system

Abstract

The system according to this embodiment aims to provide a fitness program optimized for each individual user, supporting accurate movements while maintaining motivation. [Solution] The system according to the embodiment comprises a collection unit, a creation unit, a support unit, a motivation maintenance unit, an answer unit, and a prediction unit. The collection unit collects information on the user's body type, goals, lifestyle, and past exercise history. The creation unit analyzes the information collected by the collection unit and creates an individually appropriate training menu. The support unit uses the smartphone camera to perform real-time form checks and support accurate movements. The motivation maintenance unit provides visualization of achievement and challenge functions. The answer unit answers questions about nutrition and exercise 24 hours a day. The prediction unit predicts and visualizes future changes in body type based on the current exercise status and goals.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide a fitness program optimized for each individual user, and there is a problem that user motivation maintenance and accurate operation support are not sufficiently performed.

[0005] The system according to the embodiment aims to provide a fitness program optimized for each individual user and support accurate operation while maintaining motivation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a creation unit, a support unit, a motivation maintenance unit, a response unit, and a prediction unit. The collection unit collects information about the user's body type, goals, lifestyle, and past exercise history. The creation unit analyzes the information collected by the collection unit and creates an individually appropriate training menu. The support unit uses the smartphone camera to perform real-time form checks and support accurate movements. The motivation maintenance unit provides visualization of achievement and challenge functions. The response unit answers questions about nutrition and exercise 24 hours a day. The prediction unit predicts and visualizes future changes in body type based on the current exercise status and goals. [Effects of the Invention]

[0007] The system according to this embodiment can provide a fitness program optimized for each individual user, supporting precise movements while maintaining motivation. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​personal trainer system according to an embodiment of the present invention is a smartphone application that utilizes the latest artificial intelligence technology to provide a fitness program optimized for each individual user. This AI personal trainer system analyzes the user's body type, goals, lifestyle, past exercise history, etc., in detail, and the AI ​​creates an individually optimized training menu. Furthermore, it uses the smartphone's camera to perform real-time form checks and support accurate movements. It also incorporates gamification elements, supporting user motivation through the visualization of achievement and challenge functions. The AI ​​carefully answers questions about nutrition and exercise 24 hours a day, providing an experience as if a personal trainer is always by the user's side. In addition, it is equipped with a function that predicts and visualizes future body shape changes based on the current exercise status and goals. This contributes to the long-term motivation maintenance of the user. With an affordable monthly subscription setting, users can receive high-quality personal training services at a much lower cost than hiring a personal trainer. The AI ​​personal trainer system is tailored to the busy lifestyles of modern people and supports the establishment of a continuous exercise habit. For example, if a user desires strength training, the AI ​​will propose an optimal training menu based on past exercise history and current body type. Next, the system uses the smartphone's camera to check form in real time, supporting accurate movement. For example, when a user performs squats, the AI ​​analyzes the video captured by the smartphone's camera and provides real-time advice on how to correct their form. Furthermore, gamification elements are incorporated to support user motivation through the visualization of progress and challenge functions. For example, the system visualizes the progress towards the user's set goals and allows users to set new goals through the challenge function, thus maintaining continuous motivation. In addition, the AI ​​provides thorough answers to questions about nutrition and exercise 24 hours a day, providing an experience as if a personal trainer were always by the user's side. For example, if a user asks a question about diet, the AI ​​provides appropriate nutritional advice. The system also includes a function that predicts and visualizes future body shape changes based on current exercise status and goals.For example, when a user inputs their current exercise status and goals, the AI ​​predicts and visualizes future body shape changes, contributing to long-term motivation maintenance. This allows the AI ​​personal trainer system to leverage the latest artificial intelligence technology to provide a fitness program optimized for each individual user, catering to the busy lifestyles of modern people and supporting the establishment of consistent exercise habits. The AI ​​personal trainer system collects information such as the user's body shape, goals, lifestyle, and past exercise history to create individually optimized training menus, support precise movements, visualize progress and offer challenge functions, provide 24 / 7 answers to questions about nutrition and exercise, and predict and visualize future body shape changes.

[0029] The AI ​​personal trainer system according to this embodiment comprises a data collection unit, a data creation unit, a support unit, a motivation maintenance unit, a response unit, and a prediction unit. The data collection unit collects information about the user's body type, goals, lifestyle, and past exercise history. The data collection unit collects information based on data entered by the user, for example. The data collection unit can also acquire data from wearable devices. Furthermore, the data collection unit can also collect information from the user's social media activity. For example, the data collection unit collects information based on data such as weight, height, and target weight entered by the user. The data collection unit collects information based on data such as heart rate, steps taken, and calories burned acquired from wearable devices. The data collection unit collects information from the user's social media activity, such as posts about exercise and training methods the user is interested in. The data creation unit analyzes the information collected by the data collection unit and creates an individually appropriate training menu. The data creation unit analyzes the collected data using AI, for example, and creates an optimal training menu. Furthermore, the data creation unit can adjust the training menu based on the user's goals. Furthermore, the data creation unit can apply different algorithms depending on the user's body type and exercise history. For example, the creation unit uses AI to analyze the user's body type and exercise history to create an optimal training menu. Based on the user's goals, the creation unit provides a detailed training menu for short-term goals. The creation unit applies different algorithms depending on the user's body type and exercise history to create an optimal training menu. The support unit uses the smartphone camera to perform real-time form checks and support accurate movement. For example, the support unit uses AI to analyze videos taken with the smartphone camera and provides real-time advice on areas for form correction. The support unit can also improve the accuracy of the checks by considering the user's movement history. Furthermore, the support unit can also perform checks considering the user's body type and exercise history. For example, the support unit uses AI to analyze videos taken with the smartphone camera and provides real-time advice on areas for form correction. The support unit improves the accuracy of form checks based on the user's movement history.The support unit performs appropriate form checks, taking into account the user's body type and exercise history. The motivation maintenance unit provides visualization of achievement and challenge functions. For example, the motivation maintenance unit maintains continuous motivation by visualizing the progress towards goals set by the user and setting new goals through the challenge function. The motivation maintenance unit can also estimate the user's emotions and adjust the display method of achievement based on the estimated emotions. Furthermore, the motivation maintenance unit can predict the current achievement level by referring to the user's past achievement data. For example, the motivation maintenance unit visualizes the progress towards goals set by the user and sets new goals through the challenge function. The motivation maintenance unit estimates the user's emotions and adjusts the display method of achievement based on the estimated emotions. The motivation maintenance unit predicts the current achievement level based on the user's past achievement data. The answer unit answers questions about nutrition and exercise 24 hours a day. The answer unit provides appropriate answers to user questions, for example, using AI. The answer unit can also estimate the user's emotions and adjust the expression of answers based on the estimated emotions. Furthermore, the answering unit can adjust the level of detail in its answers based on the user's questions. For example, the answering unit uses AI to provide appropriate answers to the user's questions. The answering unit estimates the user's emotions and adjusts the way it expresses its answers based on those emotions. The answering unit provides answers with the optimal level of detail depending on the user's questions. The prediction unit predicts and visualizes future body shape changes based on the current exercise status and goals. For example, the prediction unit uses AI to predict future body shape changes based on the current exercise status and goals. The prediction unit can also estimate the user's emotions and adjust the prediction method for body shape changes based on those emotions. Furthermore, the prediction unit can improve the accuracy of its predictions by referring to the user's past exercise history. For example, the prediction unit uses AI to predict future body shape changes based on the current exercise status and goals. The prediction unit estimates the user's emotions and adjusts the prediction method for body shape changes based on those emotions. The prediction unit improves the accuracy of its body shape change predictions based on the user's past exercise history.As a result, the AI ​​personal trainer system according to this embodiment can collect information such as the user's body type, goals, lifestyle, and past exercise history, create an individually optimized training menu, support precise movements, provide visualization of progress and challenge functions, answer questions about nutrition and exercise 24 hours a day, and predict and visualize future changes in body shape.

[0030] The data collection unit collects information about the user's body type, goals, lifestyle, and past exercise history. For example, the unit collects information based on data entered by the user. It can also acquire data from wearable devices. Furthermore, the unit can collect information from the user's social media activity. For instance, the unit collects information based on data such as weight, height, and target weight entered by the user. It also collects information based on data such as heart rate, steps, and calories burned obtained from wearable devices. From the user's social media activity, the unit collects information such as exercise-related posts and training methods the user is interested in. Specifically, by having the user input their daily weight and diet into the app, the unit accumulates this data to understand the user's health status and progress. In addition to heart rate, steps, and calories burned, wearable devices can also acquire detailed data such as sleep patterns and stress levels. This allows for a comprehensive understanding of the user's lifestyle and health status. Furthermore, by collecting information from social media, it is possible to understand what kind of exercise and training the user is interested in and what motivates them. For example, if a user is participating in a specific fitness challenge, the training menu can be adjusted based on that information. This allows the data collection unit to centrally manage diverse user data and build a foundation for providing individually optimized training menus.

[0031] The creation unit analyzes the information collected by the collection unit and creates individually appropriate training menus. For example, the creation unit uses AI to analyze the collected data and create the optimal training menu. The creation unit can also adjust the training menu based on the user's goals. Furthermore, the creation unit can apply different algorithms depending on the user's body type and exercise history. For example, the creation unit uses AI to analyze the user's body type and exercise history and create the optimal training menu. Based on the user's goals, the creation unit provides a detailed training menu for short-term goals. The creation unit applies different algorithms depending on the user's body type and exercise history to create the optimal training menu. Specifically, the AI ​​analyzes the user's body type data and exercise history and creates a menu that considers the balance of strength training, aerobic exercise, stretching, etc. For example, for strength training, it suggests appropriate weight, repetitions, and sets based on the user's muscle mass and body fat percentage. For aerobic exercise, it sets the optimal exercise intensity and time based on heart rate and calories burned. For stretching, it provides a safe and effective stretching menu considering the user's flexibility and past injury history. Furthermore, the creation unit can continuously adjust the training menu based on user feedback. For example, if a user finds a particular exercise too difficult, the intensity of that exercise can be adjusted or alternative exercises can be suggested. This allows the creation unit to provide an optimal training menu tailored to the user's individual needs and goals, supporting effective training.

[0032] The support unit uses the smartphone's camera to perform real-time form checks and support accurate movement. For example, the support unit uses AI to analyze videos taken with the smartphone's camera and provide real-time advice on areas for form correction. The support unit can also improve the accuracy of the checks by considering the user's movement history. Furthermore, the support unit can perform checks considering the user's body type and exercise history. For example, the support unit uses AI to analyze videos taken with the smartphone's camera and provides real-time advice on areas for form correction. The support unit improves the accuracy of form checks based on the user's movement history. The support unit performs appropriate form checks considering the user's body type and exercise history. Specifically, the AI ​​analyzes the user's movements and evaluates joint angles, movement speed, balance, etc. For example, during squats, it checks knee position, back angle, foot width, etc., to determine if the correct form is being used. If there are problems with the form, the AI ​​points out specific areas for correction and advises how to correct them. Furthermore, the support unit can improve the accuracy of form checks based on the user's past movement data. For example, data from past performances of the same exercise can be referenced to evaluate areas for improvement and progress. Furthermore, by considering the user's body type and exercise history, personalized form checks can be provided. This allows the support team to assist users in training with correct form and continuing their exercise effectively and safely.

[0033] The motivation maintenance unit provides features for visualizing progress and setting challenges. For example, it helps users maintain continuous motivation by visualizing their progress towards goals they have set and by allowing them to set new goals through the challenge function. The motivation maintenance unit can also estimate the user's emotions and adjust the display method of progress based on those emotions. Furthermore, it can predict the current progress by referring to the user's past progress data. Specifically, progress visualization uses graphs and charts to visually display the user's progress. For example, it can display weekly and monthly exercise, calories burned, and weight changes in graphs, allowing users to grasp their progress at a glance. The challenge function helps users maintain motivation by setting new goals and striving towards them. For example, the system offers challenges such as achieving a specific amount of exercise within a certain period or performing a specific exercise a certain number of times. Furthermore, to estimate the user's emotions, the AI ​​analyzes user input data and behavioral patterns to estimate their current emotional state. For instance, if the frequency or intensity of exercise decreases, the system may determine that the user is losing motivation and suggest encouraging messages or new challenges. Additionally, by predicting current progress based on past achievement data, the system can assess whether the user is on track to achieve their goals. This allows the motivation maintenance unit to provide support for users to continue training consistently and maintain their motivation towards achieving their goals.

[0034] The answering unit provides 24 / 7 answers to questions about nutrition and exercise. For example, the answering unit uses AI to provide appropriate answers to user questions. It can also estimate the user's emotions and adjust the wording of its answers based on those emotions. Furthermore, it can adjust the level of detail in its answers based on the content of the user's questions. Specifically, the AI ​​analyzes the user's questions using natural language processing technology and generates appropriate answers. For example, if a user asks, "What kind of diet is good for building muscle?", the AI ​​will provide a detailed answer including the importance of protein, specific foods, and timing of meals. In addition, to estimate the user's emotions, the AI ​​analyzes the context and wording of the question to determine whether the user is troubled or interested. For example, if a user asks, "I haven't been exercising well lately," the AI ​​can offer words of encouragement and motivational advice. Furthermore, by adjusting the level of detail in the answer according to the question, it can accurately provide the information the user is looking for. For instance, it can provide basic information to beginner users and more specialized information to experienced users. As a result, the answering system can provide appropriate answers tailored to the user's needs 24 hours a day, resolving users' doubts and anxieties.

[0035] The prediction unit predicts and visualizes future body shape changes based on the user's current exercise status and goals. For example, the prediction unit uses AI to predict future body shape changes based on the current exercise status and goals. The prediction unit can also estimate the user's emotions and adjust the prediction method based on those emotions. Furthermore, the prediction unit can improve the accuracy of predictions by referring to the user's past exercise history. Specifically, the AI ​​simulates future body shape changes based on the user's current exercise data, dietary data, and target body shape. For example, it predicts what the user's body shape will be like in one month or three months if their current exercise level and diet are maintained, and visualizes this in graphs and 3D models. Additionally, to estimate the user's emotions, the AI ​​analyzes the user's input data and behavioral patterns to determine their current motivation and stress level. For example, if a user feels anxious about continuing to exercise, the prediction unit takes that emotion into account and provides realistic goal setting and encouraging messages. Furthermore, by improving the accuracy of predictions based on past exercise history, it can make more accurate predictions of body shape changes. For example, it can refer to data from similar training sessions performed in the past and adjust predictions based on the degree of effectiveness. In this way, the prediction unit can support users in continuing to train effectively towards their goals and maintain user motivation by visually showing future body shape changes.

[0036] The data collection unit can analyze the user's past exercise history and select the optimal information collection method. For example, the data collection unit selects an appropriate information collection method based on the frequency and intensity of exercises the user has performed in the past. The data collection unit can also prioritize collecting information on exercise types that the user has preferred in the past. Furthermore, the data collection unit can extract effective training methods from the user's exercise history and collect that information. For example, the data collection unit selects an appropriate information collection method based on the frequency and intensity of exercises the user has performed in the past. The data collection unit prioritizes collecting information on exercise types that the user has preferred in the past. The data collection unit extracts effective training methods from the user's exercise history and collects that information. In this way, the optimal information collection method can be selected by analyzing the user's past exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's exercise history data into a generating AI and have the generating AI select the optimal information collection method.

[0037] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can filter appropriate training information according to the user's current lifestyle. The data collection unit can also prioritize the collection of relevant training information based on the user's areas of interest. Furthermore, the data collection unit can filter information to match the user's daily rhythm. For example, the data collection unit filters appropriate training information according to the user's current lifestyle. The data collection unit prioritizes the collection of relevant training information based on the user's areas of interest. The data collection unit filters information to match the user's daily rhythm. This allows for the collection of more relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0038] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location. For example, the data collection unit can prioritize the collection of information on nearby training facilities based on the user's current location. The data collection unit can also collect region-specific training methods based on the user's geographical location. Furthermore, the data collection unit can collect information that suggests the optimal training route, taking into account the user's location. For example, the data collection unit prioritizes the collection of information on nearby training facilities based on the user's current location. The data collection unit collects region-specific training methods based on the user's geographical location. The data collection unit collects information that suggests the optimal training route, taking into account the user's location. This allows for the provision of more appropriate information by prioritizing the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant information.

[0039] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect relevant training information based on exercise information shared by the user on social media. The data collection unit can also collect information on training methods the user is interested in from the user's social media activity. Furthermore, the data collection unit can also collect relevant training information based on information from fitness influencers the user follows. For example, the data collection unit can collect relevant training information based on exercise information shared by the user on social media. The data collection unit collects information on training methods the user is interested in from the user's social media activity. The data collection unit collects relevant training information based on information from fitness influencers the user follows. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0040] The creation unit can adjust the level of detail of a training menu based on the user's goals when creating it. For example, if the user has short-term goals, the creation unit will provide a detailed training menu. The creation unit can also provide an overall training plan if the user has long-term goals. Furthermore, the creation unit can adjust the necessary level of detail according to the user's goals to create the optimal training menu. For example, if the user has short-term goals, the creation unit will provide a detailed training menu. If the user has long-term goals, the creation unit will provide an overall training plan. The creation unit adjusts the necessary level of detail according to the user's goals to create the optimal training menu. This allows for the provision of more appropriate training menus by adjusting the level of detail based on the user's goals. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user goal data into a generating AI and have the generating AI adjust the level of detail of the menu.

[0041] The creation unit can apply different algorithms to the user's body type and exercise history when creating training menus. For example, the creation unit can apply an algorithm to create an appropriate training menu based on the user's body type. It can also apply an algorithm to create an optimal training menu based on the user's exercise history. Furthermore, it can apply an algorithm to create an optimal training menu by comprehensively considering the user's body type and exercise history. For example, the creation unit can apply an algorithm to create an appropriate training menu based on the user's body type. The creation unit can apply an algorithm to create an optimal training menu based on the user's exercise history. The creation unit can apply an algorithm to create an optimal training menu by comprehensively considering the user's body type and exercise history. This allows for the provision of more appropriate training menus by applying different algorithms depending on the user's body type and exercise history. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's body type and exercise history data into a generating AI and have the generating AI apply different algorithms.

[0042] The creation unit can determine the priority of training menus based on when the user submits their exercise history. For example, the creation unit can prioritize creating training menus based on the user's most recently submitted exercise history. The creation unit can also provide the optimal training menu depending on when the user submits their exercise history. Furthermore, the creation unit can create an appropriate training menu considering when the user submits their exercise history. For example, the creation unit can prioritize creating training menus based on the user's most recently submitted exercise history. The creation unit can provide the optimal training menu depending on when the user submits their exercise history. The creation unit can create an appropriate training menu considering when the user submits their exercise history. This allows for the provision of more appropriate training menus by determining the priority of menus based on when the user submits their exercise history. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's exercise history submission data into a generating AI and have the generating AI determine the menu priority.

[0043] The creation unit can adjust the order of training menus based on user relevance when creating them. For example, the creation unit can prioritize providing training menus related to the user's goals. It can also prioritize providing training menus related to the user's exercise history. Furthermore, it can prioritize providing training menus related to the user's body type. For example, the creation unit can prioritize providing training menus related to the user's goals. The creation unit can prioritize providing training menus related to the user's exercise history. The creation unit can prioritize providing training menus related to the user's body type. By adjusting the order of menus based on user relevance, a more appropriate training menu can be provided. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the menu order.

[0044] The support unit can improve the accuracy of form checks by considering the user's behavior history during form checking. For example, the support unit can improve the accuracy of form checks based on the user's past behavior history. The support unit can also analyze the user's behavior history and provide the optimal form check method. Furthermore, the support unit can adjust the criteria for form checks by considering the user's behavior history. For example, the support unit can improve the accuracy of form checks based on the user's past behavior history. The support unit can analyze the user's behavior history and provide the optimal form check method. The support unit can adjust the criteria for form checks by considering the user's behavior history. In this way, the accuracy of form checks can be improved by considering the user's behavior history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user behavior history data into a generating AI and have the generating AI perform the improvement of form check accuracy.

[0045] The support unit can perform form checks while considering the user's body type and exercise history. For example, the support unit can perform an appropriate form check according to the user's body type. The support unit can also perform an optimal form check based on the user's exercise history. Furthermore, the support unit can perform a form check by comprehensively considering the user's body type and exercise history. For example, the support unit can perform an appropriate form check according to the user's body type. The support unit can perform an optimal form check based on the user's exercise history. The support unit can perform a form check by comprehensively considering the user's body type and exercise history. This allows for a more appropriate form check by considering the user's body type and exercise history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's body type and exercise history data into a generating AI and have the generating AI perform the form check.

[0046] The support unit can perform form checks while considering the geographical distribution of users. For example, the support unit can perform appropriate form checks based on the geographical distribution of users. The support unit can also perform form checks while considering the exercise methods specific to the users' regions. Furthermore, the support unit can provide an optimal form check method based on the geographical distribution of users. For example, the support unit can perform appropriate form checks based on the geographical distribution of users. The support unit can perform form checks while considering the exercise methods specific to the users' regions. The support unit can provide an optimal form check method based on the geographical distribution of users. This allows for more appropriate form checks by considering the geographical distribution of users. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the geographical distribution data of users into a generating AI and have the generating AI perform the form check.

[0047] The support unit can improve the accuracy of form checking by referring to the user's relevant literature during form checking. For example, the support unit improves the accuracy of form checking based on the user's relevant literature. The support unit can also refer to the user's relevant literature and provide the optimal form checking method. Furthermore, the support unit can adjust the criteria for form checking by considering the user's relevant literature. For example, the support unit improves the accuracy of form checking based on the user's relevant literature. The support unit refers to the user's relevant literature and provides the optimal form checking method. The support unit adjusts the criteria for form checking by considering the user's relevant literature. In this way, the accuracy of form checking can be improved by referring to the user's relevant literature. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's relevant literature data into a generating AI and have the generating AI perform the improvement of form checking accuracy.

[0048] The motivation maintenance unit can predict the current level of achievement by referring to the user's past achievement data when visualizing the level of achievement. For example, the motivation maintenance unit predicts the current level of achievement based on the user's past achievement data. The motivation maintenance unit can also analyze the user's past achievement data and visualize the optimal level of achievement. Furthermore, the motivation maintenance unit can predict changes in the level of achievement by referring to the user's past achievement data. For example, the motivation maintenance unit predicts the current level of achievement based on the user's past achievement data. The motivation maintenance unit analyzes the user's past achievement data and visualizes the optimal level of achievement. The motivation maintenance unit predicts changes in the level of achievement by referring to the user's past achievement data. In this way, the current level of achievement can be predicted by referring to the user's past achievement data. Some or all of the above processes in the motivation maintenance unit may be performed using AI, for example, or without using AI. For example, the motivation maintenance unit can input the user's past achievement data into a generating AI and have the generating AI perform a prediction of the current level of achievement.

[0049] The motivation maintenance unit can apply different visualization methods to each user's goal when visualizing the degree of achievement. For example, the motivation maintenance unit can visualize the degree of achievement for a user's short-term goal using a progress bar. It can also visualize the degree of achievement for a user's long-term goal using a graph. Furthermore, the motivation maintenance unit can apply the most suitable visualization method according to the user's goal. For example, the motivation maintenance unit can visualize the degree of achievement for a user's short-term goal using a progress bar. For example, the motivation maintenance unit can visualize the degree of achievement for a user's long-term goal using a graph. The motivation maintenance unit applies the most suitable visualization method according to the user's goal. This allows for a more appropriate visualization of the degree of achievement by applying different visualization methods to each user's goal. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input user goal data into a generating AI and have the generating AI execute the application of visualization methods.

[0050] The motivation maintenance unit can analyze changes in achievement based on the timing of the user's exercise history submission when visualizing achievement. For example, the motivation maintenance unit analyzes changes in achievement based on the timing of the user's exercise history submission. The motivation maintenance unit can also visualize changes in achievement according to the timing of the user's exercise history submission. Furthermore, the motivation maintenance unit can predict changes in achievement by considering the timing of the user's exercise history submission. For example, the motivation maintenance unit analyzes changes in achievement based on the timing of the user's exercise history submission. The motivation maintenance unit visualizes changes in achievement according to the timing of the user's exercise history submission. The motivation maintenance unit predicts changes in achievement by considering the timing of the user's exercise history submission. This allows for the provision of more appropriate achievement levels by analyzing changes in achievement based on the timing of the user's exercise history submission. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input user exercise history submission timing data into a generating AI and have the generating AI perform the analysis of changes in achievement.

[0051] The motivation maintenance unit can analyze the degree of achievement by referring to the user's relevant market data when visualizing the degree of achievement. For example, the motivation maintenance unit analyzes the degree of achievement based on the user's relevant market data. The motivation maintenance unit can also visualize the optimal degree of achievement by referring to the user's relevant market data. Furthermore, the motivation maintenance unit can predict the degree of achievement by considering the user's relevant market data. For example, the motivation maintenance unit analyzes the degree of achievement based on the user's relevant market data. The motivation maintenance unit visualizes the optimal degree of achievement by referring to the user's relevant market data. The motivation maintenance unit predicts the degree of achievement by considering the user's relevant market data. In this way, the degree of achievement can be analyzed by referring to the user's relevant market data. Some or all of the above processes in the motivation maintenance unit may be performed using AI, for example, or without using AI. For example, the motivation maintenance unit can input the user's relevant market data into a generating AI and have the generating AI perform the analysis of the degree of achievement.

[0052] The answering unit can adjust the level of detail in its response based on the user's question. For example, if the user's question is specific, the answering unit will provide a detailed answer. If the user's question is general, the answering unit can also provide a concise answer. Furthermore, the answering unit can provide an answer with the optimal level of detail depending on the user's question. For example, if the user's question is specific, the answering unit will provide a detailed answer. If the user's question is general, the answering unit will provide a concise answer. The answering unit can provide an answer with the optimal level of detail depending on the user's question. This allows for the provision of more appropriate answers by adjusting the level of detail in the answer based on the user's question. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input the user's question data into a generating AI and have the generating AI adjust the level of detail in the answer.

[0053] The response unit can apply different response algorithms depending on the user's category when providing a response. For example, if the user's category is related to nutrition, the response unit can apply a nutrition-specific response algorithm. Similarly, if the user's category is related to exercise, the response unit can apply an exercise-specific response algorithm. Furthermore, the response unit can apply the most appropriate response algorithm depending on the user's category. For example, if the user's category is related to nutrition, the response unit can apply a nutrition-specific response algorithm. If the user's category is related to exercise, the response unit can apply an exercise-specific response algorithm. The response unit can apply the most appropriate response algorithm depending on the user's category. This allows for the provision of more appropriate answers by applying different response algorithms depending on the user's category. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user category data into a generating AI and have the generating AI apply different response algorithms.

[0054] The answering unit can determine the priority of answers based on when the user submitted the question. For example, the answering unit will prioritize answers to questions recently submitted by the user. The answering unit can also provide the most appropriate answer depending on when the user submitted the question. Furthermore, the answering unit can determine the priority of answers by considering when the user submitted the question. For example, the answering unit will prioritize answers to questions recently submitted by the user. The answering unit will provide the most appropriate answer depending on when the user submitted the question. The answering unit will determine the priority of answers by considering when the user submitted the question. This allows for the provision of more appropriate answers by determining the priority of answers based on when the user submitted the question. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input user question submission data into a generating AI and have the generating AI perform the determination of answer priority.

[0055] The answering unit can adjust the order of answers based on the relevance of the user's questions when providing answers. For example, the answering unit provides the optimal order of answers based on the relevance of the user's questions. The answering unit can also adjust the order of answers considering the relevance of the user's questions. Furthermore, the answering unit can determine the optimal order of answers according to the relevance of the user's questions. For example, the answering unit provides the optimal order of answers based on the relevance of the user's questions. The answering unit adjusts the order of answers considering the relevance of the user's questions. The answering unit determines the optimal order of answers according to the relevance of the user's questions. This allows for the provision of more appropriate answers by adjusting the order of answers based on the relevance of the user's questions. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input the relevance data of the user's questions into a generating AI and have the generating AI perform the adjustment of the order of answers.

[0056] The prediction unit can improve the accuracy of its predictions by referring to the user's past exercise history when predicting changes in body shape. For example, the prediction unit can improve the accuracy of predicting changes in body shape based on the user's past exercise history. The prediction unit can also analyze the user's exercise history and provide the optimal prediction of changes in body shape. Furthermore, the prediction unit can improve the accuracy of predicting changes in body shape by referring to the user's exercise history. For example, the prediction unit can improve the accuracy of predicting changes in body shape based on the user's past exercise history. The prediction unit can analyze the user's exercise history and provide the optimal prediction of changes in body shape. The prediction unit can improve the accuracy of predicting changes in body shape by referring to the user's exercise history. In this way, the accuracy of predicting changes in body shape can be improved by referring to the user's past exercise history. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's past exercise history data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0057] The prediction unit can apply different prediction methods to each user's goals when predicting changes in body shape. For example, the prediction unit can apply a rapid body shape change prediction method to the user's short-term goals. It can also apply a detailed body shape change prediction method to the user's long-term goals. Furthermore, the prediction unit can apply the optimal prediction method according to the user's goals. For example, the prediction unit can apply a rapid body shape change prediction method to the user's short-term goals. The prediction unit can apply a detailed body shape change prediction method to the user's long-term goals. The prediction unit can apply the optimal prediction method according to the user's goals. By applying different prediction methods to each user's goals, it is possible to provide more appropriate predictions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user goal data into a generating AI and have the generating AI execute the application of different prediction methods.

[0058] The prediction unit can analyze changes in predictions based on the timing of the user's exercise history submission when predicting changes in body shape. For example, the prediction unit analyzes predictions of changes in body shape based on the timing of the user's exercise history submission. The prediction unit can also provide predictions of changes in body shape according to the timing of the user's exercise history submission. Furthermore, the prediction unit can analyze predictions of changes in body shape considering the timing of the user's exercise history submission. For example, the prediction unit analyzes predictions of changes in body shape based on the timing of the user's exercise history submission. The prediction unit provides predictions of changes in body shape according to the timing of the user's exercise history submission. The prediction unit analyzes predictions of changes in body shape considering the timing of the user's exercise history submission. This allows for the provision of more appropriate predictions by analyzing changes in predictions based on the timing of the user's exercise history submission. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's exercise history submission timing data into a generating AI and have the generating AI perform the analysis of changes in predictions.

[0059] The prediction unit can analyze predictions by referring to the user's relevant market data when predicting changes in body shape. For example, the prediction unit analyzes predictions of changes in body shape based on the user's relevant market data. The prediction unit can also refer to the user's relevant market data and provide the optimal prediction of changes in body shape. Furthermore, the prediction unit can analyze predictions of changes in body shape by considering the user's relevant market data. For example, the prediction unit analyzes predictions of changes in body shape based on the user's relevant market data. The prediction unit provides the optimal prediction of changes in body shape by referring to the user's relevant market data. The prediction unit analyzes predictions of changes in body shape by considering the user's relevant market data. In this way, predictions of changes in body shape can be analyzed by referring to the user's relevant market data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's relevant market data into a generating AI and have the generating AI perform the prediction analysis.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The AI ​​personal trainer system can also collect user sleep data and incorporate it into training menus. The data collection unit analyzes the user's sleep patterns and quality, and the creation unit adjusts the training menu based on this. For example, if the user is not getting enough sleep, a lighter training menu can be suggested. Conversely, if the user's sleep quality improves, a more intense training menu can be provided. Furthermore, the motivation maintenance unit can send motivational messages at appropriate times based on the user's sleep data. This allows the system to provide an optimal training menu tailored to the user's sleep situation, thereby improving their overall health.

[0062] The AI ​​personal trainer system can collect user meal data and incorporate it into training menus. The data collection unit records the content and calories of meals consumed by the user, and the creation unit adjusts the training menu based on this data. For example, if a user has consumed a high-calorie meal, the system can suggest a training menu that promotes calorie burning. Furthermore, if the user's nutritional balance is unbalanced, the system can provide a training menu that encourages a more balanced diet. In addition, the response unit can provide nutritional advice based on the user's meal data. This allows the system to provide an optimal training menu tailored to the user's eating habits and support the maintenance of a healthy lifestyle.

[0063] The AI ​​personal trainer system can collect data on a user's social activities and incorporate it into their training menu. The data collection unit records the frequency and content of the user's social activities, and the creation unit adjusts the training menu based on this data. For example, if a user engages in many social activities, the system can suggest a short and effective training menu. Conversely, if a user engages in fewer social activities, the system can provide training menus that encourage social activity. Furthermore, the motivation maintenance unit can send messages encouraging participation in social events based on the user's social activity data. This allows the system to provide optimal training menus tailored to the user's social situation, supporting the maintenance of social well-being.

[0064] The AI ​​personal trainer system can provide variations of training menus based on the user's exercise history. The creation unit analyzes the types and frequency of exercises the user has performed in the past and proposes new training menus based on this. For example, by incorporating exercises the user has not done before, it can prevent training from becoming monotonous. Also, if the user is tired of a particular exercise, it can suggest different types of exercises. Furthermore, the motivation maintenance unit can suggest new challenges based on the user's exercise history. This allows for the provision of a wide variety of training menus tailored to the user's exercise history, thereby maintaining continuous motivation.

[0065] The AI ​​personal trainer system can visualize the progress of a training program based on the user's exercise history. The motivation maintenance unit analyzes data from the user's past exercises and displays progress in graphs and charts based on this analysis. For example, it can enhance a user's sense of accomplishment by visually displaying goals achieved and calories burned. It can also display the user's progress towards their set goals in real time. Furthermore, the motivation maintenance unit can provide advice on setting the next goal based on the user's exercise history. This allows for the visualization of progress based on the user's exercise history and helps maintain sustained motivation.

[0066] The AI ​​personal trainer system can adjust the intensity of training menus based on the user's exercise history. The creation unit analyzes the intensity and frequency of exercises the user has performed in the past and adjusts the intensity of the training menu accordingly. For example, if the user has performed high-intensity exercises in the past, it can provide a training menu of similar intensity. Conversely, if the user has performed low-intensity exercises, it can provide a training menu that gradually increases in intensity. Furthermore, the motivation maintenance unit can suggest a training menu of appropriate intensity based on the user's exercise history. In this way, it can provide the optimal training menu intensity based on the user's exercise history and support effective training.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects information about the user's body type, goals, lifestyle, and past exercise history. The data collection unit collects information based on data entered by the user, data from wearable devices, and information from social media activity. For example, it collects data such as weight, height, target weight, heart rate, steps taken, calories burned, exercise-related posts, and training methods the user is interested in. Step 2: The creation unit analyzes the information collected by the collection unit and creates an individually appropriate training menu. The creation unit uses AI to analyze the collected data, adjusts the training menu based on the user's goals, and applies different algorithms depending on the user's body type and exercise history. Step 3: The support unit uses the smartphone's camera to perform real-time form checks and support accurate movement. The support unit uses AI to analyze the video captured by the smartphone's camera and provides real-time advice on areas for form correction. It also improves the accuracy of the check by considering the user's movement history, body type, and exercise history. Step 4: The motivation maintenance unit provides visualization of achievement and challenge functions. The motivation maintenance unit visualizes the progress towards the goals set by the user and allows users to set new goals through the challenge function. It also estimates the user's emotions, adjusts the display method of achievement based on the estimated emotions, and predicts the current achievement level by referring to past achievement data. Step 5: The answering unit provides 24 / 7 answers to questions about nutrition and exercise. The answering unit uses AI to provide appropriate answers to user questions, estimates the user's emotions and adjusts the wording of the answers accordingly, and adjusts the level of detail in the answers based on the content of the questions. Step 6: The prediction unit predicts and visualizes future body shape changes based on the current exercise status and goals. The prediction unit uses AI to predict future body shape changes based on the current exercise status and goals, estimates the user's emotions to adjust the prediction method, and improves the accuracy of the prediction by referring to past exercise history.

[0069] (Example of form 2) The AI ​​personal trainer system according to an embodiment of the present invention is a smartphone application that utilizes the latest artificial intelligence technology to provide a fitness program optimized for each individual user. This AI personal trainer system analyzes the user's body type, goals, lifestyle, past exercise history, etc., in detail, and the AI ​​creates an individually optimized training menu. Furthermore, it uses the smartphone's camera to perform real-time form checks and support accurate movements. It also incorporates gamification elements, supporting user motivation through the visualization of achievement and challenge functions. The AI ​​carefully answers questions about nutrition and exercise 24 hours a day, providing an experience as if a personal trainer is always by the user's side. In addition, it is equipped with a function that predicts and visualizes future body shape changes based on the current exercise status and goals. This contributes to the long-term motivation maintenance of the user. With an affordable monthly subscription setting, users can receive high-quality personal training services at a much lower cost than hiring a personal trainer. The AI ​​personal trainer system is tailored to the busy lifestyles of modern people and supports the establishment of a continuous exercise habit. For example, if a user desires strength training, the AI ​​will propose an optimal training menu based on past exercise history and current body type. Next, the system uses the smartphone's camera to check form in real time, supporting accurate movement. For example, when a user performs squats, the AI ​​analyzes the video captured by the smartphone's camera and provides real-time advice on how to correct their form. Furthermore, gamification elements are incorporated to support user motivation through the visualization of progress and challenge functions. For example, the system visualizes the progress towards the user's set goals and allows users to set new goals through the challenge function, thus maintaining continuous motivation. In addition, the AI ​​provides thorough answers to questions about nutrition and exercise 24 hours a day, providing an experience as if a personal trainer were always by the user's side. For example, if a user asks a question about diet, the AI ​​provides appropriate nutritional advice. The system also includes a function that predicts and visualizes future body shape changes based on current exercise status and goals.For example, when a user inputs their current exercise status and goals, the AI ​​predicts and visualizes future body shape changes, contributing to long-term motivation maintenance. This allows the AI ​​personal trainer system to leverage the latest artificial intelligence technology to provide a fitness program optimized for each individual user, catering to the busy lifestyles of modern people and supporting the establishment of consistent exercise habits. The AI ​​personal trainer system collects information such as the user's body shape, goals, lifestyle, and past exercise history to create individually optimized training menus, support precise movements, visualize progress and offer challenge functions, provide 24 / 7 answers to questions about nutrition and exercise, and predict and visualize future body shape changes.

[0070] The AI ​​personal trainer system according to this embodiment comprises a data collection unit, a data creation unit, a support unit, a motivation maintenance unit, a response unit, and a prediction unit. The data collection unit collects information about the user's body type, goals, lifestyle, and past exercise history. The data collection unit collects information based on data entered by the user, for example. The data collection unit can also acquire data from wearable devices. Furthermore, the data collection unit can also collect information from the user's social media activity. For example, the data collection unit collects information based on data such as weight, height, and target weight entered by the user. The data collection unit collects information based on data such as heart rate, steps taken, and calories burned acquired from wearable devices. The data collection unit collects information from the user's social media activity, such as posts about exercise and training methods the user is interested in. The data creation unit analyzes the information collected by the data collection unit and creates an individually appropriate training menu. The data creation unit analyzes the collected data using AI, for example, and creates an optimal training menu. Furthermore, the data creation unit can adjust the training menu based on the user's goals. Furthermore, the data creation unit can apply different algorithms depending on the user's body type and exercise history. For example, the creation unit uses AI to analyze the user's body type and exercise history to create an optimal training menu. Based on the user's goals, the creation unit provides a detailed training menu for short-term goals. The creation unit applies different algorithms depending on the user's body type and exercise history to create an optimal training menu. The support unit uses the smartphone camera to perform real-time form checks and support accurate movement. For example, the support unit uses AI to analyze videos taken with the smartphone camera and provides real-time advice on areas for form correction. The support unit can also improve the accuracy of the checks by considering the user's movement history. Furthermore, the support unit can also perform checks considering the user's body type and exercise history. For example, the support unit uses AI to analyze videos taken with the smartphone camera and provides real-time advice on areas for form correction. The support unit improves the accuracy of form checks based on the user's movement history.The support unit performs appropriate form checks, taking into account the user's body type and exercise history. The motivation maintenance unit provides visualization of achievement and challenge functions. For example, the motivation maintenance unit maintains continuous motivation by visualizing the progress towards goals set by the user and setting new goals through the challenge function. The motivation maintenance unit can also estimate the user's emotions and adjust the display method of achievement based on the estimated emotions. Furthermore, the motivation maintenance unit can predict the current achievement level by referring to the user's past achievement data. For example, the motivation maintenance unit visualizes the progress towards goals set by the user and sets new goals through the challenge function. The motivation maintenance unit estimates the user's emotions and adjusts the display method of achievement based on the estimated emotions. The motivation maintenance unit predicts the current achievement level based on the user's past achievement data. The answer unit answers questions about nutrition and exercise 24 hours a day. The answer unit provides appropriate answers to user questions, for example, using AI. The answer unit can also estimate the user's emotions and adjust the expression of answers based on the estimated emotions. Furthermore, the answering unit can adjust the level of detail in its answers based on the user's questions. For example, the answering unit uses AI to provide appropriate answers to the user's questions. The answering unit estimates the user's emotions and adjusts the way it expresses its answers based on those emotions. The answering unit provides answers with the optimal level of detail depending on the user's questions. The prediction unit predicts and visualizes future body shape changes based on the current exercise status and goals. For example, the prediction unit uses AI to predict future body shape changes based on the current exercise status and goals. The prediction unit can also estimate the user's emotions and adjust the prediction method for body shape changes based on those emotions. Furthermore, the prediction unit can improve the accuracy of its predictions by referring to the user's past exercise history. For example, the prediction unit uses AI to predict future body shape changes based on the current exercise status and goals. The prediction unit estimates the user's emotions and adjusts the prediction method for body shape changes based on those emotions. The prediction unit improves the accuracy of its body shape change predictions based on the user's past exercise history.As a result, the AI ​​personal trainer system according to this embodiment can collect information such as the user's body type, goals, lifestyle, and past exercise history, create an individually optimized training menu, support precise movements, provide visualization of progress and challenge functions, answer questions about nutrition and exercise 24 hours a day, and predict and visualize future changes in body shape.

[0071] The data collection unit collects information about the user's body type, goals, lifestyle, and past exercise history. For example, the unit collects information based on data entered by the user. It can also acquire data from wearable devices. Furthermore, the unit can collect information from the user's social media activity. For instance, the unit collects information based on data such as weight, height, and target weight entered by the user. It also collects information based on data such as heart rate, steps, and calories burned obtained from wearable devices. From the user's social media activity, the unit collects information such as exercise-related posts and training methods the user is interested in. Specifically, by having the user input their daily weight and diet into the app, the unit accumulates this data to understand the user's health status and progress. In addition to heart rate, steps, and calories burned, wearable devices can also acquire detailed data such as sleep patterns and stress levels. This allows for a comprehensive understanding of the user's lifestyle and health status. Furthermore, by collecting information from social media, it is possible to understand what kind of exercise and training the user is interested in and what motivates them. For example, if a user is participating in a specific fitness challenge, the training menu can be adjusted based on that information. This allows the data collection unit to centrally manage diverse user data and build a foundation for providing individually optimized training menus.

[0072] The creation unit analyzes the information collected by the collection unit and creates individually appropriate training menus. For example, the creation unit uses AI to analyze the collected data and create the optimal training menu. The creation unit can also adjust the training menu based on the user's goals. Furthermore, the creation unit can apply different algorithms depending on the user's body type and exercise history. For example, the creation unit uses AI to analyze the user's body type and exercise history and create the optimal training menu. Based on the user's goals, the creation unit provides a detailed training menu for short-term goals. The creation unit applies different algorithms depending on the user's body type and exercise history to create the optimal training menu. Specifically, the AI ​​analyzes the user's body type data and exercise history and creates a menu that considers the balance of strength training, aerobic exercise, stretching, etc. For example, for strength training, it suggests appropriate weight, repetitions, and sets based on the user's muscle mass and body fat percentage. For aerobic exercise, it sets the optimal exercise intensity and time based on heart rate and calories burned. For stretching, it provides a safe and effective stretching menu considering the user's flexibility and past injury history. Furthermore, the creation unit can continuously adjust the training menu based on user feedback. For example, if a user finds a particular exercise too difficult, the intensity of that exercise can be adjusted or alternative exercises can be suggested. This allows the creation unit to provide an optimal training menu tailored to the user's individual needs and goals, supporting effective training.

[0073] The support unit uses the smartphone's camera to perform real-time form checks and support accurate movement. For example, the support unit uses AI to analyze videos taken with the smartphone's camera and provide real-time advice on areas for form correction. The support unit can also improve the accuracy of the checks by considering the user's movement history. Furthermore, the support unit can perform checks considering the user's body type and exercise history. For example, the support unit uses AI to analyze videos taken with the smartphone's camera and provides real-time advice on areas for form correction. The support unit improves the accuracy of form checks based on the user's movement history. The support unit performs appropriate form checks considering the user's body type and exercise history. Specifically, the AI ​​analyzes the user's movements and evaluates joint angles, movement speed, balance, etc. For example, during squats, it checks knee position, back angle, foot width, etc., to determine if the correct form is being used. If there are problems with the form, the AI ​​points out specific areas for correction and advises how to correct them. Furthermore, the support unit can improve the accuracy of form checks based on the user's past movement data. For example, data from past performances of the same exercise can be referenced to evaluate areas for improvement and progress. Furthermore, by considering the user's body type and exercise history, personalized form checks can be provided. This allows the support team to assist users in training with correct form and continuing their exercise effectively and safely.

[0074] The motivation maintenance unit provides features for visualizing progress and setting challenges. For example, it helps users maintain continuous motivation by visualizing their progress towards goals they have set and by allowing them to set new goals through the challenge function. The motivation maintenance unit can also estimate the user's emotions and adjust the display method of progress based on those emotions. Furthermore, it can predict the current progress by referring to the user's past progress data. Specifically, progress visualization uses graphs and charts to visually display the user's progress. For example, it can display weekly and monthly exercise, calories burned, and weight changes in graphs, allowing users to grasp their progress at a glance. The challenge function helps users maintain motivation by setting new goals and striving towards them. For example, the system offers challenges such as achieving a specific amount of exercise within a certain period or performing a specific exercise a certain number of times. Furthermore, to estimate the user's emotions, the AI ​​analyzes user input data and behavioral patterns to estimate their current emotional state. For instance, if the frequency or intensity of exercise decreases, the system may determine that the user is losing motivation and suggest encouraging messages or new challenges. Additionally, by predicting current progress based on past achievement data, the system can assess whether the user is on track to achieve their goals. This allows the motivation maintenance unit to provide support for users to continue training consistently and maintain their motivation towards achieving their goals.

[0075] The answering unit provides 24 / 7 answers to questions about nutrition and exercise. For example, the answering unit uses AI to provide appropriate answers to user questions. It can also estimate the user's emotions and adjust the wording of its answers based on those emotions. Furthermore, it can adjust the level of detail in its answers based on the content of the user's questions. Specifically, the AI ​​analyzes the user's questions using natural language processing technology and generates appropriate answers. For example, if a user asks, "What kind of diet is good for building muscle?", the AI ​​will provide a detailed answer including the importance of protein, specific foods, and timing of meals. In addition, to estimate the user's emotions, the AI ​​analyzes the context and wording of the question to determine whether the user is troubled or interested. For example, if a user asks, "I haven't been exercising well lately," the AI ​​can offer words of encouragement and motivational advice. Furthermore, by adjusting the level of detail in the answer according to the question, it can accurately provide the information the user is looking for. For instance, it can provide basic information to beginner users and more specialized information to experienced users. As a result, the answering system can provide appropriate answers tailored to the user's needs 24 hours a day, resolving users' doubts and anxieties.

[0076] The prediction unit predicts and visualizes future body shape changes based on the user's current exercise status and goals. For example, the prediction unit uses AI to predict future body shape changes based on the current exercise status and goals. The prediction unit can also estimate the user's emotions and adjust the prediction method based on those emotions. Furthermore, the prediction unit can improve the accuracy of predictions by referring to the user's past exercise history. Specifically, the AI ​​simulates future body shape changes based on the user's current exercise data, dietary data, and target body shape. For example, it predicts what the user's body shape will be like in one month or three months if their current exercise level and diet are maintained, and visualizes this in graphs and 3D models. Additionally, to estimate the user's emotions, the AI ​​analyzes the user's input data and behavioral patterns to determine their current motivation and stress level. For example, if a user feels anxious about continuing to exercise, the prediction unit takes that emotion into account and provides realistic goal setting and encouraging messages. Furthermore, by improving the accuracy of predictions based on past exercise history, it can make more accurate predictions of body shape changes. For example, it can refer to data from similar training sessions performed in the past and adjust predictions based on the degree of effectiveness. In this way, the prediction unit can support users in continuing to train effectively towards their goals and maintain user motivation by visually showing future body shape changes.

[0077] The data collection unit can analyze the user's emotions and adjust the timing of information collection based on the analyzed emotions. For example, if the user is feeling stressed, the data collection unit will collect information during a relaxed period. The data collection unit can also start collecting information immediately if the user is highly motivated. Furthermore, if the user is tired, the data collection unit can collect information after they have rested. For example, if the user is feeling stressed, the data collection unit will collect information during a relaxed period. If the user is highly motivated, the data collection unit will start collecting information immediately. If the user is tired, the data collection unit will collect information after they have rested. By adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of information collection.

[0078] The data collection unit can analyze the user's past exercise history and select the optimal information collection method. For example, the data collection unit selects an appropriate information collection method based on the frequency and intensity of exercises the user has performed in the past. The data collection unit can also prioritize collecting information on exercise types that the user has preferred in the past. Furthermore, the data collection unit can extract effective training methods from the user's exercise history and collect that information. For example, the data collection unit selects an appropriate information collection method based on the frequency and intensity of exercises the user has performed in the past. The data collection unit prioritizes collecting information on exercise types that the user has preferred in the past. The data collection unit extracts effective training methods from the user's exercise history and collects that information. In this way, the optimal information collection method can be selected by analyzing the user's past exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's exercise history data into a generating AI and have the generating AI select the optimal information collection method.

[0079] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can filter appropriate training information according to the user's current lifestyle. The data collection unit can also prioritize the collection of relevant training information based on the user's areas of interest. Furthermore, the data collection unit can filter information to match the user's daily rhythm. For example, the data collection unit filters appropriate training information according to the user's current lifestyle. The data collection unit prioritizes the collection of relevant training information based on the user's areas of interest. The data collection unit filters information to match the user's daily rhythm. This allows for the collection of more relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0080] The data collection unit can analyze the user's emotions and determine the priority of information to collect based on the analyzed emotions. For example, if the user is highly motivated, the data collection unit will prioritize collecting information on challenging training. If the user is relaxed, the data collection unit can also prioritize collecting training information that has a relaxing effect. Furthermore, if the user is stressed, the data collection unit can also prioritize collecting information that helps reduce stress. For example, if the user is highly motivated, the data collection unit will prioritize collecting information on challenging training. If the user is relaxed, the data collection unit will prioritize collecting training information that has a relaxing effect. If the user is stressed, the data collection unit will prioritize collecting information that helps reduce stress. By determining the priority of information to collect based on the user's emotions, more appropriate information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the information.

[0081] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location. For example, the data collection unit can prioritize the collection of information on nearby training facilities based on the user's current location. The data collection unit can also collect region-specific training methods based on the user's geographical location. Furthermore, the data collection unit can collect information that suggests the optimal training route, taking into account the user's location. For example, the data collection unit prioritizes the collection of information on nearby training facilities based on the user's current location. The data collection unit collects region-specific training methods based on the user's geographical location. The data collection unit collects information that suggests the optimal training route, taking into account the user's location. This allows for the provision of more appropriate information by prioritizing the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI collect highly relevant information.

[0082] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect relevant training information based on exercise information shared by the user on social media. The data collection unit can also collect information on training methods the user is interested in from the user's social media activity. Furthermore, the data collection unit can also collect relevant training information based on information from fitness influencers the user follows. For example, the data collection unit can collect relevant training information based on exercise information shared by the user on social media. The data collection unit collects information on training methods the user is interested in from the user's social media activity. The data collection unit collects relevant training information based on information from fitness influencers the user follows. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0083] The creation unit can analyze the user's emotions and adjust the way the training menu is presented based on the analyzed emotions. For example, if the user is relaxed, the creation unit will present the training menu in a soft tone. If the user is highly motivated, the creation unit can also present the training menu in a strong tone. Furthermore, if the user is stressed, the creation unit can also present the training menu in a calm tone. For example, if the user is relaxed, the creation unit will present the training menu in a soft tone. If the user is highly motivated, the creation unit will present the training menu in a strong tone. If the user is stressed, the creation unit will present the training menu in a calm tone. This allows for the provision of more appropriate training menus by adjusting the way the training menu is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user emotion data into the generating AI and have the generating AI adjust how the training menu is presented.

[0084] The creation unit can adjust the level of detail of a training menu based on the user's goals when creating it. For example, if the user has short-term goals, the creation unit will provide a detailed training menu. The creation unit can also provide an overall training plan if the user has long-term goals. Furthermore, the creation unit can adjust the necessary level of detail according to the user's goals to create the optimal training menu. For example, if the user has short-term goals, the creation unit will provide a detailed training menu. If the user has long-term goals, the creation unit will provide an overall training plan. The creation unit adjusts the necessary level of detail according to the user's goals to create the optimal training menu. This allows for the provision of more appropriate training menus by adjusting the level of detail based on the user's goals. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user goal data into a generating AI and have the generating AI adjust the level of detail of the menu.

[0085] The creation unit can apply different algorithms to the user's body type and exercise history when creating training menus. For example, the creation unit can apply an algorithm to create an appropriate training menu based on the user's body type. It can also apply an algorithm to create an optimal training menu based on the user's exercise history. Furthermore, it can apply an algorithm to create an optimal training menu by comprehensively considering the user's body type and exercise history. For example, the creation unit can apply an algorithm to create an appropriate training menu based on the user's body type. The creation unit can apply an algorithm to create an optimal training menu based on the user's exercise history. The creation unit can apply an algorithm to create an optimal training menu by comprehensively considering the user's body type and exercise history. This allows for the provision of more appropriate training menus by applying different algorithms depending on the user's body type and exercise history. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's body type and exercise history data into a generating AI and have the generating AI apply different algorithms.

[0086] The creation unit can analyze the user's emotions and adjust the length of the training menu based on the analyzed emotions. For example, if the user is tired, the creation unit can provide a shorter training menu. It can also provide a longer training menu if the user is highly motivated. Furthermore, it can provide a training menu of appropriate length if the user is relaxed. This allows for the provision of more appropriate training menus by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the creation unit may be performed using AI, or not. For example, the creation unit can input user emotion data into the generating AI and have the generating AI adjust the length of the training menu.

[0087] The creation unit can determine the priority of training menus based on when the user submits their exercise history. For example, the creation unit can prioritize creating training menus based on the user's most recently submitted exercise history. The creation unit can also provide the optimal training menu depending on when the user submits their exercise history. Furthermore, the creation unit can create an appropriate training menu considering when the user submits their exercise history. For example, the creation unit can prioritize creating training menus based on the user's most recently submitted exercise history. The creation unit can provide the optimal training menu depending on when the user submits their exercise history. The creation unit can create an appropriate training menu considering when the user submits their exercise history. This allows for the provision of more appropriate training menus by determining the priority of menus based on when the user submits their exercise history. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's exercise history submission data into a generating AI and have the generating AI determine the menu priority.

[0088] The creation unit can adjust the order of training menus based on user relevance when creating them. For example, the creation unit can prioritize providing training menus related to the user's goals. It can also prioritize providing training menus related to the user's exercise history. Furthermore, it can prioritize providing training menus related to the user's body type. For example, the creation unit can prioritize providing training menus related to the user's goals. The creation unit can prioritize providing training menus related to the user's exercise history. The creation unit can prioritize providing training menus related to the user's body type. By adjusting the order of menus based on user relevance, a more appropriate training menu can be provided. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the menu order.

[0089] The support unit can analyze the user's emotions and adjust the form check criteria based on the analyzed emotions. For example, if the user is nervous, the support unit can perform a form check with lenient criteria. Conversely, if the user is relaxed, the support unit can perform a form check with strict criteria. Furthermore, if the user is stressed, the support unit can perform a form check with flexible criteria. For example, if the user is nervous, the support unit can perform a form check with lenient criteria. If the user is relaxed, the support unit can perform a form check with strict criteria. If the user is stressed, the support unit can perform a form check with flexible criteria. This allows for more appropriate form checks by adjusting the form check criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​adjust the criteria for form checking.

[0090] The support unit can improve the accuracy of form checks by considering the user's behavior history during form checking. For example, the support unit can improve the accuracy of form checks based on the user's past behavior history. The support unit can also analyze the user's behavior history and provide the optimal form check method. Furthermore, the support unit can adjust the criteria for form checks by considering the user's behavior history. For example, the support unit can improve the accuracy of form checks based on the user's past behavior history. The support unit can analyze the user's behavior history and provide the optimal form check method. The support unit can adjust the criteria for form checks by considering the user's behavior history. In this way, the accuracy of form checks can be improved by considering the user's behavior history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user behavior history data into a generating AI and have the generating AI perform the improvement of form check accuracy.

[0091] The support unit can perform form checks while considering the user's body type and exercise history. For example, the support unit can perform an appropriate form check according to the user's body type. The support unit can also perform an optimal form check based on the user's exercise history. Furthermore, the support unit can perform a form check by comprehensively considering the user's body type and exercise history. For example, the support unit can perform an appropriate form check according to the user's body type. The support unit can perform an optimal form check based on the user's exercise history. The support unit can perform a form check by comprehensively considering the user's body type and exercise history. This allows for a more appropriate form check by considering the user's body type and exercise history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's body type and exercise history data into a generating AI and have the generating AI perform the form check.

[0092] The support unit can analyze the user's emotions and adjust the order in which the form check results are displayed based on the analyzed emotions. For example, if the user is nervous, the support unit can display important results first. If the user is relaxed, the support unit can also display detailed results sequentially. Furthermore, if the user is stressed, the support unit can also display concise results first. For example, if the user is nervous, the support unit can display important results first. If the user is relaxed, the support unit can display detailed results sequentially. If the user is stressed, the support unit can display concise results first. This allows for more appropriate results to be provided by adjusting the order in which the form check results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the form check results.

[0093] The support unit can perform form checks while considering the geographical distribution of users. For example, the support unit can perform appropriate form checks based on the geographical distribution of users. The support unit can also perform form checks while considering the exercise methods specific to the users' regions. Furthermore, the support unit can provide an optimal form check method based on the geographical distribution of users. For example, the support unit can perform appropriate form checks based on the geographical distribution of users. The support unit can perform form checks while considering the exercise methods specific to the users' regions. The support unit can provide an optimal form check method based on the geographical distribution of users. This allows for more appropriate form checks by considering the geographical distribution of users. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the geographical distribution data of users into a generating AI and have the generating AI perform the form check.

[0094] The support unit can improve the accuracy of form checking by referring to the user's relevant literature during form checking. For example, the support unit improves the accuracy of form checking based on the user's relevant literature. The support unit can also refer to the user's relevant literature and provide the optimal form checking method. Furthermore, the support unit can adjust the criteria for form checking by considering the user's relevant literature. For example, the support unit improves the accuracy of form checking based on the user's relevant literature. The support unit refers to the user's relevant literature and provides the optimal form checking method. The support unit adjusts the criteria for form checking by considering the user's relevant literature. In this way, the accuracy of form checking can be improved by referring to the user's relevant literature. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's relevant literature data into a generating AI and have the generating AI perform the improvement of form checking accuracy.

[0095] The motivation maintenance unit can analyze the user's emotions and adjust how achievement levels are displayed based on the analyzed emotions. For example, if the user is highly motivated, the motivation maintenance unit can display detailed achievement levels. It can also display simple achievement levels if the user is relaxed. Furthermore, if the user is stressed, the motivation maintenance unit can highlight positive achievement levels. For example, if the user is highly motivated, the motivation maintenance unit can display detailed achievement levels. If the user is relaxed, the motivation maintenance unit can display simple achievement levels. If the user is stressed, the motivation maintenance unit can highlight positive achievement levels. This allows for the display of more appropriate achievement levels by adjusting how achievement levels are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input user emotional data into a generating AI and have the AI ​​adjust how the achievement level is displayed.

[0096] The motivation maintenance unit can predict the current level of achievement by referring to the user's past achievement data when visualizing the level of achievement. For example, the motivation maintenance unit predicts the current level of achievement based on the user's past achievement data. The motivation maintenance unit can also analyze the user's past achievement data and visualize the optimal level of achievement. Furthermore, the motivation maintenance unit can predict changes in the level of achievement by referring to the user's past achievement data. For example, the motivation maintenance unit predicts the current level of achievement based on the user's past achievement data. The motivation maintenance unit analyzes the user's past achievement data and visualizes the optimal level of achievement. The motivation maintenance unit predicts changes in the level of achievement by referring to the user's past achievement data. In this way, the current level of achievement can be predicted by referring to the user's past achievement data. Some or all of the above processes in the motivation maintenance unit may be performed using AI, for example, or without using AI. For example, the motivation maintenance unit can input the user's past achievement data into a generating AI and have the generating AI perform a prediction of the current level of achievement.

[0097] The motivation maintenance unit can apply different visualization methods to each user's goal when visualizing the degree of achievement. For example, the motivation maintenance unit can visualize the degree of achievement for a user's short-term goal using a progress bar. It can also visualize the degree of achievement for a user's long-term goal using a graph. Furthermore, the motivation maintenance unit can apply the most suitable visualization method according to the user's goal. For example, the motivation maintenance unit can visualize the degree of achievement for a user's short-term goal using a progress bar. For example, the motivation maintenance unit can visualize the degree of achievement for a user's long-term goal using a graph. The motivation maintenance unit applies the most suitable visualization method according to the user's goal. This allows for a more appropriate visualization of the degree of achievement by applying different visualization methods to each user's goal. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input user goal data into a generating AI and have the generating AI execute the application of visualization methods.

[0098] The motivation maintenance unit can analyze the user's emotions and adjust the importance of achievement based on the analyzed emotions. For example, if the user is highly motivated, the motivation maintenance unit can set the importance of achievement to a high level. Conversely, if the user is relaxed, the motivation maintenance unit can set the importance of achievement to a low level. Furthermore, if the user is stressed, the motivation maintenance unit can set the importance of achievement to a medium level. For example, if the user is highly motivated, the motivation maintenance unit can set the importance of achievement to a high level. If the user is relaxed, the motivation maintenance unit can set the importance of achievement to a low level. If the user is stressed, the motivation maintenance unit can set the importance of achievement to a medium level. This allows for the provision of more appropriate achievement levels by adjusting the importance of achievement based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input user emotional data into a generating AI and have the AI ​​adjust the importance of achievement levels.

[0099] The motivation maintenance unit can analyze changes in achievement based on the timing of the user's exercise history submission when visualizing achievement. For example, the motivation maintenance unit analyzes changes in achievement based on the timing of the user's exercise history submission. The motivation maintenance unit can also visualize changes in achievement according to the timing of the user's exercise history submission. Furthermore, the motivation maintenance unit can predict changes in achievement by considering the timing of the user's exercise history submission. For example, the motivation maintenance unit analyzes changes in achievement based on the timing of the user's exercise history submission. The motivation maintenance unit visualizes changes in achievement according to the timing of the user's exercise history submission. The motivation maintenance unit predicts changes in achievement by considering the timing of the user's exercise history submission. This allows for the provision of more appropriate achievement levels by analyzing changes in achievement based on the timing of the user's exercise history submission. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input user exercise history submission timing data into a generating AI and have the generating AI perform the analysis of changes in achievement.

[0100] The motivation maintenance unit can analyze the degree of achievement by referring to the user's relevant market data when visualizing the degree of achievement. For example, the motivation maintenance unit analyzes the degree of achievement based on the user's relevant market data. The motivation maintenance unit can also visualize the optimal degree of achievement by referring to the user's relevant market data. Furthermore, the motivation maintenance unit can predict the degree of achievement by considering the user's relevant market data. For example, the motivation maintenance unit analyzes the degree of achievement based on the user's relevant market data. The motivation maintenance unit visualizes the optimal degree of achievement by referring to the user's relevant market data. The motivation maintenance unit predicts the degree of achievement by considering the user's relevant market data. In this way, the degree of achievement can be analyzed by referring to the user's relevant market data. Some or all of the above processes in the motivation maintenance unit may be performed using AI, for example, or without using AI. For example, the motivation maintenance unit can input the user's relevant market data into a generating AI and have the generating AI perform the analysis of the degree of achievement.

[0101] The response unit can analyze the user's emotions and adjust the way it expresses its response based on those emotions. For example, if the user is relaxed, the response unit will provide a soft tone of response. If the user is highly motivated, the response unit can provide a strong tone of response. Furthermore, if the user is stressed, the response unit can provide a calm tone of response. For example, if the user is relaxed, the response unit will provide a soft tone of response. If the user is highly motivated, the response unit will provide a strong tone of response. If the user is stressed, the response unit will provide a calm tone of response. This allows for the provision of more appropriate responses by adjusting the way the response is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user emotion data into a generating AI and have the AI ​​adjust the way the response is expressed.

[0102] The answering unit can adjust the level of detail in its response based on the user's question. For example, if the user's question is specific, the answering unit will provide a detailed answer. If the user's question is general, the answering unit can also provide a concise answer. Furthermore, the answering unit can provide an answer with the optimal level of detail depending on the user's question. For example, if the user's question is specific, the answering unit will provide a detailed answer. If the user's question is general, the answering unit will provide a concise answer. The answering unit can provide an answer with the optimal level of detail depending on the user's question. This allows for the provision of more appropriate answers by adjusting the level of detail in the answer based on the user's question. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input the user's question data into a generating AI and have the generating AI adjust the level of detail in the answer.

[0103] The response unit can apply different response algorithms depending on the user's category when providing a response. For example, if the user's category is related to nutrition, the response unit can apply a nutrition-specific response algorithm. Similarly, if the user's category is related to exercise, the response unit can apply an exercise-specific response algorithm. Furthermore, the response unit can apply the most appropriate response algorithm depending on the user's category. For example, if the user's category is related to nutrition, the response unit can apply a nutrition-specific response algorithm. If the user's category is related to exercise, the response unit can apply an exercise-specific response algorithm. The response unit can apply the most appropriate response algorithm depending on the user's category. This allows for the provision of more appropriate answers by applying different response algorithms depending on the user's category. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user category data into a generating AI and have the generating AI apply different response algorithms.

[0104] The response unit can analyze the user's emotions and adjust the length of the response based on the analyzed emotions. For example, if the user is in a hurry, the response unit will provide a short, to-the-point response. If the user is relaxed, the response unit can also provide a detailed response. Furthermore, if the user is stressed, the response unit can also provide a concise response. For example, if the user is in a hurry, the response unit will provide a short, to-the-point response. If the user is relaxed, the response unit will provide a detailed response. If the user is stressed, the response unit will provide a concise response. This allows for the provision of more appropriate responses by adjusting the length of the response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI adjust the length of the response.

[0105] The answering unit can determine the priority of answers based on when the user submitted the question. For example, the answering unit will prioritize answers to questions recently submitted by the user. The answering unit can also provide the most appropriate answer depending on when the user submitted the question. Furthermore, the answering unit can determine the priority of answers by considering when the user submitted the question. For example, the answering unit will prioritize answers to questions recently submitted by the user. The answering unit will provide the most appropriate answer depending on when the user submitted the question. The answering unit will determine the priority of answers by considering when the user submitted the question. This allows for the provision of more appropriate answers by determining the priority of answers based on when the user submitted the question. Some or all of the above processing in the answering unit may be performed using AI, for example, or not using AI. For example, the answering unit can input user question submission data into a generating AI and have the generating AI perform the determination of answer priority.

[0106] The answering unit can adjust the order of answers based on the relevance of the user's questions when providing answers. For example, the answering unit provides the optimal order of answers based on the relevance of the user's questions. The answering unit can also adjust the order of answers considering the relevance of the user's questions. Furthermore, the answering unit can determine the optimal order of answers according to the relevance of the user's questions. For example, the answering unit provides the optimal order of answers based on the relevance of the user's questions. The answering unit adjusts the order of answers considering the relevance of the user's questions. The answering unit determines the optimal order of answers according to the relevance of the user's questions. This allows for the provision of more appropriate answers by adjusting the order of answers based on the relevance of the user's questions. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input the relevance data of the user's questions into a generating AI and have the generating AI perform the adjustment of the order of answers.

[0107] The prediction unit can analyze the user's emotions and adjust the prediction method for body shape changes based on the analyzed emotions. For example, if the user is relaxed, the prediction unit can provide a detailed prediction of body shape changes. The prediction unit can also provide a positive prediction of body shape changes if the user is highly motivated. Furthermore, if the user is stressed, the prediction unit can provide a realistic prediction of body shape changes. For example, if the user is relaxed, the prediction unit can provide a detailed prediction of body shape changes. If the user is highly motivated, the prediction unit can provide a positive prediction of body shape changes. If the user is stressed, the prediction unit can provide a realistic prediction of body shape changes. This allows for more appropriate predictions by adjusting the prediction method for body shape changes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generating AI and have the generating AI adjust the method for predicting changes in body shape.

[0108] The prediction unit can improve the accuracy of its predictions by referring to the user's past exercise history when predicting changes in body shape. For example, the prediction unit can improve the accuracy of predicting changes in body shape based on the user's past exercise history. The prediction unit can also analyze the user's exercise history and provide the optimal prediction of changes in body shape. Furthermore, the prediction unit can improve the accuracy of predicting changes in body shape by referring to the user's exercise history. For example, the prediction unit can improve the accuracy of predicting changes in body shape based on the user's past exercise history. The prediction unit can analyze the user's exercise history and provide the optimal prediction of changes in body shape. The prediction unit can improve the accuracy of predicting changes in body shape by referring to the user's exercise history. In this way, the accuracy of predicting changes in body shape can be improved by referring to the user's past exercise history. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's past exercise history data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0109] The prediction unit can apply different prediction methods to each user's goals when predicting changes in body shape. For example, the prediction unit can apply a rapid body shape change prediction method to the user's short-term goals. It can also apply a detailed body shape change prediction method to the user's long-term goals. Furthermore, the prediction unit can apply the optimal prediction method according to the user's goals. For example, the prediction unit can apply a rapid body shape change prediction method to the user's short-term goals. The prediction unit can apply a detailed body shape change prediction method to the user's long-term goals. The prediction unit can apply the optimal prediction method according to the user's goals. By applying different prediction methods to each user's goals, it is possible to provide more appropriate predictions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user goal data into a generating AI and have the generating AI execute the application of different prediction methods.

[0110] The prediction unit can analyze the user's emotions and adjust the display method of body shape changes based on the analyzed emotions. For example, if the user is relaxed, the prediction unit can provide a detailed display of body shape changes. The prediction unit can also provide a positive display of body shape changes if the user is highly motivated. Furthermore, if the user is stressed, the prediction unit can provide a realistic display of body shape changes. For example, if the user is relaxed, the prediction unit can provide a detailed display of body shape changes. If the user is highly motivated, the prediction unit can provide a positive display of body shape changes. If the user is stressed, the prediction unit can provide a realistic display of body shape changes. This allows for a more appropriate display by adjusting the display method of body shape changes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generating AI and have the generating AI adjust how changes in body shape are displayed.

[0111] The prediction unit can analyze changes in predictions based on the timing of the user's exercise history submission when predicting changes in body shape. For example, the prediction unit analyzes predictions of changes in body shape based on the timing of the user's exercise history submission. The prediction unit can also provide predictions of changes in body shape according to the timing of the user's exercise history submission. Furthermore, the prediction unit can analyze predictions of changes in body shape considering the timing of the user's exercise history submission. For example, the prediction unit analyzes predictions of changes in body shape based on the timing of the user's exercise history submission. The prediction unit provides predictions of changes in body shape according to the timing of the user's exercise history submission. The prediction unit analyzes predictions of changes in body shape considering the timing of the user's exercise history submission. This allows for the provision of more appropriate predictions by analyzing changes in predictions based on the timing of the user's exercise history submission. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's exercise history submission timing data into a generating AI and have the generating AI perform the analysis of changes in predictions.

[0112] The prediction unit can analyze predictions by referring to the user's relevant market data when predicting changes in body shape. For example, the prediction unit analyzes predictions of changes in body shape based on the user's relevant market data. The prediction unit can also refer to the user's relevant market data and provide the optimal prediction of changes in body shape. Furthermore, the prediction unit can analyze predictions of changes in body shape by considering the user's relevant market data. For example, the prediction unit analyzes predictions of changes in body shape based on the user's relevant market data. The prediction unit provides the optimal prediction of changes in body shape by referring to the user's relevant market data. The prediction unit analyzes predictions of changes in body shape by considering the user's relevant market data. In this way, predictions of changes in body shape can be analyzed by referring to the user's relevant market data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the user's relevant market data into a generating AI and have the generating AI perform the prediction analysis.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The AI ​​personal trainer system can also collect user sleep data and incorporate it into training menus. The data collection unit analyzes the user's sleep patterns and quality, and the creation unit adjusts the training menu based on this. For example, if the user is not getting enough sleep, a lighter training menu can be suggested. Conversely, if the user's sleep quality improves, a more intense training menu can be provided. Furthermore, the motivation maintenance unit can send motivational messages at appropriate times based on the user's sleep data. This allows the system to provide an optimal training menu tailored to the user's sleep situation, thereby improving their overall health.

[0115] The AI ​​personal trainer system can collect user meal data and incorporate it into training menus. The data collection unit records the content and calories of meals consumed by the user, and the creation unit adjusts the training menu based on this data. For example, if a user has consumed a high-calorie meal, the system can suggest a training menu that promotes calorie burning. Furthermore, if the user's nutritional balance is unbalanced, the system can provide a training menu that encourages a more balanced diet. In addition, the response unit can provide nutritional advice based on the user's meal data. This allows the system to provide an optimal training menu tailored to the user's eating habits and support the maintenance of a healthy lifestyle.

[0116] The AI ​​personal trainer system can monitor the user's stress level and reflect it in the training menu. The data collection unit measures the user's stress level, and the creation unit adjusts the training menu based on this. For example, if the user is experiencing high stress, it can suggest a training menu with relaxing effects. Conversely, if the user's stress level is low, it can provide a more challenging training menu. Furthermore, the motivation maintenance unit can send messages encouraging relaxation at appropriate times based on the user's stress level. This allows the system to provide an optimal training menu tailored to the user's stress level and support the maintenance of their mental health.

[0117] The AI ​​personal trainer system can collect data on a user's social activities and incorporate it into their training menu. The data collection unit records the frequency and content of the user's social activities, and the creation unit adjusts the training menu based on this data. For example, if a user engages in many social activities, the system can suggest a short and effective training menu. Conversely, if a user engages in fewer social activities, the system can provide training menus that encourage social activity. Furthermore, the motivation maintenance unit can send messages encouraging participation in social events based on the user's social activity data. This allows the system to provide optimal training menus tailored to the user's social situation, supporting the maintenance of social well-being.

[0118] The AI ​​personal trainer system can analyze the user's emotions and adjust the difficulty of the training menu based on those emotions. For example, if the user is relaxed, the system will provide a more challenging training menu. If the user is stressed, it will provide a less challenging menu. Furthermore, if the user is highly motivated, it will provide a more challenging training menu. By adjusting the difficulty of the training menu based on the user's emotions, the system can provide a more appropriate training menu.

[0119] The AI ​​personal trainer system can provide variations of training menus based on the user's exercise history. The creation unit analyzes the types and frequency of exercises the user has performed in the past and proposes new training menus based on this. For example, by incorporating exercises the user has not done before, it can prevent training from becoming monotonous. Also, if the user is tired of a particular exercise, it can suggest different types of exercises. Furthermore, the motivation maintenance unit can suggest new challenges based on the user's exercise history. This allows for the provision of a wide variety of training menus tailored to the user's exercise history, thereby maintaining continuous motivation.

[0120] The AI ​​personal trainer system can analyze the user's emotions and adjust the training menu feedback based on those emotions. For example, if the user is relaxed, the support system provides detailed feedback. If the user is stressed, it can provide concise feedback. Furthermore, if the user is highly motivated, the support system can emphasize positive feedback. This allows for more appropriate feedback by adjusting the training menu feedback based on the user's emotions.

[0121] The AI ​​personal trainer system can visualize the progress of a training program based on the user's exercise history. The motivation maintenance unit analyzes data from the user's past exercises and displays progress in graphs and charts based on this analysis. For example, it can enhance a user's sense of accomplishment by visually displaying goals achieved and calories burned. It can also display the user's progress towards their set goals in real time. Furthermore, the motivation maintenance unit can provide advice on setting the next goal based on the user's exercise history. This allows for the visualization of progress based on the user's exercise history and helps maintain sustained motivation.

[0122] The AI ​​personal trainer system can analyze the user's emotions and adjust the timing of training sessions based on those emotions. For example, if the user is relaxed, the system can provide training sessions in the early morning. If the user is stressed, it can provide training sessions in the evening. Furthermore, if the user is highly motivated, it can provide training sessions during the day. By adjusting the timing of training sessions based on the user's emotions, the system can provide more appropriate training programs.

[0123] The AI ​​personal trainer system can adjust the intensity of training menus based on the user's exercise history. The creation unit analyzes the intensity and frequency of exercises the user has performed in the past and adjusts the intensity of the training menu accordingly. For example, if the user has performed high-intensity exercises in the past, it can provide a training menu of similar intensity. Conversely, if the user has performed low-intensity exercises, it can provide a training menu that gradually increases in intensity. Furthermore, the motivation maintenance unit can suggest a training menu of appropriate intensity based on the user's exercise history. In this way, it can provide the optimal training menu intensity based on the user's exercise history and support effective training.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The data collection unit collects information about the user's body type, goals, lifestyle, and past exercise history. The data collection unit collects information based on data entered by the user, data from wearable devices, and information from social media activity. For example, it collects data such as weight, height, target weight, heart rate, steps taken, calories burned, exercise-related posts, and training methods the user is interested in. Step 2: The creation unit analyzes the information collected by the collection unit and creates an individually appropriate training menu. The creation unit uses AI to analyze the collected data, adjusts the training menu based on the user's goals, and applies different algorithms depending on the user's body type and exercise history. Step 3: The support unit uses the smartphone's camera to perform real-time form checks and support accurate movement. The support unit uses AI to analyze the video captured by the smartphone's camera and provides real-time advice on areas for form correction. It also improves the accuracy of the check by considering the user's movement history, body type, and exercise history. Step 4: The motivation maintenance unit provides visualization of achievement and challenge functions. The motivation maintenance unit visualizes the progress towards the goals set by the user and allows users to set new goals through the challenge function. It also estimates the user's emotions, adjusts the display method of achievement based on the estimated emotions, and predicts the current achievement level by referring to past achievement data. Step 5: The answering unit provides 24 / 7 answers to questions about nutrition and exercise. The answering unit uses AI to provide appropriate answers to user questions, estimates the user's emotions and adjusts the wording of the answers accordingly, and adjusts the level of detail in the answers based on the content of the questions. Step 6: The prediction unit predicts and visualizes future body shape changes based on the current exercise status and goals. The prediction unit uses AI to predict future body shape changes based on the current exercise status and goals, estimates the user's emotions to adjust the prediction method, and improves the accuracy of the prediction by referring to past exercise history.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, creation unit, support unit, motivation maintenance unit, response unit, and prediction unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on the user's body shape, goals, lifestyle, and past exercise history using the control unit 46A of the smart device 14. The creation unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and creates an individually appropriate training menu. The support unit uses the camera 42 of the smart device 14 to perform real-time form checks and support accurate movements. The motivation maintenance unit provides visualization of achievement and challenge functions using the control unit 46A of the smart device 14. The response unit answers questions about nutrition and exercise 24 hours a day using the identification processing unit 290 of the data processing unit 12. The prediction unit predicts and visualizes future body shape changes based on the current exercise status and goals using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, creation unit, support unit, motivation maintenance unit, response unit, and prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on the user's body shape, goals, lifestyle, and past exercise history using the control unit 46A of the smart glasses 214. The creation unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and creates an individually appropriate training menu. The support unit uses the camera 42 of the smart glasses 214 to perform real-time form checks and support accurate movements. The motivation maintenance unit provides visualization of achievement and challenge functions using the control unit 46A of the smart glasses 214. The response unit answers questions about nutrition and exercise 24 hours a day using the identification processing unit 290 of the data processing unit 12. The prediction unit predicts and visualizes future body shape changes based on the current exercise status and goals using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, creation unit, support unit, motivation maintenance unit, response unit, and prediction unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on the user's body shape, goals, lifestyle, and past exercise history using the control unit 46A of the headset terminal 314. The creation unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and creates an individually appropriate training menu. The support unit uses the camera 42 of the headset terminal 314 to perform real-time form checks and support accurate movements. The motivation maintenance unit provides visualization of achievement and challenge functions using the control unit 46A of the headset terminal 314. The response unit answers questions about nutrition and exercise 24 hours a day using the identification processing unit 290 of the data processing unit 12. The prediction unit predicts and visualizes future body shape changes based on the current exercise status and goals using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the collection unit, creation unit, support unit, motivation maintenance unit, answering unit, and prediction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information on the user's body shape, goals, lifestyle, and past exercise history by the control unit 46A of the robot 414. The creation unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 and creates an individually appropriate training menu. The support unit performs real-time form checks using the camera 42 of the robot 414 to support accurate movements. The motivation maintenance unit provides visualization of achievement and challenge functions by the control unit 46A of the robot 414. The answering unit answers questions about nutrition and exercise 24 hours a day by the specific processing unit 290 of the data processing unit 12. The prediction unit predicts and visualizes future body shape changes from the current exercise status and goals by the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0189] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) A data collection unit that collects information about the user's body type, goals, lifestyle, and past exercise history, A creation unit analyzes the information collected by the aforementioned collection unit and creates an individually appropriate training menu. A support unit that uses a smartphone camera to check form in real time and supports accurate movement, The motivation maintenance unit provides features for visualizing progress and challenges, A support department that answers questions about nutrition and exercise 24 hours a day, It includes a prediction unit that predicts and visualizes future body shape changes based on current exercise status and goals. A system characterized by the following features. (Note 2) The aforementioned collection unit is We analyze user emotions and adjust the timing of information collection based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past exercise history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We analyze user sentiment and prioritize the information to collect based on the analyzed user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned creation unit, We analyze user emotions and adjust the way training menus are presented based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned creation unit, When creating a training menu, adjust the level of detail based on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned creation unit, When creating training menus, different algorithms are applied depending on the user's body type and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned creation unit, The system analyzes user emotions and adjusts the length of the training menu based on the analyzed emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned creation unit, When creating training menus, the priority of the menus is determined based on when the user submitted their exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned creation unit, When creating a training menu, adjust the order of the menu items based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit is Analyze user sentiment and adjust form validation criteria based on the analyzed user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit is When checking forms, improve the accuracy of the checks by considering the user's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is During form checks, the system takes into account the user's body type and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is The system analyzes user sentiment and adjusts the order in which form check results are displayed based on the analyzed user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is During form validation, the system will take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is During form validation, referencing relevant user literature improves the accuracy of the validation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned motivation maintenance unit is We analyze user sentiment and adjust how achievement levels are displayed based on the analyzed user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned motivation maintenance unit is When visualizing achievement levels, the system predicts current achievement levels by referencing the user's past achievement data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned motivation maintenance unit is When visualizing the degree of achievement, different visualization methods are applied for each user's goal. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned motivation maintenance unit is Analyze user emotions and adjust the importance of achievement based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned motivation maintenance unit is When visualizing achievement levels, we analyze changes in achievement levels based on when users submitted their exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned motivation maintenance unit is When visualizing the degree of achievement, analyze the degree of achievement by referring to the user's relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned response section is, We analyze user emotions and adjust the way responses are expressed based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned response section is, When responding, adjust the level of detail in the response based on the user's question. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned response section is, When responding, different response algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned response section is, Analyze the user's emotions and adjust the length of the response based on the analyzed emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned response section is, When responding, we prioritize answers based on when the user submitted their question. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned response section is, When users respond, the order of responses will be adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, We analyze user emotions and adjust the method of predicting body shape changes based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The prediction unit, When predicting changes in body shape, the system improves prediction accuracy by referencing the user's past exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The prediction unit, When predicting changes in body shape, different prediction methods are applied depending on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 35) The prediction unit, The system analyzes user emotions and adjusts how body shape changes are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The prediction unit, When predicting changes in body shape, the system analyzes how the prediction changes based on when the user submitted their exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 37) The prediction unit, When predicting changes in body shape, we analyze the prediction by referring to the user's relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects information about the user's body type, goals, lifestyle, and past exercise history, A creation unit analyzes the information collected by the aforementioned collection unit and creates an individually appropriate training menu. A support unit that uses a smartphone camera to check form in real time and supports accurate movement, The motivation maintenance unit provides features for visualizing achievement and challenges, A support department that answers questions about nutrition and exercise 24 hours a day, It includes a prediction unit that predicts and visualizes future body shape changes based on current exercise status and goals. A system characterized by the following features.

2. The aforementioned collection unit is We analyze user emotions and adjust the timing of information collection based on the analyzed user emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past exercise history and select the optimal method for collecting information. The system according to feature 1.

4. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is We analyze user sentiment and prioritize the information to collect based on the analyzed user sentiment. The system according to feature 1.

6. The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

8. The aforementioned creation unit, We analyze user emotions and adjust the way training menus are presented based on the analyzed user emotions. The system according to feature 1.

Images