System

Abstract

To create an individual training plan on the basis of a fitness target of a user, provide an exercise feedback in real-time, and track the progress.SOLUTION: A system according to an embodiment includes a target reception unit, a creation unit, a data collection unit, a feedback unit, a tracking unit, and an adjustment unit. The target reception unit receives a fitness target of a user. The creation unit creates a training plan on the basis of the fitness target received by the target reception unit. The data collection unit collects exercise data of the user. The feedback unit feeds back a form and strength of the exercise of the user in real-time on the basis of the data collected by the data collection unit. The tracking unit tracks the progress of the training on the basis of the data collected by the data collection unit. The adjustment unit adjusts the training plan on the basis of the tracked progress 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 a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Existing technologies leave room for improvement, as they do not adequately provide personalized training plans based on a user's fitness goals, real-time exercise feedback, or progress tracking.

[0005] The system of the embodiment aims to create a personalized training plan based on a user's fitness goals, provide real-time exercise feedback, and track progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal receiving unit, a creation unit, a data collecting unit, a feedback unit, a tracking unit, and an adjustment unit. The goal receiving unit receives a fitness goal of a user. The creation unit creates a training plan based on the fitness goal received by the goal receiving unit. The data collecting unit collects exercise data of the user. The feedback unit provides real-time feedback on the form and intensity of the user's exercise based on the data collected by the data collecting unit. The tracking unit tracks training progress based on the data collected by the data collecting unit. The adjustment unit adjusts the training plan based on the progress tracked by the tracking unit. Effect of the Invention

[0007] In accordance with an embodiment, the system can create a personalized training plan based on a user's fitness goals, provide real-time exercise feedback, and track progress. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A personal trainer AI assistant system for fitness and training according to an embodiment of the present invention is a system that creates an individual training plan based on a user's fitness goals, provides real-time feedback on the form and intensity of the exercise being performed, tracks the progress of the training, and supports adjustment of the plan as appropriate. The system inputs the user's fitness goals, and sets goals such as losing weight, gaining muscle, and improving metabolism. This information is input to a goal receiving unit. Next, a creation unit creates an individual training plan based on the fitness goals accepted by the goal receiving unit. For example, a plan is created that combines strength training three times a week and aerobic exercise twice a week. When a user starts training, a data collection unit collects the user's exercise data from a smartphone or wearable device. For example, data such as heart rate, number of steps, and calorie consumption are collected. Based on the collected data, a feedback unit provides real-time feedback on the form and intensity of the exercise. For example, AI analyzes whether the user is exercising with the correct form and training at the appropriate intensity, and provides feedback. In addition, a tracking unit tracks the progress of the training and visualizes it on a dashboard. For example, information such as how many calories the user has burned and how far they have walked is displayed. Based on the tracked progress, the adjustment unit dynamically adjusts the training plan. For example, the intensity of the training is changed or rest days are added depending on the user's condition. In addition, the advice unit provides advice on nutrition and rest. For example, advice is given on how to eat properly and how to rest. In addition, the water intake is monitored and appropriate hydration is advised. In this way, an individual training plan tailored to the user's fitness goals is provided, feedback is given in real time, progress is tracked, and the plan is adjusted appropriately to support effective training. It is possible to support training.

[0029] A personal trainer AI assistant system for fitness and training according to an embodiment includes a goal receiving unit, a creation unit, a data collecting unit, a feedback unit, a tracking unit, and an adjustment unit. The goal receiving unit receives a fitness goal of a user. The fitness goal of the user includes, but is not limited to, weight loss, muscle strength, and endurance. The goal receiving unit receives, for example, a fitness goal input by the user and stores the fitness goal in a database. The creation unit creates a training plan based on the fitness goal received by the goal receiving unit. The training plan includes, for example, the type, frequency, and intensity of exercise, but is not limited to, for example. The creation unit creates a plan that combines, for example, three strength training sessions and two aerobic exercise sessions per week based on the fitness goal of the user. The data collecting unit collects exercise data of the user. The exercise data includes, for example, heart rate, number of steps, calorie consumption, and the like, but is not limited to, for example. The data collecting unit collects exercise data from, for example, a smartphone or a wearable device. The feedback unit provides real-time feedback on the form and intensity of the user's exercise based on the data collected by the data collection unit. The feedback is provided by, for example, voice feedback, visual feedback, and other methods, but is not limited to such examples. The feedback unit analyzes the user's exercise form using, for example, AI, and provides feedback on whether the user is exercising with the correct form. The tracking unit tracks the progress of the training based on the data collected by the data collection unit. The tracking is provided by, for example, goal achievement rate, training completion rate, and other methods, but is not limited to such examples. The tracking unit provides, for example, a dashboard to display information such as how many calories the user has consumed and how far the user has walked. The adjustment unit adjusts the training plan based on the progress tracked by the tracking unit. The adjustment is provided by, for example, changes to the training plan, frequency, and other methods, but is not limited to such examples.The adjustment unit, for example, changes the training intensity according to the user's condition, adds rest days, etc. As a result, the personal trainer AI assistant system for fitness and training according to the embodiment can support effective training by creating an individual training plan based on the user's fitness goals, providing feedback in real time, tracking progress, and adjusting the plan as appropriate.

[0030] The goal receiving unit receives the fitness goal of the user. The fitness goal of the user includes, but is not limited to, weight loss, muscle strengthening, and endurance improvement. For example, the goal receiving unit receives the fitness goal input by the user and stores it in a database. Specifically, the goal receiving unit provides an interface that receives the goal input by the user through a smartphone or a personal computer. This interface is designed intuitively so that the user can easily set the goal, and for example, the goal can be selected and adjusted using a drop-down menu or a slider. The goal receiving unit also has a function of proposing realistic and achievable goals taking into account the user's past data and current fitness level. For example, the goal receiving unit automatically generates goals suitable for the user based on past exercise history and health data, and allows the user to select from among them. Furthermore, the goal receiving unit stores the goals set by the user in a database and makes them accessible to other departments. This allows the entire system to consistently provide services based on the user's goals. The goal receiving unit also responds to cases where the user wants to change the goal, allowing the goal to be flexibly re-set. For example, if the user wants to change the goal midway, the goal receiving unit accepts the new goal and updates the database. This allows users to always train in a way that is aligned with their fitness goals.

[0031] The creation unit creates a training plan based on the fitness goal accepted by the goal acceptance unit. The training plan includes, for example, the type, frequency, and intensity of exercise, but is not limited to such examples. For example, the creation unit creates a plan that combines strength training three times a week and aerobic exercise twice a week based on the user's fitness goal. Specifically, the creation unit uses an AI algorithm to analyze the user's goal and current fitness level to generate an optimal training plan. This AI algorithm learns from past data and success stories of other users to provide an optimal plan for each individual user. For example, a plan centered on weightlifting and resistance training is proposed for a user aiming to increase muscle strength, and a plan centered on running and cycling is proposed for a user aiming to improve endurance. The creation unit also has a function of adjusting the frequency and time of training to suit the user's schedule and lifestyle. For example, the creation unit proposes short and effective high-intensity interval training (HIIT) to a busy user, and proposes long-term aerobic exercise to a user who has time. Furthermore, the creation unit continuously improves the training plan based on user feedback. For example, if the user feels that a particular exercise is difficult, the intensity of the exercise can be adjusted so that the user can continue the exercise without straining themselves. This allows the creation unit to support the user in achieving their goals and provide an effective training plan.

[0032] The data collection unit collects the user's exercise data. The exercise data includes, but is not limited to, for example, heart rate, number of steps, and calorie consumption. The data collection unit collects the exercise data from, for example, a smartphone or a wearable device. Specifically, the data collection unit acquires data in real time from a smart watch or fitness tracker worn by the user. These devices are equipped with a heart rate sensor, an acceleration sensor, a GPS, and the like, and can monitor the user's exercise status in detail. For example, the heart rate sensor measures the heart rate during exercise, and the acceleration sensor measures the number of steps and the intensity of exercise. The GPS tracks the user's travel distance and speed, and records the running or cycling route. In addition, the data collection unit transmits the data collected from these devices to a cloud server and centrally manages it. This allows other departments to access the data they need and use it for analysis and feedback. In addition, the data collection unit encrypts and anonymizes the data to protect the user's privacy. This minimizes the risk of the user's personal information being leaked to a third party. The data collection unit also provides the ability for users to manually input exercise data, accommodating users who do not have a device or who want to add specific exercise data, allowing the data collection unit to comprehensively collect users' exercise data and improve the accuracy and reliability of the entire system.

[0033] The feedback unit feeds back the user's exercise form and intensity in real time based on the data collected by the data collection unit. The feedback is performed by, for example, voice feedback, visual feedback, and other methods, but is not limited to such examples. For example, the feedback unit uses AI to analyze the user's exercise form and provides feedback on whether the user is exercising with the correct form. Specifically, the feedback unit captures the user's exercise form in real time using a wearable device or a smartphone camera worn by the user, and analyzes it using an AI algorithm. This AI algorithm learns past data and expert knowledge, and evaluates the user's movement by comparing it with the correct form. For example, if the knee position is incorrect when performing a squat, the voice feedback instructs the user to "move the knee a little further forward." In addition, as visual feedback, the correct form and the user's form are displayed side by side on the screen of a smartphone or tablet, making it easier to understand visually. Furthermore, the feedback unit also provides advice on the intensity of the exercise in real time. For example, if the heart rate exceeds the target range, the voice notifies the user to "slow down a little," and conversely, if the heart rate is too low, the voice instructs the user to "increase the intensity a little." This allows the user to train safely and effectively. The feedback section stores the user's feedback history and makes continuous improvements based on past advice. This allows the feedback section to maximize the user's training effect and support exercising with the correct form and appropriate intensity.

[0034] The tracking unit tracks the progress of the training based on the data collected by the data collection unit. The tracking is performed by, for example, a method such as a goal achievement rate or a degree of completion of the training, but is not limited to such examples. For example, the tracking unit provides a dashboard and displays information such as how many calories the user has consumed and how far they have walked. Specifically, the tracking unit analyzes the user's exercise data and visualizes the progress toward the goal. The dashboard visually displays the progress using graphs and charts so that the user can check the progress at a glance. For example, data such as calorie consumption, number of steps, and exercise time are displayed by day, week, and month, so that the user can easily grasp their progress. In addition, the tracking unit calculates the achievement rate for the goal set by the user and indicates the remaining steps to achieving the goal. For example, for a user who is aiming to lose weight, the difference between the current weight and the goal weight is displayed, and the progress toward the achievement is shown as a percentage. Furthermore, the tracking unit also has a function of praising the achieved goals and records in order to maintain the user's motivation. For example, when a certain amount of calories is consumed or a certain distance is walked, a badge or medal can be acquired, giving the user a sense of accomplishment. The Tracking Department will continually improve the design and functionality of the Dashboard based on user feedback to provide users with more usable and effective tools, allowing them to accurately track their progress and stay motivated to achieve their goals.

[0035] The adjustment unit adjusts the training plan based on the progress tracked by the tracking unit. The adjustment is performed, for example, by changing the training plan, frequency, etc., but is not limited to such examples. The adjustment unit, for example, changes the training intensity or adds rest days according to the user's condition. Specifically, the adjustment unit analyzes data provided by the tracking unit and evaluates the user's progress and physical condition. For example, if the user is not making progress toward the goal set by the user, the adjustment unit makes a suggestion to increase the frequency and intensity of training. On the other hand, if the user is excessively fatigued or at risk of injury, the adjustment unit makes a suggestion to reduce the training intensity or increase the number of rest days. The adjustment unit also has a function to flexibly change the training plan based on the user's feedback. For example, if the user is not good at a particular exercise, the adjustment unit makes a suggestion to replace that exercise with another exercise. Furthermore, the adjustment unit adjusts the training plan according to changes in the season, weather, and the user's lifestyle. For example, the adjustment unit increases the number of exercises that can be performed indoors in winter and recommends outdoor activities in summer. In addition, if the user needs to change the training time due to work or family circumstances, the adjustment unit provides a plan that matches the schedule. This allows the adjustment unit to provide an optimal training plan tailored to the individual needs and circumstances of the user, and to support effective training. The adjustment unit continuously collects feedback from the user and makes improvements to improve the accuracy and effectiveness of the training plan. This allows the adjustment unit to provide optimal support for the user to achieve their goals, and to maximize the effectiveness of the training.

[0036] The advice unit can provide advice on nutrition or rest. The advice unit can, for example, analyze the user's dietary content and suggest an appropriate nutritional intake method. For example, the advice unit can provide advice on protein intake and vitamin supplementation. The advice unit can also analyze the user's sleep pattern and suggest an appropriate rest method. For example, the advice unit can provide advice on the user's sleep time and rest day settings. This can further improve the user's training effect by providing advice on nutrition and rest. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input the user's dietary content into AI and have the AI ​​execute a suggestion of an appropriate nutritional intake method.

[0037] The data collection unit can collect the user's exercise data from at least one of the smartphone and the wearable device. The data collection unit can, for example, measure the user's moving distance using the GPS function of the smartphone. The data collection unit can also count the user's steps using the acceleration sensor of the smartphone. Furthermore, the data collection unit can measure the user's heart rate using the heart rate sensor of the wearable device. For example, the data collection unit collects exercise data from a smart watch or a fitness tracker. As a result, the user's exercise status can be accurately grasped by collecting exercise data from the smartphone or the wearable device. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the exercise data acquired from the smartphone or the wearable device to AI and have the AI ​​perform analysis of the data.

[0038] The feedback unit can provide feedback on the form and intensity of the exercise in real time using AI. For example, the feedback unit can use AI to analyze the form of the user's exercise and provide feedback on whether the user is exercising with the correct form. For example, the feedback unit can capture the user's movements with a camera, and the AI ​​can analyze the video to evaluate the accuracy of the form. The feedback unit can also use AI to analyze the intensity of the user's exercise and provide feedback on whether the user is training at an appropriate intensity. For example, the feedback unit can monitor the user's heart rate and calorie consumption in real time, and the AI ​​can analyze the data to evaluate the appropriateness of the intensity. In this way, feedback on the form and intensity of the exercise can be provided in real time by using AI. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's exercise data into AI and have the AI ​​perform analysis of the form and intensity.

[0039] The data collection unit can collect at least one of the heart rate, the number of steps, and the calorie consumption from the wearable device with the sensor. The data collection unit, for example, measures the user's heart rate using a heart rate sensor. For example, the data collection unit monitors the user's heart rate in real time using the heart rate sensor of the wearable device. The data collection unit can also count the user's steps using an acceleration sensor. For example, the data collection unit accurately counts the user's steps using the acceleration sensor of the wearable device. Furthermore, the data collection unit can analyze the user's exercise data to calculate the calorie consumption. For example, the data collection unit calculates the user's calorie consumption based on the data of the wearable device. In this way, by collecting detailed exercise data from the wearable device with the sensor, the user's exercise status can be grasped more accurately. Part or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the exercise data acquired from the wearable device to AI and have the AI ​​perform the analysis of the data.

[0040] The tracking unit can provide a dashboard that visualizes the training progress. The tracking unit provides, for example, a dashboard that displays the user's training progress in a graph or a progress bar. For example, the tracking unit visually displays information such as how many calories the user has consumed and how far the user has walked. The tracking unit can also display the user's goal achievement rate and the completion rate of the training. For example, the tracking unit updates the user's progress toward the fitness goal in real time and displays it on the dashboard. This allows the user to intuitively grasp his or her own progress by visualizing the training progress. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's exercise data into AI and have the AI ​​perform an analysis of the progress.

[0041] The adjustment unit can dynamically change the training plan according to the user's condition. The adjustment unit, for example, monitors the user's condition in real time and dynamically changes the training plan. For example, the adjustment unit adjusts the intensity and frequency of the training based on the user's heart rate and fatigue level. The adjustment unit can also estimate the user's muscle tension and adjust the training plan based on the estimated result. For example, the adjustment unit measures the user's muscle tension with a sensor and changes the training plan based on the data. This makes it possible to provide more effective training by dynamically changing the training plan according to the user's condition. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's condition data into AI and cause AI to adjust the training plan.

[0042] The adjustment unit can estimate the muscle tension of the user and adjust the training plan based on the estimated result. For example, the adjustment unit measures the muscle tension of the user with a sensor and changes the training plan based on the data. For example, the adjustment unit monitors the muscle tension of the user in real time and adjusts the intensity and frequency of the training. The adjustment unit can also have the user self-evaluate the muscle tension of the user and adjust the training plan based on the result. For example, the adjustment unit has the user self-evaluate the muscle tension and change the training plan based on the data. In this way, it is possible to provide a reasonable training by estimating the muscle tension of the user and adjusting the training plan based on it. Part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the muscle tension data of the user to AI and cause AI to execute adjustment of the training plan.

[0043] The advice unit can monitor the amount of water intake of the user and advise the user on appropriate hydration. The advice unit, for example, records the amount of water intake of the user and suggests appropriate hydration. For example, the advice unit records the amount of water consumed by the user and provides advice based on the recommended amount of water intake. The advice unit can also suggest the timing and amount of hydration based on the user's exercise data. For example, the advice unit suggests the timing and amount of hydration in consideration of the exercise intensity of the user and environmental conditions. This makes it possible to support water management during training by monitoring the user's water intake and advising appropriate hydration. The monitoring of water intake is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the water intake data of the user to an AI and cause the AI ​​to execute hydration advice.

[0044] The goal receiving unit can analyze the user's past fitness goal history and propose an optimal goal setting method. The goal receiving unit, for example, acquires the user's past fitness goal history from a database and proposes an optimal goal setting method based on the data. For example, the goal receiving unit automatically displays fitness goals set by the user in the past as candidates. The goal receiving unit can also preferentially propose goal setting methods (such as voice and text) used by the user in the past. Furthermore, the goal receiving unit can predict and propose goals to be set in a specific time period from the user's past goal history. This makes it possible to propose a more appropriate goal setting method by analyzing the user's past fitness goal history. A part or all of the above-mentioned processing in the goal receiving unit may be performed, for example, using AI or may be performed without using AI. For example, the goal receiving unit can input the user's past fitness goal history data into AI and cause AI to execute the proposal of an optimal goal setting method.

[0045] The goal receiving unit can filter the goal based on the user's current health condition and lifestyle habits when receiving the goal. The goal receiving unit proposes an appropriate fitness goal based on the user's current health condition (heart rate, blood pressure, etc.). For example, the goal receiving unit monitors the user's health data in real time and sets a realistic goal based on the data. The goal receiving unit can also set a realistic goal taking into account the user's lifestyle habits (eating, sleep patterns, etc.). For example, the goal receiving unit filters and proposes a reasonable goal based on the user's lifestyle habits data. This makes it possible to set a realistic and achievable goal by filtering the goal based on the user's current health condition and lifestyle habits. A part or all of the above-mentioned processing in the goal receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the goal receiving unit can input the user's health condition and lifestyle habits data to AI and cause the AI ​​to filter the goal.

[0046] The goal receiving unit can preferentially present highly relevant goals in consideration of the geographical location information of the user when receiving the goal. The goal receiving unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and preferentially presents highly relevant goals based on the information. For example, the goal receiving unit proposes appropriate fitness goals in consideration of the climate and topography of the area in which the user lives. The goal receiving unit can also set realistic goals based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the goal receiving unit proposes goals related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to propose fitness goals suitable for the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the goal receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal receiving unit can input the geographical location information data of the user to AI and cause the AI ​​to execute the presentation of highly relevant goals.

[0047] The goal receiving unit can analyze the user's social media activities at the time of goal reception, and suggest related goals based on the analysis results. For example, the goal receiving unit can suggest related goals based on fitness goals and achievements shared by the user on social media. The goal receiving unit can also suggest goals that will increase motivation by referring to goals achieved by the user's social media friends. Furthermore, the goal receiving unit can suggest related goals by referring to goals of fitness influencers the user follows on social media. In this way, by analyzing the user's social media activities, it is possible to suggest goals that are highly relevant to the user. Some or all of the above-mentioned processing in the goal receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal receiving unit can input the user's social media activity data into AI and have the AI ​​execute the suggestion of related goals.

[0048] When creating a training plan, the creation unit can create an optimal plan by referring to the user's past training history. The creation unit, for example, acquires the user's past training history from a database and creates an optimal training plan based on the data. For example, the creation unit creates an effective plan based on the results of the user's past training. The creation unit can also create a plan to avoid the risk of injury from the user's past training history. Furthermore, the creation unit analyzes the user's past training history and creates a plan according to the user's progress. As a result, a more effective training plan can be created by referring to the user's past training history. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the user's past training history data into AI and cause AI to create an optimal training plan.

[0049] The creation unit can customize the plan based on the user's current physical fitness level when creating the training plan. The creation unit creates an appropriate training plan based on the user's current physical fitness level (heart rate, muscle strength, etc.), for example. For example, the creation unit adjusts the intensity and frequency of training according to the user's physical fitness level. The creation unit can also monitor the user's physical fitness level in real time and dynamically customize the plan. For example, the creation unit adjusts the training plan based on the user's heart rate and muscle strength data. This makes it possible to provide an optimal training plan for the user by customizing the plan based on the user's current physical fitness level. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the user's physical fitness level data to AI and cause AI to customize the training plan.

[0050] The creation unit can propose an optimal training location in consideration of the geographical location information of the user when creating a training plan. The creation unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and proposes an optimal training location based on the information. For example, the creation unit proposes an appropriate training location in consideration of the climate and topography of the area where the user lives. The creation unit can also propose a realistic training location based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the creation unit proposes training locations related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to propose an optimal training location for the user by considering the geographical location information of the user. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the geographical location information data of the user to AI and cause the AI ​​to execute the proposal of an optimal training location.

[0051] The creation unit can analyze the user's social media activities when creating a training plan, and can propose a related training plan based on the analysis results. For example, the creation unit can propose a related training plan based on the fitness goals and achievements shared by the user on social media. The creation unit can also propose a plan to increase motivation by referring to a training plan achieved by the user's social media friends. Furthermore, the creation unit can propose a related plan by referring to a training plan of a fitness influencer the user follows on social media. In this way, a training plan that is highly relevant to the user can be proposed by analyzing the user's social media activities. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the user's social media activity data into AI and have the AI ​​execute the proposal of a related training plan.

[0052] The data collection unit can select the optimal data collection method by referring to the user's past exercise data when collecting data. The data collection unit, for example, acquires the user's past exercise data from a database and selects the optimal data collection method based on the data. For example, the data collection unit selects an effective data collection method based on data collected by the user in the past. The data collection unit can also select a data collection method for avoiding the risk of injury from the user's past exercise data. Furthermore, the data collection unit analyzes the user's past exercise data and selects a data collection method according to the progress. In this way, a more effective data collection method can be selected by referring to the user's past exercise data. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the user's past exercise data into AI and cause AI to select the optimal data collection method.

[0053] The data collection unit can customize the data collection items based on the user's current health condition at the time of data collection. The data collection unit selects appropriate data collection items based on the user's current health condition (heart rate, blood pressure, etc.), for example. For example, the data collection unit monitors the user's health condition in real time and customizes the data collection items based on the data. The data collection unit can also adjust the frequency and method of data collection according to the user's health condition. Furthermore, the data collection unit monitors the user's health condition in real time and dynamically customizes the data collection items. As a result, the data collection items can be customized based on the user's current health condition, thereby allowing optimal data collection for the user. A part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the user's health condition data into AI and cause AI to customize the data collection items.

[0054] The data collection unit can preferentially collect highly relevant data in consideration of the user's geographical location information when collecting data. The data collection unit, for example, acquires the user's geographical location information using GPS data or a location information service, and preferentially collects highly relevant data based on the information. For example, the data collection unit collects appropriate data in consideration of the climate and topography of the area where the user lives. The data collection unit can also collect realistic data based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the data collection unit collects data related to local fitness events and activities based on the user's geographical location information. In this way, data highly relevant to the user can be collected by considering the user's geographical location information. A part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the user's geographical location information data to AI and cause AI to collect highly relevant data.

[0055] The data collection unit can analyze the user's social media activities during data collection and collect relevant data based on the analysis results. For example, the data collection unit collects relevant data based on fitness goals and achievements shared by the user on social media. The data collection unit can also collect data to increase motivation by referring to data achieved by the user's social media friends. Furthermore, the data collection unit collects relevant data by referring to data of fitness influencers the user follows on social media. In this way, data highly relevant to the user can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the user's social media activity data into AI and cause AI to collect related data.

[0056] The feedback unit can provide optimal feedback by referring to the user's past exercise data when providing feedback. The feedback unit, for example, obtains the user's past exercise data from a database and provides optimal feedback based on the data. For example, the feedback unit provides effective feedback based on the results of the user's past training. The feedback unit can also provide feedback to avoid the risk of injury from the user's past exercise data. Furthermore, the feedback unit analyzes the user's past exercise data and provides feedback according to the progress. As a result, more effective feedback can be provided by referring to the user's past exercise data. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's past exercise data into AI and cause AI to provide optimal feedback.

[0057] The feedback unit can adjust the strength of the feedback based on the user's current physical fitness level at the time of feedback. The feedback unit provides appropriate feedback based on the user's current physical fitness level (heart rate, muscle strength, etc.), for example. For example, the feedback unit adjusts the strength and frequency of the feedback according to the user's physical fitness level. The feedback unit can also monitor the user's physical fitness level in real time and dynamically adjust the strength of the feedback. This makes it possible to provide optimal feedback for the user by adjusting the strength of the feedback based on the user's current physical fitness level. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's physical fitness level data to AI and cause AI to adjust the strength of the feedback.

[0058] The feedback unit can provide optimal feedback by considering the geographical location information of the user at the time of feedback. For example, the feedback unit obtains the geographical location information of the user using GPS data or a location information service, and provides optimal feedback based on the information. For example, the feedback unit provides appropriate feedback by considering the climate and topography of the area where the user lives. The feedback unit can also provide realistic feedback based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the feedback unit provides feedback related to local fitness events and activities based on the geographical location information of the user. In this way, optimal feedback for the user can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location information data of the user to AI and cause the AI ​​to provide optimal feedback.

[0059] The feedback unit can analyze the user's social media activity at the time of feedback and provide relevant feedback based on the analysis result. For example, the feedback unit can provide relevant feedback based on fitness goals and achievements shared by the user on social media. The feedback unit can also provide motivational feedback by referring to feedback achieved by the user's social media friends. Furthermore, the feedback unit can provide relevant feedback by referring to feedback from fitness influencers the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to provide feedback that is highly relevant to the user. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or can be performed without using AI. For example, the feedback unit can input the user's social media activity data into AI and cause the AI ​​to provide relevant feedback.

[0060] The tracking unit can select the optimal tracking method by referring to the user's past exercise data during tracking. The tracking unit, for example, acquires the user's past exercise data from a database and selects the optimal tracking method based on the data. For example, the tracking unit selects an effective tracking method based on the results of the user's past training. The tracking unit can also select a tracking method to avoid the risk of injury from the user's past exercise data. Furthermore, the tracking unit analyzes the user's past exercise data and selects a tracking method according to the progress. In this way, a more effective tracking method can be selected by referring to the user's past exercise data. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's past exercise data into AI and cause AI to select the optimal tracking method.

[0061] The tracking unit can customize the tracking items based on the user's current health condition during tracking. The tracking unit selects appropriate tracking items based on the user's current health condition (heart rate, blood pressure, etc.). For example, the tracking unit monitors the user's health condition in real time and customizes the tracking items based on the data. The tracking unit can also adjust the frequency and method of tracking according to the user's health condition. Furthermore, the tracking unit monitors the user's health condition in real time and dynamically customizes the tracking items. As a result, the tracking items can be customized based on the user's current health condition, thereby performing optimal tracking for the user. A part 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 the user's health condition data into AI and cause AI to customize the tracking items.

[0062] The tracking unit can provide an optimal tracking method by taking into account the geographical location information of the user during tracking. The tracking unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and provides an optimal tracking method based on the information. For example, the tracking unit provides an appropriate tracking method by taking into account the climate and topography of the area where the user lives. The tracking unit can also provide a realistic tracking method based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the tracking unit provides a tracking method related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to provide an optimal tracking method for the user by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the geographical location information data of the user to AI and cause the AI ​​to provide the optimal tracking method.

[0063] The tracking unit can analyze the user's social media activities during tracking and provide related tracking information based on the analysis results. For example, the tracking unit can provide related tracking information based on fitness goals and achievements shared by the user on social media. The tracking unit can also provide motivational tracking information by referring to tracking information achieved by the user's social media friends. Furthermore, the tracking unit can provide related tracking information by referring to tracking information of fitness influencers the user follows on social media. In this way, by analyzing the user's social media activities, it is possible to provide tracking information that is highly relevant to the user. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's social media activity data into AI and cause AI to provide related tracking information.

[0064] During adjustment, the adjustment unit can select the optimal adjustment method by referring to the user's past training data. The adjustment unit, for example, acquires the user's past training data from a database and selects the optimal adjustment method based on the data. For example, the adjustment unit selects an effective adjustment method based on the results of the user's past training. The adjustment unit can also select an adjustment method to avoid the risk of injury from the user's past training data. Furthermore, the adjustment unit analyzes the user's past training data and selects an adjustment method according to the progress. As a result, a more effective adjustment method can be selected by referring to the user's past training data. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past training data to AI and cause the AI ​​to select the optimal adjustment method.

[0065] During the adjustment, the adjustment unit can customize the training plan based on the user's current physical fitness level. The adjustment unit adjusts an appropriate training plan based on, for example, the user's current physical fitness level (heart rate, muscle strength, etc.). For example, the adjustment unit adjusts the intensity and frequency of training according to the user's physical fitness level. The adjustment unit can also monitor the user's physical fitness level in real time and dynamically customize the training plan. This makes it possible to provide an optimal training plan for the user by customizing the training plan based on the user's current physical fitness level. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's physical fitness level data to AI and cause AI to customize the training plan.

[0066] The adjustment unit can provide an optimal training plan by considering the geographical location information of the user at the time of adjustment. The adjustment unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and provides an optimal training plan based on the information. For example, the adjustment unit provides an appropriate training plan by considering the climate and topography of the area where the user lives. The adjustment unit can also provide a realistic training plan based on places that the user frequently visits (gyms, parks, etc.). Furthermore, the adjustment unit provides a training plan related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to provide an optimal training plan for the user by considering the geographical location information of the user. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the geographical location information data of the user to AI and cause the AI ​​to provide an optimal training plan.

[0067] During the adjustment, the adjustment unit can analyze the user's social media activities and provide a related training plan based on the analysis results. For example, the adjustment unit can provide a related training plan based on fitness goals and achievements shared by the user on social media. The adjustment unit can also provide a plan to increase motivation by referring to training plans achieved by the user's social media friends. Furthermore, the adjustment unit can provide a related plan by referring to training plans of fitness influencers the user follows on social media. In this way, a training plan that is highly relevant to the user can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's social media activity data into AI and cause AI to provide a related training plan.

[0068] The advice unit can provide optimal advice by referring to the user's past health data when giving advice. The advice unit, for example, acquires the user's past health data from a database and provides optimal advice based on the data. For example, the advice unit provides effective advice based on the results of health management performed by the user in the past. The advice unit can also provide advice to avoid the risk of injury from the user's past health data. Furthermore, the advice unit analyzes the user's past health data and provides advice according to the progress. In this way, more effective advice can be provided by referring to the user's past health data. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or may be performed without using AI. For example, the advice unit can input the user's past health data into AI and cause AI to provide optimal advice.

[0069] The advice unit can customize the contents of the advice based on the user's current lifestyle habits when providing advice. The advice unit provides appropriate advice based on, for example, the user's current lifestyle habits (eating habits, sleep patterns, etc.). For example, the advice unit monitors the user's lifestyle habits in real time and customizes the contents of the advice based on the data. The advice unit can also adjust the frequency and contents of the advice according to the user's lifestyle habits. Furthermore, the advice unit monitors the user's lifestyle habits in real time and dynamically customizes the contents of the advice. As a result, the advice content can be customized based on the user's current lifestyle habits, thereby providing optimal advice for the user. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's lifestyle habit data to AI and cause AI to customize the contents of the advice.

[0070] The advice unit can provide optimal advice by considering the geographical location information of the user when providing advice. The advice unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and provides optimal advice based on the information. For example, the advice unit provides appropriate advice by considering the climate and topography of the area where the user lives. The advice unit can also provide realistic advice based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the advice unit provides advice related to local fitness events and activities based on the geographical location information of the user. In this way, optimal advice for the user can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the geographical location information data of the user to AI and cause the AI ​​to provide optimal advice.

[0071] The advice unit can analyze the user's social media activity at the time of giving advice, and provide relevant advice based on the analysis result. For example, the advice unit can provide relevant advice based on fitness goals and achievements shared by the user on social media. The advice unit can also provide advice to increase motivation by referring to advice achieved by the user's social media friends. Furthermore, the advice unit can provide relevant advice by referring to advice of fitness influencers the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to provide advice that is highly relevant to the user. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input the user's social media activity data into AI and cause the AI ​​to provide relevant advice.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The goal receiving unit can analyze the user's past fitness goal history and suggest an optimal goal setting method. For example, the goal receiving unit acquires the user's past fitness goal history from a database and suggests an optimal goal setting method based on the data. For example, the goal receiving unit automatically displays fitness goals set by the user in the past as candidates. The goal receiving unit can also preferentially suggest goal setting methods (such as voice and text) that the user has used in the past. Furthermore, the goal receiving unit can predict and suggest goals to be set in a specific time period from the user's past goal history. In this way, a more appropriate goal setting method can be suggested by analyzing the user's past fitness goal history.

[0074] The data collection unit can preferentially collect highly relevant data taking into account the geographical location information of the user. For example, the data collection unit acquires the geographical location information of the user using GPS data or a location information service, and preferentially collects highly relevant data based on the information. For example, the data collection unit collects appropriate data taking into account the climate and topography of the area where the user lives. The data collection unit can also collect realistic data based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the data collection unit collects data related to local fitness events and activities based on the geographical location information of the user. In this way, data highly relevant to the user can be collected by taking into account the geographical location information of the user.

[0075] The tracking unit can select the optimal tracking method by referring to the user's past exercise data. For example, the tracking unit acquires the user's past exercise data from a database and selects the optimal tracking method based on the data. For example, the tracking unit selects an effective tracking method based on the results of the user's past training. The tracking unit can also select a tracking method for avoiding the risk of injury from the user's past exercise data. Furthermore, the tracking unit analyzes the user's past exercise data and selects a tracking method according to the user's progress. In this way, a more effective tracking method can be selected by referring to the user's past exercise data.

[0076] When giving advice, the advice unit can provide optimal advice by referring to the user's past health data. For example, the advice unit obtains the user's past health data from a database and provides optimal advice based on that data. For example, the advice unit provides effective advice based on the results of health management performed by the user in the past. The advice unit can also provide advice to avoid the risk of injury based on the user's past health data. Furthermore, the advice unit analyzes the user's past health data and provides advice according to the user's progress. In this way, more effective advice can be provided by referring to the user's past health data.

[0077] When creating a training plan, the creation unit can customize the plan based on the user's current physical fitness level. For example, the creation unit creates an appropriate training plan based on the user's current physical fitness level (heart rate, muscle strength, etc.). For example, the creation unit adjusts the intensity and frequency of training according to the user's physical fitness level. The creation unit can also monitor the user's physical fitness level in real time and dynamically customize the plan. For example, the creation unit adjusts the training plan based on the user's heart rate and muscle strength data. In this way, the plan can be customized based on the user's current physical fitness level, thereby providing an optimal training plan for the user.

[0078] The process flow of the first embodiment will be briefly described below.

[0079] Step 1: The goal receiving unit receives a fitness goal of the user. The fitness goal of the user may include, for example, weight loss, muscle building, and endurance improvement. The goal receiving unit receives the fitness goal input by the user and stores it in a database. Step 2: The creation unit creates a training plan based on the fitness goal accepted by the goal acceptance unit. The training plan includes the type, frequency, and intensity of exercise. For example, the creation unit creates a plan that combines strength training three times a week and aerobic exercise twice a week based on the user's fitness goal. Step 3: The data collection unit collects the user's exercise data. The exercise data includes heart rate, number of steps, calorie consumption, etc. The data collection unit collects the exercise data from a smartphone or a wearable device. Step 4: The feedback unit provides real-time feedback on the user's exercise form and intensity based on the data collected by the data collection unit. Feedback is provided by methods such as audio feedback and visual feedback. The feedback unit uses AI to analyze the user's exercise form and provides feedback on whether the user is exercising with the correct form. Step 5: The tracking unit tracks the progress of the training based on the data collected by the data collection unit. Tracking is performed by methods such as goal achievement rate, training completion rate, etc. The tracking unit provides a dashboard to display information such as how many calories the user has burned and how far they have walked. Step 6: The adjustment unit adjusts the training plan based on the progress tracked by the tracking unit. The adjustment is performed by changing the training plan contents, frequency, etc. The adjustment unit changes the training intensity or adds rest days depending on the user's condition.

[0080] (Example 2) A personal trainer AI assistant system for fitness and training according to an embodiment of the present invention is a system that creates an individual training plan based on a user's fitness goals, provides real-time feedback on the form and intensity of the exercise being performed, tracks the progress of the training, and supports adjustment of the plan as appropriate. The system inputs the user's fitness goals, and sets goals such as losing weight, gaining muscle, and improving metabolism. This information is input to a goal receiving unit. Next, a creation unit creates an individual training plan based on the fitness goals accepted by the goal receiving unit. For example, a plan is created that combines strength training three times a week and aerobic exercise twice a week. When a user starts training, a data collection unit collects the user's exercise data from a smartphone or wearable device. For example, data such as heart rate, number of steps, and calorie consumption are collected. Based on the collected data, a feedback unit provides real-time feedback on the form and intensity of the exercise. For example, AI analyzes whether the user is exercising with the correct form and training at the appropriate intensity, and provides feedback. In addition, a tracking unit tracks the progress of the training and visualizes it on a dashboard. For example, information such as how many calories the user has burned and how far they have walked is displayed. Based on the tracked progress, the adjustment unit dynamically adjusts the training plan. For example, the intensity of the training is changed or rest days are added depending on the user's condition. In addition, the advice unit provides advice on nutrition and rest. For example, advice is given on how to eat properly and how to rest. In addition, the water intake is monitored and appropriate hydration is advised. In this way, an individual training plan tailored to the user's fitness goals is provided, feedback is given in real time, progress is tracked, and the plan is adjusted appropriately to support effective training. It is possible to support training.

[0081] A personal trainer AI assistant system for fitness and training according to an embodiment includes a goal receiving unit, a creation unit, a data collecting unit, a feedback unit, a tracking unit, and an adjustment unit. The goal receiving unit receives a fitness goal of a user. The fitness goal of the user includes, but is not limited to, weight loss, muscle strength, and endurance. The goal receiving unit receives, for example, a fitness goal input by the user and stores the fitness goal in a database. The creation unit creates a training plan based on the fitness goal received by the goal receiving unit. The training plan includes, for example, the type, frequency, and intensity of exercise, but is not limited to, for example. The creation unit creates a plan that combines, for example, three strength training sessions and two aerobic exercise sessions per week based on the fitness goal of the user. The data collecting unit collects exercise data of the user. The exercise data includes, for example, heart rate, number of steps, calorie consumption, and the like, but is not limited to, for example. The data collecting unit collects exercise data from, for example, a smartphone or a wearable device. The feedback unit provides real-time feedback on the form and intensity of the user's exercise based on the data collected by the data collection unit. The feedback is provided by, for example, voice feedback, visual feedback, and other methods, but is not limited to such examples. The feedback unit analyzes the user's exercise form using, for example, AI, and provides feedback on whether the user is exercising with the correct form. The tracking unit tracks the progress of the training based on the data collected by the data collection unit. The tracking is provided by, for example, goal achievement rate, training completion rate, and other methods, but is not limited to such examples. The tracking unit provides, for example, a dashboard to display information such as how many calories the user has consumed and how far the user has walked. The adjustment unit adjusts the training plan based on the progress tracked by the tracking unit. The adjustment is provided by, for example, changes to the training plan, frequency, and other methods, but is not limited to such examples.The adjustment unit, for example, changes the training intensity according to the user's condition, adds rest days, etc. As a result, the personal trainer AI assistant system for fitness and training according to the embodiment can support effective training by creating an individual training plan based on the user's fitness goals, providing feedback in real time, tracking progress, and adjusting the plan as appropriate.

[0082] The goal receiving unit receives the fitness goal of the user. The fitness goal of the user includes, but is not limited to, weight loss, muscle strengthening, and endurance improvement. For example, the goal receiving unit receives the fitness goal input by the user and stores it in a database. Specifically, the goal receiving unit provides an interface that receives the goal input by the user through a smartphone or a personal computer. This interface is designed intuitively so that the user can easily set the goal, and for example, the goal can be selected and adjusted using a drop-down menu or a slider. The goal receiving unit also has a function of proposing realistic and achievable goals taking into account the user's past data and current fitness level. For example, the goal receiving unit automatically generates goals suitable for the user based on past exercise history and health data, and allows the user to select from among them. Furthermore, the goal receiving unit stores the goals set by the user in a database and makes them accessible to other departments. This allows the entire system to consistently provide services based on the user's goals. The goal receiving unit also responds to cases where the user wants to change the goal, allowing the goal to be flexibly re-set. For example, if the user wants to change the goal midway, the goal receiving unit accepts the new goal and updates the database. This allows users to always train in a way that is aligned with their fitness goals.

[0083] The creation unit creates a training plan based on the fitness goal accepted by the goal acceptance unit. The training plan includes, for example, the type, frequency, and intensity of exercise, but is not limited to such examples. For example, the creation unit creates a plan that combines strength training three times a week and aerobic exercise twice a week based on the user's fitness goal. Specifically, the creation unit uses an AI algorithm to analyze the user's goal and current fitness level to generate an optimal training plan. This AI algorithm learns from past data and success stories of other users to provide an optimal plan for each individual user. For example, a plan centered on weightlifting and resistance training is proposed for a user aiming to increase muscle strength, and a plan centered on running and cycling is proposed for a user aiming to improve endurance. The creation unit also has a function of adjusting the frequency and time of training to suit the user's schedule and lifestyle. For example, the creation unit proposes short and effective high-intensity interval training (HIIT) to a busy user, and proposes long-term aerobic exercise to a user who has time. Furthermore, the creation unit continuously improves the training plan based on user feedback. For example, if the user feels that a particular exercise is difficult, the intensity of the exercise can be adjusted so that the user can continue the exercise without straining themselves. This allows the creation unit to support the user in achieving their goals and provide an effective training plan.

[0084] The data collection unit collects the user's exercise data. The exercise data includes, but is not limited to, for example, heart rate, number of steps, and calorie consumption. The data collection unit collects the exercise data from, for example, a smartphone or a wearable device. Specifically, the data collection unit acquires data in real time from a smart watch or fitness tracker worn by the user. These devices are equipped with a heart rate sensor, an acceleration sensor, a GPS, and the like, and can monitor the user's exercise status in detail. For example, the heart rate sensor measures the heart rate during exercise, and the acceleration sensor measures the number of steps and the intensity of exercise. The GPS tracks the user's travel distance and speed, and records the running or cycling route. In addition, the data collection unit transmits the data collected from these devices to a cloud server and centrally manages it. This allows other departments to access the data they need and use it for analysis and feedback. In addition, the data collection unit encrypts and anonymizes the data to protect the user's privacy. This minimizes the risk of the user's personal information being leaked to a third party. The data collection unit also provides the ability for users to manually input exercise data, accommodating users who do not have a device or who want to add specific exercise data, allowing the data collection unit to comprehensively collect users' exercise data and improve the accuracy and reliability of the entire system.

[0085] The feedback unit feeds back the user's exercise form and intensity in real time based on the data collected by the data collection unit. The feedback is performed by, for example, voice feedback, visual feedback, and other methods, but is not limited to such examples. For example, the feedback unit uses AI to analyze the user's exercise form and provides feedback on whether the user is exercising with the correct form. Specifically, the feedback unit captures the user's exercise form in real time using a wearable device or a smartphone camera worn by the user, and analyzes it using an AI algorithm. This AI algorithm learns past data and expert knowledge, and evaluates the user's movement by comparing it with the correct form. For example, if the knee position is incorrect when performing a squat, the voice feedback instructs the user to "move the knee a little further forward." In addition, as visual feedback, the correct form and the user's form are displayed side by side on the screen of a smartphone or tablet, making it easier to understand visually. Furthermore, the feedback unit also provides advice on the intensity of the exercise in real time. For example, if the heart rate exceeds the target range, the voice notifies the user to "slow down a little," and conversely, if the heart rate is too low, the voice instructs the user to "increase the intensity a little." This allows the user to train safely and effectively. The feedback section stores the user's feedback history and makes continuous improvements based on past advice. This allows the feedback section to maximize the user's training effect and support exercising with the correct form and appropriate intensity.

[0086] The tracking unit tracks the progress of the training based on the data collected by the data collection unit. The tracking is performed by, for example, a method such as a goal achievement rate or a degree of completion of the training, but is not limited to such examples. For example, the tracking unit provides a dashboard and displays information such as how many calories the user has consumed and how far they have walked. Specifically, the tracking unit analyzes the user's exercise data and visualizes the progress toward the goal. The dashboard visually displays the progress using graphs and charts so that the user can check the progress at a glance. For example, data such as calorie consumption, number of steps, and exercise time are displayed by day, week, and month, so that the user can easily grasp their progress. In addition, the tracking unit calculates the achievement rate for the goal set by the user and indicates the remaining steps to achieving the goal. For example, for a user who is aiming to lose weight, the difference between the current weight and the goal weight is displayed, and the progress toward the achievement is shown as a percentage. Furthermore, the tracking unit also has a function of praising the achieved goals and records in order to maintain the user's motivation. For example, when a certain amount of calories is consumed or a certain distance is walked, a badge or medal can be acquired, giving the user a sense of accomplishment. The Tracking Department will continually improve the design and functionality of the Dashboard based on user feedback to provide users with more usable and effective tools, allowing them to accurately track their progress and stay motivated to achieve their goals.

[0087] The adjustment unit adjusts the training plan based on the progress tracked by the tracking unit. The adjustment is performed, for example, by changing the training plan, frequency, etc., but is not limited to such examples. The adjustment unit, for example, changes the training intensity or adds rest days according to the user's condition. Specifically, the adjustment unit analyzes data provided by the tracking unit and evaluates the user's progress and physical condition. For example, if the user is not making progress toward the goal set by the user, the adjustment unit makes a suggestion to increase the frequency and intensity of training. On the other hand, if the user is excessively fatigued or at risk of injury, the adjustment unit makes a suggestion to reduce the training intensity or increase the number of rest days. The adjustment unit also has a function to flexibly change the training plan based on the user's feedback. For example, if the user is not good at a particular exercise, the adjustment unit makes a suggestion to replace that exercise with another exercise. Furthermore, the adjustment unit adjusts the training plan according to changes in the season, weather, and the user's lifestyle. For example, the adjustment unit increases the number of exercises that can be performed indoors in winter and recommends outdoor activities in summer. In addition, if the user needs to change the training time due to work or family circumstances, the adjustment unit provides a plan that matches the schedule. This allows the adjustment unit to provide an optimal training plan tailored to the individual needs and circumstances of the user, and to support effective training. The adjustment unit continuously collects feedback from the user and makes improvements to improve the accuracy and effectiveness of the training plan. This allows the adjustment unit to provide optimal support for the user to achieve their goals, and to maximize the effectiveness of the training.

[0088] The advice unit can provide advice on nutrition or rest. The advice unit can, for example, analyze the user's dietary content and suggest an appropriate nutritional intake method. For example, the advice unit can provide advice on protein intake and vitamin supplementation. The advice unit can also analyze the user's sleep pattern and suggest an appropriate rest method. For example, the advice unit can provide advice on the user's sleep time and rest day settings. This can further improve the user's training effect by providing advice on nutrition and rest. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input the user's dietary content into AI and have the AI ​​execute a suggestion of an appropriate nutritional intake method.

[0089] The data collection unit can collect the user's exercise data from at least one of the smartphone and the wearable device. The data collection unit can, for example, measure the user's moving distance using the GPS function of the smartphone. The data collection unit can also count the user's steps using the acceleration sensor of the smartphone. Furthermore, the data collection unit can measure the user's heart rate using the heart rate sensor of the wearable device. For example, the data collection unit collects exercise data from a smart watch or a fitness tracker. As a result, the user's exercise status can be accurately grasped by collecting exercise data from the smartphone or the wearable device. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the exercise data acquired from the smartphone or the wearable device to AI and have the AI ​​perform analysis of the data.

[0090] The feedback unit can provide feedback on the form and intensity of the exercise in real time using AI. For example, the feedback unit can use AI to analyze the form of the user's exercise and provide feedback on whether the user is exercising with the correct form. For example, the feedback unit can capture the user's movements with a camera, and the AI ​​can analyze the video to evaluate the accuracy of the form. The feedback unit can also use AI to analyze the intensity of the user's exercise and provide feedback on whether the user is training at an appropriate intensity. For example, the feedback unit can monitor the user's heart rate and calorie consumption in real time, and the AI ​​can analyze the data to evaluate the appropriateness of the intensity. In this way, feedback on the form and intensity of the exercise can be provided in real time by using AI. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's exercise data into AI and have the AI ​​perform analysis of the form and intensity.

[0091] The data collection unit can collect at least one of the heart rate, the number of steps, and the calorie consumption from the wearable device with the sensor. The data collection unit, for example, measures the user's heart rate using a heart rate sensor. For example, the data collection unit monitors the user's heart rate in real time using the heart rate sensor of the wearable device. The data collection unit can also count the user's steps using an acceleration sensor. For example, the data collection unit accurately counts the user's steps using the acceleration sensor of the wearable device. Furthermore, the data collection unit can analyze the user's exercise data to calculate the calorie consumption. For example, the data collection unit calculates the user's calorie consumption based on the data of the wearable device. In this way, by collecting detailed exercise data from the wearable device with the sensor, the user's exercise status can be grasped more accurately. Part or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the exercise data acquired from the wearable device to AI and have the AI ​​perform the analysis of the data.

[0092] The tracking unit can provide a dashboard that visualizes the training progress. The tracking unit provides, for example, a dashboard that displays the user's training progress in a graph or a progress bar. For example, the tracking unit visually displays information such as how many calories the user has consumed and how far the user has walked. The tracking unit can also display the user's goal achievement rate and the completion rate of the training. For example, the tracking unit updates the user's progress toward the fitness goal in real time and displays it on the dashboard. This allows the user to intuitively grasp his or her own progress by visualizing the training progress. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's exercise data into AI and have the AI ​​perform an analysis of the progress.

[0093] The adjustment unit can dynamically change the training plan according to the user's condition. The adjustment unit, for example, monitors the user's condition in real time and dynamically changes the training plan. For example, the adjustment unit adjusts the intensity and frequency of the training based on the user's heart rate and fatigue level. The adjustment unit can also estimate the user's muscle tension and adjust the training plan based on the estimated result. For example, the adjustment unit measures the user's muscle tension with a sensor and changes the training plan based on the data. This makes it possible to provide more effective training by dynamically changing the training plan according to the user's condition. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's condition data into AI and cause AI to adjust the training plan.

[0094] The adjustment unit can estimate the muscle tension of the user and adjust the training plan based on the estimated result. For example, the adjustment unit measures the muscle tension of the user with a sensor and changes the training plan based on the data. For example, the adjustment unit monitors the muscle tension of the user in real time and adjusts the intensity and frequency of the training. The adjustment unit can also have the user self-evaluate the muscle tension of the user and adjust the training plan based on the result. For example, the adjustment unit has the user self-evaluate the muscle tension and change the training plan based on the data. In this way, it is possible to provide a reasonable training by estimating the muscle tension of the user and adjusting the training plan based on it. Part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the muscle tension data of the user to AI and cause AI to execute adjustment of the training plan.

[0095] The advice unit can monitor the amount of water intake of the user and advise the user on appropriate hydration. The advice unit, for example, records the amount of water intake of the user and suggests appropriate hydration. For example, the advice unit records the amount of water consumed by the user and provides advice based on the recommended amount of water intake. The advice unit can also suggest the timing and amount of hydration based on the user's exercise data. For example, the advice unit suggests the timing and amount of hydration in consideration of the exercise intensity of the user and environmental conditions. This makes it possible to support water management during training by monitoring the user's water intake and advising appropriate hydration. The monitoring of water intake is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the water intake data of the user to an AI and cause the AI ​​to execute hydration advice.

[0096] The goal acceptance unit can estimate the user's emotion and adjust the input method of the fitness goal based on the estimated user's emotion. The goal acceptance unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and adjusts the input method of the fitness goal based on the result. For example, the goal acceptance unit provides a simple interface and minimizes input procedures when the user is stressed. In addition, the goal acceptance unit can provide detailed input options and suggest a customizable input method when the user is relaxed. Furthermore, when the user is in a hurry, the goal acceptance unit prioritizes voice input and enables the fitness goal to be input quickly. This allows the user to set a goal without stress by adjusting the input method of the fitness goal according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal acceptance unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the goal receiving unit can input a user's emotional data into the AI ​​and cause the AI ​​to adjust the method of inputting fitness goals.

[0097] The goal receiving unit can analyze the user's past fitness goal history and propose an optimal goal setting method. The goal receiving unit, for example, acquires the user's past fitness goal history from a database and proposes an optimal goal setting method based on the data. For example, the goal receiving unit automatically displays fitness goals set by the user in the past as candidates. The goal receiving unit can also preferentially propose goal setting methods (such as voice and text) used by the user in the past. Furthermore, the goal receiving unit can predict and propose goals to be set in a specific time period from the user's past goal history. This makes it possible to propose a more appropriate goal setting method by analyzing the user's past fitness goal history. A part or all of the above-mentioned processing in the goal receiving unit may be performed, for example, using AI or may be performed without using AI. For example, the goal receiving unit can input the user's past fitness goal history data into AI and cause AI to execute the proposal of an optimal goal setting method.

[0098] The goal receiving unit can filter the goal based on the user's current health condition and lifestyle habits when receiving the goal. The goal receiving unit proposes an appropriate fitness goal based on the user's current health condition (heart rate, blood pressure, etc.). For example, the goal receiving unit monitors the user's health data in real time and sets a realistic goal based on the data. The goal receiving unit can also set a realistic goal taking into account the user's lifestyle habits (eating, sleep patterns, etc.). For example, the goal receiving unit filters and proposes a reasonable goal based on the user's lifestyle habits data. This makes it possible to set a realistic and achievable goal by filtering the goal based on the user's current health condition and lifestyle habits. A part or all of the above-mentioned processing in the goal receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the goal receiving unit can input the user's health condition and lifestyle habits data to AI and cause the AI ​​to filter the goal.

[0099] The goal receiving unit can estimate the user's emotion and determine the priority of goals based on the estimated user's emotion. The goal receiving unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and determines the priority of goals based on the result. For example, when the user is highly motivated, the goal receiving unit preferentially proposes challenging goals. In addition, when the user is tired, the goal receiving unit can also preferentially propose realistic and achievable goals. Furthermore, when the user feels anxious, the goal receiving unit preferentially proposes goals that give a sense of security. In this way, the priority of goals is determined according to the user's emotion, and it is possible to propose the optimal goal for the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the goal receiving unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of goals.

[0100] The goal receiving unit can preferentially present highly relevant goals in consideration of the geographical location information of the user when receiving the goal. The goal receiving unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and preferentially presents highly relevant goals based on the information. For example, the goal receiving unit proposes appropriate fitness goals in consideration of the climate and topography of the area in which the user lives. The goal receiving unit can also set realistic goals based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the goal receiving unit proposes goals related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to propose fitness goals suitable for the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the goal receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal receiving unit can input the geographical location information data of the user to AI and cause the AI ​​to execute the presentation of highly relevant goals.

[0101] The goal receiving unit can analyze the user's social media activities at the time of goal reception, and suggest related goals based on the analysis results. For example, the goal receiving unit can suggest related goals based on fitness goals and achievements shared by the user on social media. The goal receiving unit can also suggest goals that will increase motivation by referring to goals achieved by the user's social media friends. Furthermore, the goal receiving unit can suggest related goals by referring to goals of fitness influencers the user follows on social media. In this way, by analyzing the user's social media activities, it is possible to suggest goals that are highly relevant to the user. Some or all of the above-mentioned processing in the goal receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal receiving unit can input the user's social media activity data into AI and have the AI ​​execute the suggestion of related goals.

[0102] The creation unit can estimate the user's emotion and adjust the content of the training plan based on the estimated user's emotion. The creation unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and adjusts the content of the training plan based on the result. For example, when the user is relaxed, the creation unit creates a training plan at a leisurely pace. Also, when the user is in a hurry, the creation unit can also create a training plan that is effective in a short time. Furthermore, when the user is excited, the creation unit creates a challenging and stimulating training plan. In this way, it is possible to provide an optimal training plan for the user by adjusting the content of the training plan according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the content of the training plan.

[0103] When creating a training plan, the creation unit can create an optimal plan by referring to the user's past training history. The creation unit, for example, acquires the user's past training history from a database and creates an optimal training plan based on the data. For example, the creation unit creates an effective plan based on the results of the user's past training. The creation unit can also create a plan to avoid the risk of injury from the user's past training history. Furthermore, the creation unit analyzes the user's past training history and creates a plan according to the user's progress. As a result, a more effective training plan can be created by referring to the user's past training history. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the user's past training history data into AI and cause AI to create an optimal training plan.

[0104] The creation unit can customize the plan based on the user's current physical fitness level when creating the training plan. The creation unit creates an appropriate training plan based on the user's current physical fitness level (heart rate, muscle strength, etc.), for example. For example, the creation unit adjusts the intensity and frequency of training according to the user's physical fitness level. The creation unit can also monitor the user's physical fitness level in real time and dynamically customize the plan. For example, the creation unit adjusts the training plan based on the user's heart rate and muscle strength data. This makes it possible to provide an optimal training plan for the user by customizing the plan based on the user's current physical fitness level. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the user's physical fitness level data to AI and cause AI to customize the training plan.

[0105] The creation unit can estimate the user's emotions and determine the priority of the training plan based on the estimated user's emotions. The creation unit, for example, estimates the user's emotions using facial expression recognition or voice analysis, and determines the priority of the training plan based on the result. For example, when the user is highly motivated, the creation unit preferentially suggests challenging training. In addition, when the user is tired, the creation unit can also preferentially suggest recovery training. Furthermore, when the user feels anxious, the creation unit preferentially suggests training that gives a sense of security. In this way, by determining the priority of the training plan according to the user's emotions, it is possible to provide an optimal training plan for the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data into an AI and cause the AI ​​to execute the determination of the priority of the training plan.

[0106] The creation unit can propose an optimal training location in consideration of the geographical location information of the user when creating a training plan. The creation unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and proposes an optimal training location based on the information. For example, the creation unit proposes an appropriate training location in consideration of the climate and topography of the area where the user lives. The creation unit can also propose a realistic training location based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the creation unit proposes training locations related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to propose an optimal training location for the user by considering the geographical location information of the user. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the geographical location information data of the user to AI and cause the AI ​​to execute the proposal of an optimal training location.

[0107] The creation unit can analyze the user's social media activities when creating a training plan, and can propose a related training plan based on the analysis results. For example, the creation unit can propose a related training plan based on the fitness goals and achievements shared by the user on social media. The creation unit can also propose a plan to increase motivation by referring to a training plan achieved by the user's social media friends. Furthermore, the creation unit can propose a related plan by referring to a training plan of a fitness influencer the user follows on social media. In this way, a training plan that is highly relevant to the user can be proposed by analyzing the user's social media activities. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the user's social media activity data into AI and have the AI ​​execute the proposal of a related training plan.

[0108] The data collection unit can estimate the user's emotion and adjust the frequency of data collection based on the estimated user's emotion. The data collection unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and adjusts the frequency of data collection based on the result. For example, when the user is stressed, the data collection unit reduces the frequency of data collection to reduce the burden. In addition, when the user is relaxed, the data collection unit can also increase the frequency to collect detailed data. Furthermore, when the user is in a hurry, the data collection unit collects the minimum necessary data. In this way, by adjusting the frequency of data collection according to the user's emotion, it is possible to perform optimal data collection for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data collection unit can input the user's emotion data to an AI and cause the AI ​​to adjust the frequency of data collection.

[0109] The data collection unit can select the optimal data collection method by referring to the user's past exercise data when collecting data. The data collection unit, for example, acquires the user's past exercise data from a database and selects the optimal data collection method based on the data. For example, the data collection unit selects an effective data collection method based on data collected by the user in the past. The data collection unit can also select a data collection method for avoiding the risk of injury from the user's past exercise data. Furthermore, the data collection unit analyzes the user's past exercise data and selects a data collection method according to the progress. In this way, a more effective data collection method can be selected by referring to the user's past exercise data. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the user's past exercise data into AI and cause AI to select the optimal data collection method.

[0110] The data collection unit can customize the data collection items based on the user's current health condition at the time of data collection. The data collection unit selects appropriate data collection items based on the user's current health condition (heart rate, blood pressure, etc.), for example. For example, the data collection unit monitors the user's health condition in real time and customizes the data collection items based on the data. The data collection unit can also adjust the frequency and method of data collection according to the user's health condition. Furthermore, the data collection unit monitors the user's health condition in real time and dynamically customizes the data collection items. As a result, the data collection items can be customized based on the user's current health condition, thereby allowing optimal data collection for the user. A part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the user's health condition data into AI and cause AI to customize the data collection items.

[0111] The data collection unit can estimate the user's emotion and determine the priority of data collection based on the estimated user's emotion. The data collection unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and determines the priority of data collection based on the result. For example, when the user is highly motivated, the data collection unit preferentially collects detailed data. In addition, when the user is tired, the data collection unit can also preferentially collect the minimum amount of data required. Furthermore, when the user feels anxious, the data collection unit preferentially collects data that gives a sense of security. In this way, by determining the priority of data collection according to the user's emotion, it is possible to perform optimal data collection for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data collection unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the priority of data collection.

[0112] The data collection unit can preferentially collect highly relevant data in consideration of the user's geographical location information when collecting data. The data collection unit, for example, acquires the user's geographical location information using GPS data or a location information service, and preferentially collects highly relevant data based on the information. For example, the data collection unit collects appropriate data in consideration of the climate and topography of the area where the user lives. The data collection unit can also collect realistic data based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the data collection unit collects data related to local fitness events and activities based on the user's geographical location information. In this way, data highly relevant to the user can be collected by considering the user's geographical location information. A part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the user's geographical location information data to AI and cause AI to collect highly relevant data.

[0113] The data collection unit can analyze the user's social media activities during data collection and collect relevant data based on the analysis results. For example, the data collection unit collects relevant data based on fitness goals and achievements shared by the user on social media. The data collection unit can also collect data to increase motivation by referring to data achieved by the user's social media friends. Furthermore, the data collection unit collects relevant data by referring to data of fitness influencers the user follows on social media. In this way, data highly relevant to the user can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the user's social media activity data into AI and cause AI to collect related data.

[0114] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and adjusts the content of the feedback based on the result. For example, the feedback unit provides positive feedback when the user is relaxed. Also, the feedback unit can provide encouraging feedback when the user is stressed. Furthermore, the feedback unit provides concise and to the point feedback when the user is in a hurry. In this way, the feedback content can be adjusted according to the user's emotion, thereby providing optimal feedback for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to an AI and cause the AI ​​to adjust the feedback content.

[0115] The feedback unit can provide optimal feedback by referring to the user's past exercise data when providing feedback. The feedback unit, for example, obtains the user's past exercise data from a database and provides optimal feedback based on the data. For example, the feedback unit provides effective feedback based on the results of the user's past training. The feedback unit can also provide feedback to avoid the risk of injury from the user's past exercise data. Furthermore, the feedback unit analyzes the user's past exercise data and provides feedback according to the progress. As a result, more effective feedback can be provided by referring to the user's past exercise data. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's past exercise data into AI and cause AI to provide optimal feedback.

[0116] The feedback unit can adjust the strength of the feedback based on the user's current physical fitness level at the time of feedback. The feedback unit provides appropriate feedback based on the user's current physical fitness level (heart rate, muscle strength, etc.), for example. For example, the feedback unit adjusts the strength and frequency of the feedback according to the user's physical fitness level. The feedback unit can also monitor the user's physical fitness level in real time and dynamically adjust the strength of the feedback. This makes it possible to provide optimal feedback for the user by adjusting the strength of the feedback based on the user's current physical fitness level. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's physical fitness level data to AI and cause AI to adjust the strength of the feedback.

[0117] The feedback unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and determines the priority of feedback based on the result. For example, when the user is highly motivated, the feedback unit preferentially provides challenging feedback. In addition, when the user is tired, the feedback unit can also preferentially provide recovery feedback. Furthermore, when the user is feeling anxious, the feedback unit preferentially provides feedback that gives a sense of security. In this way, by determining the priority of feedback according to the user's emotion, it is possible to provide optimal feedback for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the priority of feedback.

[0118] The feedback unit can provide optimal feedback by considering the geographical location information of the user at the time of feedback. For example, the feedback unit obtains the geographical location information of the user using GPS data or a location information service, and provides optimal feedback based on the information. For example, the feedback unit provides appropriate feedback by considering the climate and topography of the area where the user lives. The feedback unit can also provide realistic feedback based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the feedback unit provides feedback related to local fitness events and activities based on the geographical location information of the user. In this way, optimal feedback for the user can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location information data of the user to AI and cause the AI ​​to provide optimal feedback.

[0119] The feedback unit can analyze the user's social media activity at the time of feedback and provide relevant feedback based on the analysis result. For example, the feedback unit can provide relevant feedback based on fitness goals and achievements shared by the user on social media. The feedback unit can also provide motivational feedback by referring to feedback achieved by the user's social media friends. Furthermore, the feedback unit can provide relevant feedback by referring to feedback from fitness influencers the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to provide feedback that is highly relevant to the user. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or can be performed without using AI. For example, the feedback unit can input the user's social media activity data into AI and cause the AI ​​to provide relevant feedback.

[0120] The tracking unit can estimate the user's emotion and adjust the display method of the tracking based on the estimated user's emotion. The tracking unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and adjusts the display method of the tracking based on the result. For example, when the user is nervous, the tracking unit provides a simple and highly visible display method. In addition, when the user is relaxed, the tracking unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the tracking unit provides a display method that focuses on the main points. In this way, the display method of the tracking can be adjusted according to the user's emotion, thereby providing an optimal display method for the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the display method of the tracking.

[0121] The tracking unit can select the optimal tracking method by referring to the user's past exercise data during tracking. The tracking unit, for example, acquires the user's past exercise data from a database and selects the optimal tracking method based on the data. For example, the tracking unit selects an effective tracking method based on the results of the user's past training. The tracking unit can also select a tracking method to avoid the risk of injury from the user's past exercise data. Furthermore, the tracking unit analyzes the user's past exercise data and selects a tracking method according to the progress. In this way, a more effective tracking method can be selected by referring to the user's past exercise data. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's past exercise data into AI and cause AI to select the optimal tracking method.

[0122] The tracking unit can customize the tracking items based on the user's current health condition during tracking. The tracking unit selects appropriate tracking items based on the user's current health condition (heart rate, blood pressure, etc.). For example, the tracking unit monitors the user's health condition in real time and customizes the tracking items based on the data. The tracking unit can also adjust the frequency and method of tracking according to the user's health condition. Furthermore, the tracking unit monitors the user's health condition in real time and dynamically customizes the tracking items. As a result, the tracking items can be customized based on the user's current health condition, thereby performing optimal tracking for the user. A part 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 the user's health condition data into AI and cause AI to customize the tracking items.

[0123] The tracking unit can estimate the user's emotion and determine the tracking priority based on the estimated user's emotion. The tracking unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and determines the tracking priority based on the result. For example, when the user is highly motivated, the tracking unit can provide detailed tracking with priority. In addition, when the user is tired, the tracking unit can also provide tracking that gives a sense of security with priority when the user is anxious. In this way, by determining the tracking priority according to the user's emotion, it is possible to perform optimal tracking for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the tracking priority.

[0124] The tracking unit can provide an optimal tracking method by taking into account the geographical location information of the user during tracking. The tracking unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and provides an optimal tracking method based on the information. For example, the tracking unit provides an appropriate tracking method by taking into account the climate and topography of the area where the user lives. The tracking unit can also provide a realistic tracking method based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the tracking unit provides a tracking method related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to provide an optimal tracking method for the user by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the geographical location information data of the user to AI and cause the AI ​​to provide the optimal tracking method.

[0125] The tracking unit can analyze the user's social media activities during tracking and provide related tracking information based on the analysis results. For example, the tracking unit can provide related tracking information based on fitness goals and achievements shared by the user on social media. The tracking unit can also provide motivational tracking information by referring to tracking information achieved by the user's social media friends. Furthermore, the tracking unit can provide related tracking information by referring to tracking information of fitness influencers the user follows on social media. In this way, by analyzing the user's social media activities, it is possible to provide tracking information that is highly relevant to the user. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's social media activity data into AI and cause AI to provide related tracking information.

[0126] The adjustment unit can estimate the user's emotion and change the adjustment method of the training plan based on the estimated user's emotion. The adjustment unit estimates the user's emotion using, for example, facial expression recognition or voice analysis, and changes the adjustment method of the training plan based on the result. For example, when the user is relaxed, the adjustment unit adjusts the training plan to a leisurely pace. Also, when the user is in a hurry, the adjustment unit can adjust the training plan to a short and effective training plan. Furthermore, when the user is excited, the adjustment unit adjusts the training plan to a challenging and stimulating training plan. In this way, by changing the adjustment method of the training plan according to the user's emotion, it is possible to provide an optimal training plan for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute a change in the adjustment method of the training plan.

[0127] During adjustment, the adjustment unit can select the optimal adjustment method by referring to the user's past training data. The adjustment unit, for example, acquires the user's past training data from a database and selects the optimal adjustment method based on the data. For example, the adjustment unit selects an effective adjustment method based on the results of the user's past training. The adjustment unit can also select an adjustment method to avoid the risk of injury from the user's past training data. Furthermore, the adjustment unit analyzes the user's past training data and selects an adjustment method according to the progress. As a result, a more effective adjustment method can be selected by referring to the user's past training data. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past training data to AI and cause the AI ​​to select the optimal adjustment method.

[0128] During the adjustment, the adjustment unit can customize the training plan based on the user's current physical fitness level. The adjustment unit adjusts an appropriate training plan based on, for example, the user's current physical fitness level (heart rate, muscle strength, etc.). For example, the adjustment unit adjusts the intensity and frequency of training according to the user's physical fitness level. The adjustment unit can also monitor the user's physical fitness level in real time and dynamically customize the training plan. This makes it possible to provide an optimal training plan for the user by customizing the training plan based on the user's current physical fitness level. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's physical fitness level data to AI and cause AI to customize the training plan.

[0129] The adjustment unit can estimate the user's emotion and determine the priority of the training plan based on the estimated user's emotion. The adjustment unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and determines the priority of the training plan based on the result. For example, when the user is highly motivated, the adjustment unit preferentially suggests a challenging training. In addition, when the user is tired, the adjustment unit can also preferentially suggest a recovery training. Furthermore, when the user feels anxious, the adjustment unit preferentially suggests a training that gives a sense of security. In this way, by determining the priority of the training plan according to the user's emotion, it is possible to provide an optimal training plan for the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of the training plan.

[0130] The adjustment unit can provide an optimal training plan by considering the geographical location information of the user at the time of adjustment. The adjustment unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and provides an optimal training plan based on the information. For example, the adjustment unit provides an appropriate training plan by considering the climate and topography of the area where the user lives. The adjustment unit can also provide a realistic training plan based on places that the user frequently visits (gyms, parks, etc.). Furthermore, the adjustment unit provides a training plan related to local fitness events and activities based on the geographical location information of the user. In this way, it is possible to provide an optimal training plan for the user by considering the geographical location information of the user. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the geographical location information data of the user to AI and cause the AI ​​to provide an optimal training plan.

[0131] During the adjustment, the adjustment unit can analyze the user's social media activities and provide a related training plan based on the analysis results. For example, the adjustment unit can provide a related training plan based on fitness goals and achievements shared by the user on social media. The adjustment unit can also provide a plan to increase motivation by referring to training plans achieved by the user's social media friends. Furthermore, the adjustment unit can provide a related plan by referring to training plans of fitness influencers the user follows on social media. In this way, a training plan that is highly relevant to the user can be provided by analyzing the user's social media activities. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's social media activity data into AI and cause AI to provide a related training plan.

[0132] The advice unit can estimate the user's emotion and adjust the nutrition and rest advice based on the estimated user's emotion. The advice unit, for example, estimates the user's emotion using facial expression recognition or voice analysis, and adjusts the nutrition and rest advice based on the result. For example, when the user is relaxed, the advice unit suggests a balanced meal and moderate rest. In addition, when the user is stressed, the advice unit can also suggest a meal and a relaxation method that is effective for stress reduction. Furthermore, when the user is in a hurry, the advice unit suggests a simple and nutritious meal and a short and effective rest method. In this way, it is possible to provide optimal advice for the user by adjusting the nutrition and rest advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotional data into the AI ​​and have the AI ​​adjust advice regarding nutrition and rest.

[0133] The advice unit can provide optimal advice by referring to the user's past health data when giving advice. The advice unit, for example, acquires the user's past health data from a database and provides optimal advice based on the data. For example, the advice unit provides effective advice based on the results of health management performed by the user in the past. The advice unit can also provide advice to avoid the risk of injury from the user's past health data. Furthermore, the advice unit analyzes the user's past health data and provides advice according to the progress. In this way, more effective advice can be provided by referring to the user's past health data. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or may be performed without using AI. For example, the advice unit can input the user's past health data into AI and cause AI to provide optimal advice.

[0134] The advice unit can customize the contents of the advice based on the user's current lifestyle habits when providing advice. The advice unit provides appropriate advice based on, for example, the user's current lifestyle habits (eating habits, sleep patterns, etc.). For example, the advice unit monitors the user's lifestyle habits in real time and customizes the contents of the advice based on the data. The advice unit can also adjust the frequency and contents of the advice according to the user's lifestyle habits. Furthermore, the advice unit monitors the user's lifestyle habits in real time and dynamically customizes the contents of the advice. As a result, the advice content can be customized based on the user's current lifestyle habits, thereby providing optimal advice for the user. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's lifestyle habit data to AI and cause AI to customize the contents of the advice.

[0135] The advice unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. The advice unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis, and determine the priority of advice based on the result. For example, when the user is highly motivated, the advice unit can provide challenging advice with priority. In addition, when the user is tired, the advice unit can also provide recovery advice with priority. Furthermore, when the user is feeling anxious, the advice unit can provide advice that gives a sense of security with priority. In this way, by determining the priority of advice according to the user's emotion, it is possible to provide optimal advice for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of advice.

[0136] The advice unit can provide optimal advice by considering the geographical location information of the user when providing advice. The advice unit, for example, acquires the geographical location information of the user using GPS data or a location information service, and provides optimal advice based on the information. For example, the advice unit provides appropriate advice by considering the climate and topography of the area where the user lives. The advice unit can also provide realistic advice based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the advice unit provides advice related to local fitness events and activities based on the geographical location information of the user. In this way, optimal advice for the user can be provided by considering the geographical location information of the user. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the geographical location information data of the user to AI and cause the AI ​​to provide optimal advice.

[0137] The advice unit can analyze the user's social media activity at the time of giving advice, and provide relevant advice based on the analysis result. For example, the advice unit can provide relevant advice based on fitness goals and achievements shared by the user on social media. The advice unit can also provide advice to increase motivation by referring to advice achieved by the user's social media friends. Furthermore, the advice unit can provide relevant advice by referring to advice of fitness influencers the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to provide advice that is highly relevant to the user. Some or all of the above-mentioned processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit can input the user's social media activity data into AI and cause the AI ​​to provide relevant advice.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The goal receiving unit can analyze the user's past fitness goal history and suggest an optimal goal setting method. For example, the goal receiving unit acquires the user's past fitness goal history from a database and suggests an optimal goal setting method based on the data. For example, the goal receiving unit automatically displays fitness goals set by the user in the past as candidates. The goal receiving unit can also preferentially suggest goal setting methods (such as voice and text) that the user has used in the past. Furthermore, the goal receiving unit can predict and suggest goals to be set in a specific time period from the user's past goal history. In this way, a more appropriate goal setting method can be suggested by analyzing the user's past fitness goal history.

[0140] The advice unit can estimate the user's emotions and adjust the nutrition and rest advice based on the estimated user's emotions. For example, the advice unit estimates the user's emotions using facial expression recognition or voice analysis, and adjusts the nutrition and rest advice based on the result. For example, the advice unit can suggest a balanced diet and moderate rest when the user is relaxed. In addition, the advice unit can also suggest a diet and relaxation method that is effective for reducing stress when the user is feeling stressed. Furthermore, the advice unit can suggest a simple and nutritious diet and a short and effective rest method when the user is in a hurry. In this way, the nutrition and rest advice can be adjusted according to the user's emotions, thereby providing optimal advice for the user.

[0141] The data collection unit can preferentially collect highly relevant data taking into account the geographical location information of the user. For example, the data collection unit acquires the geographical location information of the user using GPS data or a location information service, and preferentially collects highly relevant data based on the information. For example, the data collection unit collects appropriate data taking into account the climate and topography of the area where the user lives. The data collection unit can also collect realistic data based on places frequently visited by the user (gyms, parks, etc.). Furthermore, the data collection unit collects data related to local fitness events and activities based on the geographical location information of the user. In this way, data highly relevant to the user can be collected by taking into account the geographical location information of the user.

[0142] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, the feedback unit estimates the user's emotion using facial expression recognition or voice analysis, and adjusts the content of the feedback based on the result. For example, the feedback unit provides positive feedback when the user is relaxed. Also, the feedback unit can provide encouraging feedback when the user is stressed. Furthermore, the feedback unit provides concise and to the point feedback when the user is in a hurry. In this way, the feedback content can be adjusted according to the user's emotion, thereby providing optimal feedback for the user.

[0143] The tracking unit can select the optimal tracking method by referring to the user's past exercise data. For example, the tracking unit acquires the user's past exercise data from a database and selects the optimal tracking method based on the data. For example, the tracking unit selects an effective tracking method based on the results of the user's past training. The tracking unit can also select a tracking method for avoiding the risk of injury from the user's past exercise data. Furthermore, the tracking unit analyzes the user's past exercise data and selects a tracking method according to the user's progress. In this way, a more effective tracking method can be selected by referring to the user's past exercise data.

[0144] The adjustment unit can estimate the user's emotions and change the adjustment method of the training plan based on the estimated user's emotions. For example, the adjustment unit estimates the user's emotions using facial expression recognition or voice analysis, and changes the adjustment method of the training plan based on the result. For example, when the user is relaxed, the adjustment unit adjusts the training plan to a leisurely pace. In addition, when the user is in a hurry, the adjustment unit can also adjust the training plan to a short and effective training plan. Furthermore, when the user is excited, the adjustment unit adjusts the training plan to a challenging and stimulating training plan. In this way, by changing the adjustment method of the training plan according to the user's emotions, it is possible to provide an optimal training plan for the user.

[0145] When giving advice, the advice unit can provide optimal advice by referring to the user's past health data. For example, the advice unit obtains the user's past health data from a database and provides optimal advice based on that data. For example, the advice unit provides effective advice based on the results of health management performed by the user in the past. The advice unit can also provide advice to avoid the risk of injury based on the user's past health data. Furthermore, the advice unit analyzes the user's past health data and provides advice according to the user's progress. In this way, more effective advice can be provided by referring to the user's past health data.

[0146] The goal receiving unit can estimate the user's emotions and determine the priority order of goals based on the estimated user's emotions. For example, the goal receiving unit estimates the user's emotions using facial expression recognition or voice analysis, and determines the priority order of goals based on the result. For example, when the user is highly motivated, the goal receiving unit preferentially suggests challenging goals. In addition, when the user is tired, the goal receiving unit can also preferentially suggest realistic and achievable goals. Furthermore, when the user is feeling anxious, the goal receiving unit preferentially suggests goals that give a sense of security. In this way, by determining the priority order of goals according to the user's emotions, it is possible to suggest optimal goals for the user.

[0147] When creating a training plan, the creation unit can customize the plan based on the user's current physical fitness level. For example, the creation unit creates an appropriate training plan based on the user's current physical fitness level (heart rate, muscle strength, etc.). For example, the creation unit adjusts the intensity and frequency of training according to the user's physical fitness level. The creation unit can also monitor the user's physical fitness level in real time and dynamically customize the plan. For example, the creation unit adjusts the training plan based on the user's heart rate and muscle strength data. In this way, the plan can be customized based on the user's current physical fitness level, thereby providing an optimal training plan for the user.

[0148] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, the data collection unit estimates the user's emotions using facial expression recognition or voice analysis, and adjusts the frequency of data collection based on the results. For example, when the user is feeling stressed, the data collection unit reduces the frequency of data collection to reduce the burden. In addition, when the user is relaxed, the data collection unit can also increase the frequency to collect detailed data. Furthermore, when the user is in a hurry, the data collection unit collects the minimum amount of data necessary. In this way, by adjusting the frequency of data collection according to the user's emotions, optimal data collection for the user can be performed.

[0149] The process flow of the second embodiment will be briefly described below.

[0150] Step 1: The goal receiving unit receives a fitness goal of the user. The fitness goal of the user may include, for example, weight loss, muscle building, and endurance improvement. The goal receiving unit receives the fitness goal input by the user and stores it in a database. Step 2: The creation unit creates a training plan based on the fitness goal accepted by the goal acceptance unit. The training plan includes the type, frequency, and intensity of exercise. For example, the creation unit creates a plan that combines strength training three times a week and aerobic exercise twice a week based on the user's fitness goal. Step 3: The data collection unit collects the user's exercise data. The exercise data includes heart rate, number of steps, calorie consumption, etc. The data collection unit collects the exercise data from a smartphone or a wearable device. Step 4: The feedback unit provides real-time feedback on the user's exercise form and intensity based on the data collected by the data collection unit. Feedback is provided by methods such as audio feedback and visual feedback. The feedback unit uses AI to analyze the user's exercise form and provides feedback on whether the user is exercising with the correct form. Step 5: The tracking unit tracks the progress of the training based on the data collected by the data collection unit. Tracking is performed by methods such as goal achievement rate, training completion rate, etc. The tracking unit provides a dashboard to display information such as how many calories the user has burned and how far they have walked. Step 6: The adjustment unit adjusts the training plan based on the progress tracked by the tracking unit. The adjustment is performed by changing the training plan contents, frequency, etc. The adjustment unit changes the training intensity or adds rest days depending on the user's condition.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0153] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] For example, the goal receiving unit is realized by the receiving device 38 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The creation unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The data collection unit collects exercise data using, for example, the camera 42 or the communication I / F 44 of the smart device 14. The feedback unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The tracking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides a dashboard. The adjustment unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0165] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0167] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0169] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0170] For example, the goal acceptance unit is realized by the microphone 238 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The creation unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The data collection unit collects exercise data using, for example, the camera 42 and the communication I / F 44 of the smart glasses 214. The feedback unit is realized by, for example, the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The tracking unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and provides a dashboard. The adjustment unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] 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.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0178] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0181] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0185] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0186] For example, the goal receiving unit is realized by the microphone 238 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The creation unit is realized by the specific processing unit 290 of the data processing device 12. The data collection unit collects exercise data using, for example, the camera 42 and the communication I / F 44 of the headset type terminal 314. The feedback unit is realized by, for example, the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The tracking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides a dashboard. The adjustment unit is realized by, for example, the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0190] 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.

[0191] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0192] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0193] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0194] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0195] 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.

[0196] 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.

[0197] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0198] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0199] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0200] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0201] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0202] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0203] For example, the target receiving unit is realized by the microphone 238 of the robot 414 and the specific processing unit 290 of the data processing device 12. The creation unit is realized by the specific processing unit 290 of the data processing device 12, for example. The data collection unit collects motion data using the camera 42 and the communication I / F 44 of the robot 414, for example. The feedback unit is realized by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, for example. The tracking unit is realized by the specific processing unit 290 of the data processing device 12, for example, and provides a dashboard. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0204] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0205] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0206] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0207] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0208] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0209] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0210] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0211] 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.

[0212] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0213] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0214] 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.

[0215] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0216] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0217] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0218] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0219] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0220] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0221] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0222] (Appendix 1) a goal receiving unit that receives a fitness goal of a user; a creation unit that creates a training plan based on the fitness goal accepted by the goal acceptance unit; A data collection unit that collects exercise data of the user; a feedback unit that provides real-time feedback on the form and intensity of the user's exercise based on the data collected by the data collection unit; A tracking unit that tracks the progress of training based on the data collected by the data collection unit; and an adjustment unit that adjusts the training plan based on the progress tracked by the tracking unit. A system characterized by: (Appendix 2) Equipping the staff with advice on nutrition and rest 2. The system of claim 1, (Appendix 3) The data collection unit includes: Collecting the user's exercise data from at least one of a smartphone or a wearable device. 2. The system of claim 1, (Appendix 4) The feedback unit is Uses AI to provide real-time feedback on exercise form and intensity 2. The system of claim 1, (Appendix 5) The data collection unit includes: Collect at least one of heart rate, number of steps, or calorie consumption from a wearable device with a sensor. 2. The system of claim 1, (Appendix 6) The tracking unit includes: Providing a dashboard to visualize training progress 2. The system of claim 1, (Appendix 7) The adjustment unit is Dynamically changing the training plan according to the user's condition. 2. The system of claim 1, (Appendix 8) The adjustment unit is Estimating the muscle tone of the user and adjusting the training plan based on the estimation result. 2. The system of claim 1, (Appendix 9) The advice unit, Monitor the user's water intake and advise appropriate hydration 3. The system of claim 2, (Appendix 10) The goal receiving unit: Estimating a user's emotion and adjusting a fitness goal input method based on the estimated user's emotion 2. The system of claim 1, (Appendix 11) The goal receiving unit: Analyze the user's past fitness goal history and suggest optimal goal setting methods 2. The system of claim 1, (Appendix 12) The goal receiving unit: When accepting a goal, filter goals based on the user's current health and lifestyle habits 2. The system of claim 1, (Appendix 13) The goal receiving unit: Estimating user emotions and prioritizing goals based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The goal receiving unit: When accepting a goal, the system takes into account the user's geographic location information and prioritizes the presentation of highly relevant goals. 2. The system of claim 1, (Appendix 15) The goal receiving unit: Upon goal entry, we analyze the user's social media activity and suggest relevant goals. 2. The system of claim 1, (Appendix 16) The creation unit is Inferring a user's emotions and adjusting the training plan based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The creation unit is When creating a training plan, the system creates the optimal plan by referring to the user's past training history. 2. The system of claim 1, (Appendix 18) The creation unit is When creating a training plan, customize the plan based on the user's current fitness level 2. The system of claim 1, (Appendix 19) The creation unit is Infer user emotions and prioritize training plans based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The creation unit is When creating a training plan, the system will suggest the best training location based on the user's geographic location. 2. The system of claim 1, (Appendix 21) The creation unit is When creating a training plan, analyze the user's social media activity to suggest relevant training plans 2. The system of claim 1, (Appendix 22) The data collection unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The data collection unit includes: When collecting data, the optimal data collection method is selected by referring to the user's past exercise data. 2. The system of claim 1, (Appendix 24) The data collection unit includes: At the time of data collection, customize the data collection items based on the user's current health status 2. The system of claim 1, (Appendix 25) The data collection unit includes: Inferring user sentiment and prioritizing data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The data collection unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 27) The data collection unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 28) The feedback unit is Inferring user emotions and adjusting feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The feedback unit is When providing feedback, the system provides optimal feedback by referring to the user's past exercise data. 2. The system of claim 1, (Appendix 30) The feedback unit is Upon feedback, adjust the strength of the feedback based on the user's current fitness level 2. The system of claim 1, (Appendix 31) The feedback unit is Estimate user emotions and prioritize feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 32) The feedback unit is When providing feedback, provide optimal feedback by considering the user's geographic location information 2. The system of claim 1, (Appendix 33) The feedback unit is When feedback is provided, analyze the user's social media activity and provide relevant feedback 2. The system of claim 1, (Appendix 34) The tracking unit includes: Inferring a user's emotion and adjusting the way tracking is displayed based on the estimated user's emotion 2. The system of claim 1, (Appendix 35) The tracking unit includes: When tracking, the optimal tracking method is selected by referring to the user's past exercise data. 2. The system of claim 1, (Appendix 36) The tracking unit includes: When tracking, customize the tracking items based on the user's current health status 2. The system of claim 1, (Appendix 37) The tracking unit includes: Estimate user emotions and prioritize tracking based on the estimated user emotions 2. The system of claim 1, (Appendix 38) The tracking unit includes: When tracking, provide the optimal tracking method by taking into account the user's geographic location information 2. The system of claim 1, (Appendix 39) The tracking unit includes: When tracking, to analyze your social media activity and provide you with relevant tracking information 2. The system of claim 1, (Appendix 40) The adjustment unit is Inferring a user's emotions and adjusting the training plan accordingly 2. The system of claim 1, (Appendix 41) The adjustment unit is When making adjustments, the system refers to the user's past training data to select the optimal adjustment method. 2. The system of claim 1, (Appendix 42) The adjustment unit is When calibrating, customize a training plan based on the user's current fitness level 2. The system of claim 1, (Appendix 43) The adjustment unit is Infer user emotions and prioritize training plans based on the estimated user emotions 2. The system of claim 1, (Appendix 44) The adjustment unit is When adjusting, it takes into account the user's geographic location to provide the optimal training plan. 2. The system of claim 1, (Appendix 45) The adjustment unit is When training, analyze your social media activity and provide relevant training plans 2. The system of claim 1, (Appendix 46) The advice unit, Estimate the user's emotions and adjust nutrition and rest advice based on the estimated user emotions 3. The system of claim 2, (Appendix 47) The advice unit, When providing advice, the system refers to the user's past health data to provide optimal advice. 3. The system of claim 2, (Appendix 48) The advice unit, When providing advice, customize the advice content based on the user's current lifestyle habits 3. The system of claim 2, (Appendix 49) The advice unit, Estimate the user's emotions and prioritize advice based on the estimated user emotions. 3. The system of claim 2, (Appendix 50) The advice unit, When providing advice, the system takes into account the user's geographic location to provide optimal advice. 3. The system of claim 2, (Appendix 51) The advice unit, When providing advice, the system analyzes the user's social media activity to provide relevant advice 3. The system of claim 2, [Explanation of symbols]

[0223] 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 goal receiving unit that receives a fitness goal of a user; a creation unit that creates a training plan based on the fitness goal accepted by the goal acceptance unit; A data collection unit that collects exercise data of the user; a feedback unit that provides real-time feedback on the form and intensity of the user's exercise based on the data collected by the data collection unit; A tracking unit that tracks the progress of training based on the data collected by the data collection unit; and an adjustment unit that adjusts the training plan based on the progress tracked by the tracking unit. A system characterized by:

2. Equipping the staff with advice on nutrition and rest 2. The system of claim 1 .

3. The data collection unit includes: Collecting the user's exercise data from at least one of a smartphone or a wearable device.

2. The system of claim 1 .

4. The feedback unit is Uses AI to provide real-time feedback on exercise form and intensity 2. The system of claim 1 .

5. The data collection unit includes: Collect at least one of heart rate, number of steps, or calorie consumption from a wearable device with a sensor.

2. The system of claim 1 .

6. The tracking unit includes: Providing a dashboard to visualize training progress 2. The system of claim 1 .

7. The adjustment unit is Dynamically changing the training plan according to the user's condition.

2. The system of claim 1 .

8. The adjustment unit is Estimating the muscle tone of the user and adjusting the training plan based on the estimation result.

2. The system of claim 1 .

9. The advice unit, Monitor the user's water intake and advise appropriate hydration 3. The system of claim 2.

10. The goal receiving unit: Estimating an emotion of the user and adjusting a method of inputting a fitness goal based on the estimated emotion of the user.

2. The system of claim 1 .

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