers text input, the collection unit can prioritize collecting text data. Furthermore, if the user prefers image input, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting relaxation-related data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting urgent data. Thus, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0080] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0081] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data regarding locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can adjust the collection method, for example, based on feedback provided by the user in the past. The collection unit can also select the optimal collection method from the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and customize the collection method. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0083] The generation unit can estimate the user's emotions and adjust the presentation of the training plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a training plan that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a training plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can also generate a training plan that adds visually stimulating effects. This allows the optimal training plan to be provided by adjusting the presentation of the training plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation of the training plan.

[0084] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the user's level of goal achievement. For example, if the user is approaching the goal, the generation unit can provide a detailed training plan. Also, if the user is far from the goal, the generation unit can provide a concise training plan. Furthermore, the generation unit can adjust the level of detail of the training plan according to the user's level of goal achievement. In this way, by adjusting the level of detail of the plan based on the user's level of goal achievement, it is possible to provide an optimal training plan for the user. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's level of goal achievement into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0085] When generating a training plan, the generation unit can apply different generation algorithms depending on the user's fitness category. For example, if the user emphasizes strength training, the generation unit can apply a generation algorithm specialized for strength training. Furthermore, if the user emphasizes aerobic exercise, the generation unit can apply a generation algorithm specialized for aerobic exercise. Furthermore, if the user emphasizes flexibility, the generation unit can apply a generation algorithm specialized for flexibility. In this way, by applying different generation algorithms depending on the user's fitness category, it is possible to provide an optimal training plan for the user. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's fitness category into the generation AI and cause the generation AI to apply the generation algorithm.

[0086] When generating a training plan, the generation unit can improve the accuracy of the plan by referring to the user's past training results. The generation unit can, for example, analyze the user's past training results and generate an optimal training plan. The generation unit can also generate an effective training plan from the user's past training results. Furthermore, the generation unit can improve the accuracy of the training plan by referring to the user's past training results. In this way, the accuracy of the training plan can be improved by referring to the user's past training results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's past training results into the generation AI and cause the generation AI to improve the accuracy of the plan.

[0087] The generation unit can estimate the user's emotions and adjust the length of the training plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point training plan. Furthermore, if the user is relaxed, the generation unit can generate a longer training plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a training plan with visually stimulating effects. This allows the user to be provided with an optimal training plan by adjusting the length of the training plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the training plan.

[0088] When generating a training plan, the generation unit can determine the priority of the plan based on the user's training history. For example, the generation unit can prioritize training that the user has frequently performed in the past into the plan. The generation unit can also prioritize effective training based on the user's training history into the plan. Furthermore, the generation unit can analyze the user's training history and determine the priority of the optimal training plan. This makes it possible to provide the optimal training plan for the user by determining the priority of the plan based on the user's training history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's training history into the generation AI and have the generation AI determine the priority of the plans.

[0089] When generating a training plan, the generation unit can adjust the order of the plan based on the user's related data. The generation unit can adjust the optimal order of the training plan based on the user's body composition data, for example. The generation unit can also adjust the order of the training plan based on the user's heart rate data. Furthermore, the generation unit can adjust the order of the training plan based on the user's calorie consumption data. In this way, by adjusting the order of the plans based on the user's related data, it is possible to provide an optimal training plan for the user. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's related data into the generation AI and have the generation AI adjust the order of the plans.

[0090] When generating a training plan, the generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user is a beginner, the generation unit can generate a training plan using simple technical terminology. Furthermore, if the user is an intermediate user, the generation unit can generate a training plan using appropriate technical terminology. Furthermore, if the user is an advanced user, the generation unit can generate a training plan using detailed technical terminology. By adjusting the use of technical terminology in the plan according to the user's level of expertise, a training plan that is easy for the user to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0091] The tracking unit can estimate the user's emotions and adjust the progress tracking criteria based on the estimated user emotions. For example, the tracking unit can relax the progress tracking criteria when the user is stressed. The tracking unit can also tighten the progress tracking criteria when the user is relaxed. Furthermore, the tracking unit can temporarily relax the progress tracking criteria when the user is in a hurry. By adjusting the progress tracking criteria according to the user's emotions, optimal progress tracking for the user can be achieved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the tracking unit can be performed using, for example, an AI, or without an AI. For example, the tracking unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the progress tracking criteria.

[0092] The tracking unit can improve the accuracy of progress tracking by taking into account the user's training history. For example, the tracking unit can analyze the user's past training history to improve the accuracy of progress tracking. The tracking unit can also select an effective progress tracking method from the user's training history. Furthermore, the tracking unit can improve the accuracy of progress tracking by referring to the user's training history. In this way, the accuracy of progress tracking can be improved by taking the user's training history into consideration. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input data of the user's training history into the generation AI and cause the generation AI to improve the accuracy of progress tracking.

[0093] The tracking unit can take into account the user's attribute information when tracking progress. The tracking unit can adjust the progress tracking criteria, for example, by taking into account the user's age. The tracking unit can also adjust the progress tracking criteria by taking into account the user's gender. Furthermore, the tracking unit can adjust the progress tracking criteria by taking into account the user's weight and height. This allows for more appropriate progress tracking by taking into account the user's attribute information. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input data on the user's attribute information into the generation AI and cause the generation AI to adjust the progress tracking criteria.

[0094] The tracking unit can weight the progress tracking based on the user's training frequency when tracking the progress. For example, if the user trains frequently, the tracking unit can increase the weighting of the progress tracking. Also, if the user trains infrequently, the tracking unit can decrease the weighting of the progress tracking. Furthermore, the tracking unit can adjust the weighting of the progress tracking according to the user's training frequency. In this way, more appropriate progress tracking can be achieved by weighting the tracking based on the user's training frequency. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input data on the user's training frequency to the generation AI and cause the generation AI to adjust the weighting.

[0095] The tracking unit can estimate the user's emotions and adjust the order in which the progress tracking results are displayed based on the estimated user emotions. For example, if the user is nervous, the tracking unit can display important results first. Furthermore, if the user is relaxed, the tracking unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the tracking unit can display key results first. By adjusting the order in which the progress tracking results are displayed according to the user's emotions, the results can be displayed in an order that is optimal for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the tracking unit can be performed using, for example, an AI, or without an AI. For example, the tracking unit can input the user's emotion data into the generation AI and have the generation AI adjust the order in which the results are displayed.

[0096] The tracking unit can take the user's geographical distribution into consideration when tracking progress. For example, when the user is in a specific area, the tracking unit can prioritize tracking progress related to that area. Furthermore, when the user is traveling, the tracking unit can prioritize tracking progress related to the travel destination. Furthermore, when the user is at home, the tracking unit can prioritize tracking progress related to the home. This allows for more appropriate progress tracking by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input data on the user's geographical distribution to the generation AI and cause the generation AI to adjust the progress tracking.

[0097] The tracking unit can improve the accuracy of progress tracking by referring to the user's related literature when tracking progress. The tracking unit can improve the accuracy of progress tracking, for example, based on literature referenced by the user. The tracking unit can also select an effective progress tracking method from the user's related literature. Furthermore, the tracking unit can improve the accuracy of progress tracking by referring to the user's related literature. In this way, the accuracy of progress tracking can be improved by referring to the user's related literature. Some or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI or without AI. For example, the tracking unit can input data on the user's related literature into the generation AI and cause the generation AI to improve the accuracy of progress tracking.

[0098] The tracking unit can take the user's market value into consideration when tracking progress. For example, if the user's market value is high, the tracking unit can increase the accuracy of progress tracking. Also, if the user's market value is low, the tracking unit can adjust the accuracy of progress tracking. Furthermore, the tracking unit can adjust the criteria for progress tracking according to the user's market value. This allows for more appropriate progress tracking by taking the user's market value into consideration. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input data on the user's market value into the generation AI and cause the generation AI to adjust the progress tracking.

[0099] The feedback unit can estimate the user's emotions and adjust the feedback display method based on the estimated user emotions. For example, if the user is nervous, the feedback unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the feedback unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the feedback unit can provide a display method that focuses on the main points. By adjusting the feedback display method according to the user's emotions, optimal feedback can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0100] When providing feedback, the feedback unit can optimize the current feedback by referring to past feedback data. The feedback unit can provide optimal feedback based on, for example, the user's past feedback data. The feedback unit can also select an effective feedback method from the user's past feedback data. Furthermore, the feedback unit can also optimize the feedback by referring to the user's past feedback data. In this way, the current feedback can be optimized by referring to the past feedback data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI and cause the generation AI to optimize the current feedback.

[0101] The feedback unit can apply different feedback techniques to each user category when providing feedback. For example, if the user is a beginner, the feedback unit can apply a simple feedback technique. Furthermore, if the user is an intermediate player, the feedback unit can apply a moderate feedback technique. Furthermore, if the user is an advanced player, the feedback unit can apply a detailed feedback technique. In this way, by applying different feedback techniques to each user category, optimal feedback can be provided to the user. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input user category data into a generation AI and cause the generation AI to apply the feedback technique.

[0102] When providing feedback, the feedback unit can provide the feedback taking into consideration the user's attribute information. The feedback unit can provide the feedback taking into consideration, for example, the user's age. The feedback unit can also provide the feedback taking into consideration the user's gender. Furthermore, the feedback unit can provide the feedback taking into consideration the user's weight and height. In this way, by taking the user's attribute information into consideration, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input data of the user's attribute information into the generation AI and cause the generation AI to provide the feedback.

[0103] The feedback unit can estimate the user's emotions and adjust the importance of feedback based on the estimated user emotions. For example, if the user is nervous, the feedback unit can prioritize providing important feedback. Furthermore, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide feedback that focuses on the main points. By adjusting the importance of feedback according to the user's emotions, optimal feedback can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of the feedback.

[0104] When providing feedback, the feedback unit can analyze changes in feedback based on the user's training history. The feedback unit can, for example, analyze changes in feedback based on the user's training history. The feedback unit can also select an effective feedback method from the user's training history. Furthermore, the feedback unit can analyze changes in feedback with reference to the user's training history. In this way, more effective feedback can be provided by analyzing changes in feedback based on the user's training history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data of the user's training history into a generation AI and cause the generation AI to analyze changes in feedback.

[0105] When providing feedback, the feedback unit can analyze the feedback by referring to market data related to the user. For example, the feedback unit can analyze the feedback based on the market data related to the user. The feedback unit can also select an effective feedback method from the market data related to the user. Furthermore, the feedback unit can analyze the feedback by referring to the market data related to the user. In this way, more effective feedback can be provided by referring to the market data related to the user. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the market data related to the user into a generation AI and cause the generation AI to analyze the feedback.

[0106] When providing feedback, the feedback unit can analyze the feedback taking into account the user's technical maturity. For example, if the user is a beginner, the feedback unit can provide simple feedback. Furthermore, if the user is an intermediate user, the feedback unit can provide moderate feedback. Furthermore, if the user is an advanced user, the feedback unit can provide detailed feedback. This allows for more appropriate feedback to be provided by taking the user's technical maturity into consideration. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the user's technical maturity into the generation AI and cause the generation AI to analyze the feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, tracking unit, and feedback unit, described above, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle and training habits using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the data processing device 12 via the control unit 46A. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and generates a training plan. The tracking unit, implemented, for example, by the control unit 46A of the smart device 14, tracks the user's progress in real time. The feedback unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides the user with feedback on their achievement. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, tracking unit, and feedback unit, described above, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle and training habits using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data to the data processing device 12 via the control unit 46A. The generation unit, implemented, for example, by a specific processing unit 290 of the data processing device 12, analyzes the collected data and generates a training plan. The tracking unit, implemented, for example, by the control unit 46A of the smart glasses 214, tracks the user's progress in real time. The feedback unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides the user with feedback on their achievement. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, tracking unit, and feedback unit is realized, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle and training habits using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate a training plan. The tracking unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and tracks the user's progress in real time. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the user with feedback on their achievement level. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, tracking unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle and training habits using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate a training plan. The tracking unit is realized, for example, by the control unit 46A of the robot 414 and tracks the user's progress in real time. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides the user with feedback on their achievement level.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The collection unit can collect data on the user's hobbies and interests in addition to the user's lifestyle and training habits. For example, the collection unit can collect data such as the user's preferred sports and activities, the type of music the user listens to, and the types of movies and TV shows the user watches. This allows the generation unit to generate a more enjoyable and sustainable training plan based on the user's hobbies and interests. For example, if the user likes to listen to music while training, the generation unit can incorporate the user's favorite music into the training plan. Also, if the user is interested in a particular sport, the generation unit can include training related to that sport in the plan. Furthermore, if the user likes to watch movies and TV shows, the generation unit can suggest content that can be viewed while training. This allows the training plan to be customized based on the user's hobbies and interests, thereby improving the enjoyment of training.

[0109] When generating a training plan for a user, the generation unit can adjust the plan based on the user's sociability and communication style. For example, if the user is sociable, the generation unit can recommend group training or partner training. Alternatively, if the user prefers training alone, the generation unit can provide an individual training plan. Furthermore, if the user prefers interacting with online communities, the generation unit can provide opportunities for the user to interact with other users through online forums or chat groups. By adjusting the training plan according to the user's sociability and communication style, the generation unit can support the user in continuing their training more comfortably.

[0110] When tracking the user's progress, the tracking unit can estimate the user's emotions and adjust the way the progress is displayed based on the estimated emotions. For example, if the user is feeling stressed, the progress display can be simplified and positive feedback can be emphasized. Alternatively, if the user is relaxed, detailed progress data can be provided and specific advice for the next step can be given. Furthermore, if the user is excited, the progress can be displayed using visually stimulating graphics and animations. In this way, adjusting the way the progress is displayed according to the user's emotions can make it easier for the user to maintain motivation.

[0111] When providing advice to maintain motivation based on the user's progress, the feedback unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling down, the feedback unit can provide an encouraging message or advice to celebrate small successes. If the user is feeling confident, the feedback unit can suggest setting specific goals for the next challenge. Furthermore, if the user is feeling impatient, the feedback unit can provide advice to help them relax or methods to relieve stress. In this way, adjusting the content of the advice according to the user's emotions makes it easier for the user to continue training.

[0112] The collection unit can collect data on the user's occupation and work schedule in addition to the user's lifestyle and training habits. For example, the collection unit can collect data such as the user's working hours, commuting time, and work stress level. This allows the generation unit to generate a training plan tailored to the user's work schedule. For example, if the user has a busy job, a short and effective training plan can be provided. Also, if the user works night shifts, a training plan that can be done at night can be suggested. Furthermore, if the user works a desk job, a training plan including simple exercises that can be done at the office can be provided. This makes it easier for the user to continue training by customizing the training plan based on the user's occupation and work schedule.

[0113] When generating a training plan for a user, the generation unit can adjust the plan based on the user's health condition and medical history. For example, if the user has a previously injured part of the body or has a chronic illness, the generation unit can provide a training plan that does not put strain on that part of the body. Also, if the user has a specific medical condition, the generation unit can provide a training plan that is suited to that condition. Furthermore, if the user is undergoing rehabilitation, the generation unit can provide a training plan that is suited to rehabilitation. In this way, by adjusting the training plan based on the user's health condition and medical history, it is possible to support the user in training safely.

[0114] When tracking the user's progress, the tracking unit can estimate the user's emotions and adjust the progress feedback based on the estimated emotions. For example, if the user is feeling stressed, the progress feedback can be simplified and the positive aspects can be emphasized. If the user is relaxed, detailed feedback can be provided with specific advice for the next step. Furthermore, if the user is excited, feedback can be provided using visually stimulating graphics and animations. In this way, adjusting the progress feedback according to the user's emotions can make it easier for the user to maintain motivation.

[0115] When providing advice to maintain motivation based on the user's progress, the feedback unit can estimate the user's emotions and adjust the timing of the advice based on the estimated emotions. For example, if the user is feeling down, an encouraging message can be provided immediately. If the user is feeling confident, a specific goal setting for the next challenge can be suggested. Furthermore, if the user is feeling impatient, advice on how to relax or ways to relieve stress can be provided. In this way, adjusting the timing of advice according to the user's emotions makes it easier for the user to continue training.

[0116] The collection unit can collect data on the user's dietary preferences and allergy information in addition to the user's lifestyle and training habits. For example, the collection unit can collect data such as the user's preferred ingredients and allergies. This allows the generation unit to provide a more appropriate meal plan based on the user's dietary preferences and allergy information. For example, if the user prefers a particular ingredient, a meal plan including that ingredient can be suggested. Also, if the user is allergic to a particular ingredient, a meal plan that avoids that ingredient can be suggested. Furthermore, if the user needs to consume a particular nutrient, a meal plan including that nutrient can be suggested. This makes it easier for the user to maintain a healthy diet by customizing the meal plan based on the user's dietary preferences and allergy information.

[0117] The generation unit can reflect user feedback in real time when generating the user's training plan. For example, if the user provides feedback about dissatisfaction or areas for improvement felt during training, the generation unit can immediately reflect that feedback and adjust the training plan. Also, if the user prefers a particular training style, the generation unit can incorporate that training style into the plan. Furthermore, if the user provides feedback regarding the intensity and frequency of training, the generation unit can adjust the training plan based on that feedback. In this way, by reflecting user feedback in real time, it is possible to provide the user with an optimal training plan.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects data on the user's lifestyle, training habits, muscle growth rate, and dietary trends. For example, the collection unit can collect data on the user's lifestyle, such as the user's daily activity level, sleep patterns, and type of work. The collection unit can also collect data on the user's training habits, such as how many times a week the user trains and what type of training the user does. Furthermore, the collection unit can collect data on the user's muscle growth rate, such as the user's monthly muscle mass gain rate and changes in body fat percentage. The collection unit can also collect data on the user's dietary trends, such as the user's calorie intake, nutritional balance, and meal frequency. Step 2: The generation unit analyzes the data collected by the collection unit and generates a training plan suitable for the user. For example, the generation unit generates a training plan based on the user's goals and current fitness level. The generation unit can customize the training plan based on the user's short-term goals, long-term goals, specific numerical goals, etc. The generation unit can also generate a training plan based on the user's fitness level, such as the results of a physical fitness test or past training history. Step 3: The tracking unit tracks the user's progress based on the training plan generated by the generation unit. For example, the tracking unit can track the user's progress in real time, such as training achievement, weight change, muscle mass increase, etc. The tracking unit can track the progress based on real-time criteria, such as data update frequency and latency. Step 4: The feedback unit provides achievement feedback based on the progress tracked by the tracking unit. For example, the feedback unit provides advice to maintain motivation based on the user's progress. The feedback unit can provide advice to maintain motivation, such as encouraging messages or rewards for achieving goals. The feedback unit can also provide reminders to encourage the user to continue training and a reward system for achieved goals. The feedback unit can provide reminders based on reminder settings, such as notification timing and notification method (email, app notification, etc.). The feedback unit can also provide rewards based on a reward system, such as specific reward content for achieved goals and a reward award method.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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, in order to avoid confusion and to 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 data on the user's lifestyle, training habits, muscle growth rate, and dietary habits; a generation unit that analyzes the data collected by the collection unit and generates a training plan suitable for the user; a tracking unit that tracks the user's progress based on the training plan generated by the generation unit; a feedback unit that provides achievement feedback based on the progress tracked by the tracking unit. A system characterized by:

2. The collecting unit Collect data from a body composition scale or smartwatch 2. The system of claim 1.

3. The generation unit Generates training plans based on the user's goals and current fitness level 2. The system of claim 1.

4. The tracking unit Track user progress in real time 2. The system of claim 1.

5. The feedback unit Providing motivational advice based on the user's progress 2. The system of claim 1.

6. The feedback unit Providing reminders to keep users training and a reward system for achieving goals 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the user's past data collection history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit Filtering data collection based on the user's current health and activity level 2. The system of claim 1.

10. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

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