system

Abstract

We provide the system. [Solution] A means for inputting the user's physical information, A means of conducting a user fitness assessment, Means for transmitting the user's physical information and fitness evaluation results to a server, The server has means for generating a training program based on the information, Means for providing the aforementioned training program to the user, A means of collecting user training feedback and optimizing the training program, A system that includes this.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] Conventional training programs in sports gyms have a problem that it is difficult to provide an optimal program in a batch manner according to the characteristics and progress of individual users. Specifically, the same training menu is often provided to users with different fitness levels, ages, weights, goals, etc., making it difficult to achieve effective and efficient training. In addition, there was no system that could collect user feedback in real time and adjust the program accordingly, which could have an adverse effect on user motivation and progress.

Means for Solving the Problems

[0005] To solve the above-mentioned problems, the present invention provides a system that generates an optimal training program based on the user's physical information and fitness assessment results, and provides it to the user. This system includes the following means:

[0006] A means of inputting the user's physical information,

[0007] A means of conducting user fitness assessments,

[0008] Means for transmitting the user's physical information and fitness evaluation results to a server,

[0009] The server has means for generating a training program based on the information.

[0010] Means for providing the aforementioned training program to the user,

[0011] A means of collecting user training feedback and optimizing the training program.

[0012] This allows users to receive training programs tailored to their individual characteristics, and the program is optimized based on feedback each time, resulting in more effective and efficient training. Furthermore, real-time feedback collection and program adjustments make it easy to maintain user motivation and monitor progress.

[0013] "Means for inputting user physical information" refers to terminals, devices, and related software used to input physical characteristics and personal data such as the user's age, height, weight, gender, fitness level, and goals.

[0014] "Means for conducting user fitness assessments" refers to devices or processes that allow users to perform specific fitness tasks (e.g., heart rate, strength tests, endurance tests, flexibility tests, etc.) and record the results.

[0015] "Means for transmitting the user's physical information and fitness evaluation results to the server" refers to communication devices and protocols for transmitting the user's physical information and fitness evaluation results to the server via the Internet or a local network.

[0016] "Means by which the server generates a training program based on the information" refers to software and hardware installed on the server that analyzes the received user information using AI algorithms and data analysis technologies to generate individually optimized training programs.

[0017] "Means for providing the training program to the user" refers to a display device, a mobile application, and its interface for visually or audibly presenting the content of the generated training program to the user.

[0018] "Means for collecting user training feedback and optimizing the training program" refers to input devices and software for collecting user feedback on training, and AI algorithms and software for analyzing that feedback and adjusting the training program accordingly. [Brief explanation of the drawing]

[0019] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0021] First, the language used in the following description will be explained.

[0022] 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), and APU (Accelerated Processing Unit).

[0023] 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.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 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.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] The 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.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention relates to a system that enables users to efficiently and effectively achieve their fitness goals by providing them with individually optimized training programs. The system uses AI to generate training programs based on the user's physical information and fitness assessment results, and adjusts them in real time according to the user's progress.

[0041] System-wide configuration

[0042] This system consists of the following main components:

[0043] 1. User terminal

[0044] 2. Fitness assessment equipment

[0045] 3. Server

[0046] Program Processing Overview

[0047] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals.

[0048] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, the user might input the number of squats performed per minute, the duration of a plank, or their heart rate while using the exercise bike.

[0049] The device transmits the collected user's physical information and fitness assessment results to the server. The server receives this data, analyzes it using an AI algorithm, and generates an optimal training program for the user. This program details appropriate exercises, sets, reps, rest times, and more.

[0050] The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. During training, the terminal records the user's progress in real time and provides instructions and feedback as needed.

[0051] After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into a device. The device sends this feedback to a server, which analyzes it to optimize the next training program. For example, the server might decide whether the user should increase the number of squat sets or add exercises to improve endurance.

[0052] Specific example

[0053] Day 1

[0054] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[0055] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0056] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0057] Terminal: Sends these evaluation results to the server.

[0058] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0059] Terminal: Displays the generated training program.

[0060] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[0061] Terminal: Sends feedback to the server.

[0062] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[0063] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[0067] Step 2:

[0068] Terminal: The terminal sends the profile information entered by the user to the server. The server then receives the user's basic information and registers the profile.

[0069] Step 3:

[0070] Device: Encourage the user to complete an initial fitness assessment. Specifically, instruct them to perform basic exercises such as squats, planks, and stationary bike exercises, and to input the results. For example, present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the stationary bike."

[0071] Step 4:

[0072] User: Perform the instructed evaluation and enter the results into the terminal. For example, enter results such as "15 squats, 30 seconds plank, exercise bike heart rate 120".

[0073] Step 5:

[0074] The device sends the user's fitness assessment results to the server. The server then receives the user's detailed fitness data.

[0075] Step 6:

[0076] Server: Based on the received user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data. Based on the analysis, it generates an optimal training program for the user. This program includes specific exercise types, sets, reps, rest times, and more.

[0077] Step 7:

[0078] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[0079] Step 8:

[0080] User: Follow the program displayed on your device or smartphone to perform the workout. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[0081] Step 9:

[0082] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[0083] Step 10:

[0084] User: After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might provide feedback such as, "The squats were a little too easy," or "The exercise bike was appropriate."

[0085] Step 11:

[0086] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[0087] Step 12:

[0088] Server: Analyzes the received feedback and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or slightly shorten the rest time.

[0089] Step 13:

[0090] Server: Sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[0091] By repeating the above steps, users can always train efficiently with a training program optimized for them. This allows users to achieve their fitness goals more effectively and efficiently.

[0092] (Example 1)

[0093] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Traditional training systems have struggled to provide optimal training programs tailored to each user's individual fitness level and goals. Furthermore, they lacked the ability to instantly reflect user training progress and feedback, and adjust training programs in real time. As a result, they provided insufficient support for efficiently and effectively achieving fitness goals.

[0095] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0096] In this invention, the server includes means for inputting the user's biometric information, means for performing an evaluation of the user's exercise, and means for transmitting the user's biometric information and exercise evaluation results to the server. This makes it possible to generate individually optimized training programs and adjust the training programs in real time by immediately reflecting the user's progress and feedback.

[0097] "Biometric information" refers to data that shows a user's personal physical characteristics, such as age, gender, height, and weight.

[0098] "Exercise evaluation" refers to measuring and evaluating the results of exercise performed by a user, and includes test results for squats, planks, and exercise bikes.

[0099] A "server" is a device or system that receives a user's biometric information and exercise evaluation results, generates a training program based on that data, and performs analysis including feedback.

[0100] A "generative AI model" refers to an artificial intelligence algorithm used to analyze collected user data and generate the optimal training program.

[0101] A "training program" is a detailed plan that outlines the type of exercises a user should perform, the number of sets, the number of repetitions, rest periods, and other specific details.

[0102] "Feedback" refers to opinions and evaluations that users provide after a training session regarding the difficulty level of the training, satisfaction level, physical condition, etc.

[0103] A "calculating device" refers to a device used by users to input information, and includes smartphones, tablets, and dedicated terminals at gyms.

[0104] This invention relates to a system that provides users with individually optimized training programs to efficiently achieve their fitness goals. Specific embodiments for carrying out the invention are described below.

[0105] System Configuration

[0106] This system consists of the following main components:

[0107] 1. User devices (e.g., smartphones, tablets)

[0108] 2. Fitness assessment equipment (e.g., exercise bike ergometer, squat sensor)

[0109] 3. Server (Cloud Server)

[0110] Program processing flow

[0111] 1. Upon arriving at the gym, users log in to the system using their smartphone or a gym terminal and enter their profile information. This includes age, gender, height, weight, fitness level, and goals. For example, information such as 30 years old, male, 175cm tall, 70kg in weight, beginner fitness level, and goal of increasing muscle strength might be entered.

[0112] 2. The device prompts the user to perform an initial fitness assessment. This includes basic exercises such as squats, planks, and using an exercise bike ergometer, and the results are entered into the device. For example, the user might enter how many squats they performed per minute or how long they were able to hold a plank. For instance, they might enter results such as 15 squats in one minute, a 30-second plank, and a heart rate of 120 on the exercise bike ergometer.

[0113] 3. The terminal transmits the collected user biometric information and exercise evaluation results to the server. For example, it transmits data such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TENSORFLOW® or PyTorch). The server generates an optimal training program based on the user's characteristics. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0114] 4. The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. The terminal records the user's progress in real time during training and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats."

[0115] 5. After completing the training session, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might enter feedback such as, "The number of squat sets was a little too easy." The device sends this feedback to the server. The server analyzes the feedback and optimizes the next training program. For example, adjustments might be made, such as increasing the number of squat sets or adding exercises to improve endurance.

[0116] Specific example

[0117] Day 1

[0118] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[0119] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0120] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0121] Terminal: Sends these evaluation results to the server.

[0122] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0123] Terminal: Displays the generated training program.

[0124] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[0125] Terminal: Sends feedback to the server.

[0126] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[0127] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[0128] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0129] Step 1:

[0130] Upon arriving at the gym, users log into the system using a terminal or their smartphone and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. For example, the input might include data such as "30 years old, male, 175cm tall, 70kg weight, beginner fitness level, goal: muscle improvement." This data is received by the terminal and stored as the user's basic physical information.

[0131] Step 2:

[0132] The device prompts the user to perform an initial fitness assessment. This assessment includes exercises such as squats, planks, and exercise bike ergometer exercises. The user performs these exercises and inputs the results into the device. For example, the user might input data such as 15 squats per minute, a 30-second plank, and a heart rate of 120 on the exercise bike. This data is collected by the device and stored as the user's exercise assessment result.

[0133] Step 3:

[0134] The device sends the collected user biometric information and exercise evaluation results to the server. Input includes evaluation results such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TensorFlow or PyTorch). Specifically, it generates an optimal training program based on the user's physical information and exercise evaluation results. This analysis result becomes the server's output.

[0135] Step 4:

[0136] The server sends the generated training program to the terminal or the user's smartphone. The output training program might include, for example, 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks. This program is then displayed on the terminal.

[0137] Step 5:

[0138] The user performs the training according to the displayed training program. During training, the device records the user's progress in real time and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats." Data regarding the user's progress is collected by the device.

[0139] Step 6:

[0140] After completing a workout, users input feedback on the difficulty level, satisfaction level, and physical condition into their device. Specific inputs may include comments such as, "The number of squat sets was a little too easy." This feedback data is received by the device and sent to the server.

[0141] Step 7:

[0142] The server analyzes the received feedback and optimizes the next training program. Specifically, it adjusts the content of the user's training program (number of sets, reps, exercise types, etc.) based on the feedback. For example, it might increase the number of squat sets or add exercises to improve endurance. This optimized training program becomes the server's output and is provided to the user in the next training session.

[0143] (Application Example 1)

[0144] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0145] Traditional fitness systems have struggled to provide personalized training programs in real time, tailored to each user's unique physical characteristics and fitness level. Furthermore, they lacked sufficient means to monitor training progress in real time and provide appropriate feedback. As a result, it was difficult for users to efficiently and effectively achieve their fitness goals.

[0146] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0147] In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user using a smart wearable device or head-mounted display, means for transmitting the user's physical information and fitness assessment results to the server, means for generating a training program using a generative AI model, means for providing the training program to the user, means for monitoring the user's progress in real time and providing feedback through a smart wearable device or head-mounted display, and means for collecting the user's training feedback and optimizing the training program. This enables the generation of individually optimized training programs and the provision of real-time progress monitoring and feedback.

[0148] "User physical information" refers to data such as the user's age, gender, height, weight, fitness level, and fitness goals.

[0149] A "smart wearable device" is an electronic device that collects a user's biometric information and can monitor their training progress in real time. Examples include smartwatches and heart rate monitors.

[0150] A "head-mounted display" is a display device worn by a user that visually displays training programs and feedback. This includes, for example, AR (augmented reality) and VR (virtual reality) devices.

[0151] A "fitness assessment" is a process that measures a user's current fitness level through basic exercises such as squats, planks, and stationary bikes.

[0152] A "server" is a centralized computer system that receives users' physical information and fitness assessment results, and generates and optimizes training programs using a generated AI model.

[0153] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and automatically generates and adjusts the optimal training program based on that analysis.

[0154] A "training program" is a plan that includes a set of exercises individually optimized for the user to achieve their fitness goals, along with instructions regarding the number of sets, repetitions, rest times, and other related details.

[0155] "Real-time monitoring" refers to the ability to instantly monitor a user's progress and physical condition during training, and to provide immediate adjustments and feedback as needed.

[0156] "Feedback" refers to information used to modify and optimize training programs based on the user's experience with training, including difficulty level, satisfaction level, and physical condition.

[0157] "Optimization" is the process of adjusting a training program to best suit the user's fitness goals based on their past data and feedback.

[0158] This invention relates to a personal fitness system using smart wearable devices and head-mounted displays (HMDs) that can be used by users in fitness facilities. The embodiments for carrying out this invention are described in detail below.

[0159] Overall system configuration

[0160] This system consists of a user terminal, a smart wearable device or head-mounted display (HMD), fitness assessment equipment, and a server.

[0161] Program Processing Overview

[0162] First, upon arriving at the fitness facility, users put on smart glasses or HMDs and enter their profile information. This includes age, gender, height, weight, fitness level, and goals.

[0163] Next, the user performs an initial fitness assessment using a smart wearable device or HMD. For example, they perform squats, planks, or use an exercise bike, and the results are automatically collected via sensors. The collected data is then transmitted to a server via the user's device.

[0164] The server generates a training program using an AI model based on the received user's physical information and fitness assessment results. The generated training program is sent to a smart wearable device or HMD and presented to the user visually.

[0165] While the user is training, smart wearable devices and HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, instructions such as "Improve your squat form" or "You have 10 seconds until the next set" may be displayed.

[0166] After completing a training session, users provide feedback through smart wearable devices or HMDs. For example, they can input feedback on the difficulty level of the training, satisfaction, and their physical condition.

[0167] The server analyzes the collected feedback to optimize the next training program. This process ensures that users always receive an optimized training program, allowing them to achieve their fitness goals efficiently and effectively.

[0168] Specific example

[0169] Initial Setup

[0170] User: 30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement. Log in wearing smart glasses and enter profile information.

[0171] Smart glasses: As an initial evaluation, we suggest testing them during squats, planks, and exercise bike workouts.

[0172] User: Obtained results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0173] Smart glasses: These evaluation results are sent to the server.

[0174] Server: Analyzes data to generate the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0175] Smart glasses: Display the generated training program.

[0176] Training Session

[0177] User: Conduct training according to the proposed program.

[0178] Smart glasses: Monitor progress in real time and provide feedback such as "Improve your squat form."

[0179] User: After completing the training, provide feedback on difficulty level, satisfaction, and physical condition.

[0180] Smart glasses: Input feedback is sent to the server.

[0181] Server: Analyzes feedback and optimizes the next training program.

[0182] Example of a prompt

[0183] "Please generate an optimized training program for a 30-year-old male, 175cm tall, weighing 70kg, aiming for beginner-level strength improvement."

[0184] "You are a 30-year-old male aiming to improve your strength. Initial assessments include 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm on an exercise bike. Please recommend an optimal training program."

[0185] In this way, the system of the present invention effectively supports users in achieving their fitness goals.

[0186] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0187] Step 1:

[0188] When a user arrives at a fitness facility, they put on smart glasses or a head-mounted display (HMD) and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. This provides the user with basic physical information.

[0189] Input: Age, Gender, Height, Weight, Fitness Level, Goals

[0190] Output: User's basic physical information

[0191] Step 2:

[0192] Using smart glasses or an HMD, users perform an initial fitness assessment. As users perform exercises such as squats, planks, and exercise bike rides, the results are automatically collected via sensors. These assessment results serve as a baseline for the user's initial training.

[0193] Input: User's exercise results (e.g., squats, planks, exercise bike)

[0194] Output: Fitness evaluation results (e.g., number of squats, duration of plank, heart rate on exercise bike)

[0195] Step 3:

[0196] The terminal sends the collected user's physical information and fitness assessment results to the server. The server receives this data and performs preprocessing.

[0197] Input: User's basic physical information, fitness assessment results

[0198] Output: User information and evaluation results stored in the database

[0199] Step 4:

[0200] The server uses a generative AI model to generate training programs based on user data. This AI model analyzes user data to determine the optimal type of exercise, number of sets, number of reps, and rest time.

[0201] Input: User information, fitness assessment results

[0202] Output: Optimized training program (e.g., 20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds plank x 3 sets)

[0203] Step 5:

[0204] The generated training program is sent from the server to smart glasses or an HMD and presented visually to the user. The user then performs the training according to this program.

[0205] Input: Optimized training program

[0206] Output: Training program displayed on smart glasses or HMD.

[0207] Step 6:

[0208] While the user is training, smart glasses or HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, they might display instructions such as, "Improve your squat form" or "You have 10 seconds until the next set begins."

[0209] Input: User training progress

[0210] Output: Real-time feedback and instructions

[0211] Step 7:

[0212] After completing the training, users provide feedback through smart glasses or HMDs. This feedback includes information about the difficulty level of the training, satisfaction level, and physical condition.

[0213] Input: User feedback (information on difficulty level, satisfaction level, and physical condition)

[0214] Output: Feedback data

[0215] Step 8:

[0216] The device sends the collected feedback data to the server. The server analyzes this data and optimizes the next training program.

[0217] Input: User feedback data

[0218] Output: Optimized next training program

[0219] This series of processing steps ensures that users always receive a individually optimized training program, allowing them to effectively achieve their fitness goals.

[0220] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0221] This invention relates to a system that provides a user with an optimized training program and further adjusts that program based on the user's emotional state. This system not only generates an optimal training program based on the user's physical information and fitness assessment results, but also acquires the user's emotional data using an emotion engine and reflects it in the training content, thereby providing a more effective training experience.

[0222] System-wide configuration

[0223] This system consists of the following main components:

[0224] 1. User terminal

[0225] 2. Fitness assessment equipment

[0226] 3. Server

[0227] 4. Emotional Engine

[0228] Program Processing Overview

[0229] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: muscle improvement."

[0230] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, it might present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the exercise bike."

[0231] The user performs the instructed evaluation and enters the results into the device. For example, they might enter results such as "15 squats, 30 seconds of plank, and a heart rate of 120 on the exercise bike."

[0232] These evaluation results and profile information are sent from the device to the server. The server uses an AI algorithm to analyze this data and generate an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[0233] The server sends the generated training program to the terminal or the user's smartphone. This allows the user to review their training program.

[0234] Users perform their workouts according to the displayed program. For example, they might follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[0235] Furthermore, this system has an emotion engine that acquires the user's emotional data. The emotion engine recognizes the user's emotional state (e.g., stress level, motivation level) by analyzing the user's facial expressions and tone of voice. During training, the emotion engine collects the user's emotional data in real time and sends it to the server.

[0236] The server adjusts the training program in real time based on emotional data and training progress. For example, if a user is feeling stressed, it may add stretching exercises to help them relax.

[0237] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The device sends this feedback to a server, which is used to optimize the next training program.

[0238] Specific example

[0239] Day 1

[0240] User: Arrives at the gym, logs in on the device, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle improvement).

[0241] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0242] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[0243] Terminal: Sends these evaluation results to the server.

[0244] Server: Analyzes data and generates an optimal training program. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0245] Terminal: Displays the generated training program.

[0246] User: Follow the program to complete the training.

[0247] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[0248] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[0249] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[0250] Terminal: Sends feedback to the server.

[0251] Server: Analyzes the feedback and adjusts the next training program (e.g., increases the number of squat sets).

[0252] By repeating this process, users can always train efficiently with a training program optimized for them. Combining this with an emotional engine allows for training tailored to the user's emotional state, resulting in an even more effective training experience.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[0256] Step 2:

[0257] Terminal: Sends the profile information entered by the user to the server. The server saves the received data and registers it as the user profile.

[0258] Step 3:

[0259] Device: Prompts the user to perform an initial fitness assessment. For example, assessment items such as squats, planks, and heart rate measurement while using an exercise bike are displayed.

[0260] Step 4:

[0261] User: Perform the initial assessment displayed on the screen and enter the results into the device. For example, enter "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120".

[0262] Step 5:

[0263] The device sends the user's fitness assessment results to the server. The server then receives detailed fitness data.

[0264] Step 6:

[0265] Server: Based on the user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data and generate an optimal training program for the user. This program includes detailed information such as the type of exercise, number of sets, number of reps, and rest time.

[0266] Step 7:

[0267] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[0268] Step 8:

[0269] User: Follow the training program displayed on your device or smartphone. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks."

[0270] Step 9:

[0271] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[0272] Step 10:

[0273] Emotion Engine: During training, it analyzes the user's facial expressions and tone of voice to recognize emotions. For example, it analyzes the user's facial expressions when they are facing the camera and sends that data to the server.

[0274] Step 11:

[0275] Server: Based on emotional data and training progress data, the server adjusts the training program in real time. For example, if the user is feeling tired or stressed, it adds stretching exercises to the program to help them relax.

[0276] Step 12:

[0277] User: After completing a workout, enter feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, enter feedback such as "The squats were a little too easy" or "The exercise bike was appropriate."

[0278] Step 13:

[0279] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[0280] Step 14:

[0281] Server: Analyzes the received feedback data and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or shorten rest periods.

[0282] Step 15:

[0283] Server: The server sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[0284] By repeating the above steps, the user can always efficiently train with a training program optimized for themselves. Furthermore, by using the emotion engine, a training program can be provided according to the user's emotional state, realizing a more effective training experience.

[0285] (Example 2)

[0286] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0287] Conventional exercise programs are generated once based on the user's physical information and initial fitness evaluation, but there is a problem that it is difficult to achieve the user's motivation and effective training because the user's emotional state during and after exercise is not considered. In addition, since real-time adjustment is not possible, the once-set program may not be optimal for the user.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0289] In this invention, the server includes means for receiving and storing the user's biological information and physical strength evaluation results, means for generating an exercise plan based on the received information, means for recognizing the emotional state and collecting data, and means for adjusting and optimizing the exercise plan in real time based on the emotional data and feedback after exercise. As a result, it is possible to provide an optimal exercise plan considering the user's emotional state and to adjust the training program in real time.

[0290] "Biological information" refers to basic physical data such as the user's age, gender, height, and weight.

[0291] "Physical fitness assessment" refers to the results of exercises such as squats, planks, and stationary bike rides performed to measure the user's fitness level.

[0292] The term "central processing unit" refers to a computer device that analyzes the user's biometric information and physical fitness assessment results, and generates and adjusts exercise plans.

[0293] An "exercise plan" refers to a program that includes specific exercise instructions for the user, such as the type of training, number of sets, number of reps, and rest time that is best suited to that user.

[0294] "Emotional state" refers to a psychological state such as stress level and motivation level, which can be determined from the user's facial expressions and tone of voice.

[0295] "Emotional data" refers to data collected as numerical or textual information by analyzing the emotional state of users.

[0296] "Real-time adjustment" refers to the process of instantly optimizing the exercise plan based on the user's current emotional state and training progress.

[0297] "Feedback" refers to the opinions and evaluations that users provide regarding the difficulty level, satisfaction level, and physical condition of their training.

[0298] The system of this invention provides the user with an optimized exercise plan and further adjusts the exercise plan in real time based on their emotional state. This system mainly includes a user terminal, fitness evaluation equipment, a server, and an emotion engine.

[0299] Hardware and software to be used

[0300] User terminal

[0301] The user terminal is a device used to collect biometric information and fitness assessment results entered by the user. Specific examples include smartphones and tablets. Users enter data such as their age, gender, height, weight, fitness level, and goals into this terminal. For example, they might enter: "Age: 30, Gender: Male, Height: 175cm, Weight: 70kg, Beginner, Goal: Muscle Strength Improvement."

[0302] Fitness assessment equipment

[0303] Fitness assessment equipment is used to measure a user's physical fitness level. Specific examples include squat counter plates, plank timers, and exercise bike ergometers. These devices measure the results of the exercises performed by the user and input them into a terminal. For example, the user might input results such as "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120."

[0304] server

[0305] The server receives biometric information and fitness assessment results sent from the user's terminal and analyzes them using an AI algorithm. The server implements an AI algorithm using Python and TensorFlow, which is used to generate an optimal exercise plan. The generated exercise plan includes details such as the type of exercise, number of sets, number of repetitions, and rest time. The server then sends the generated exercise plan to the user's terminal.

[0306] Emotional Engine

[0307] The emotion engine is a system for recognizing the user's emotional state and collecting data. Expression analysis and voice analysis software using cameras and microphones (e.g., OpenCV and Watson (registered trademark) Tone Analyzer) are utilized. This engine analyzes the user's expressions and voice tones and collects emotional data such as stress levels and motivation levels. The collected emotional data is sent to the server in real time and used to adjust the exercise plan.

[0308] Specific examples

[0309] Example of profile information input

[0310] The user inputs the following using a smartphone:

[0311] "Age: 30 years old, gender: male, height: 175 cm, weight: 70 kg, fitness level: beginner, goal: muscle strength improvement"

[0312] Example of fitness evaluation input

[0313] The user inputs the results of the fitness evaluation as follows:

[0314] "Squats: 15 times in 1 minute, plank: 30 seconds, air bike heart rate: 120"

[0315] Example of training program display

[0316] The exercise plan generated by the server is displayed as follows:

[0317] "Training program: 20 minutes of air bike, 15 squats × 3 sets, 30 - second plank × 3 sets"

[0318] Example of feedback input

[0319] The user provides feedback as follows after the exercise:

[0320] "Feedback: The squats were a little too easy."

[0321] Thus, by using this system, users can always train efficiently with an exercise plan optimized for them. Furthermore, by introducing an emotion engine, real-time adjustments can be made according to the user's emotional state, maximizing the effectiveness of the training.

[0322] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0323] Step 1:

[0324] Upon arriving at the gym, users log in to the system using their personal device or smartphone and enter their profile information. Specifically, they enter basic data such as age, gender, height, weight, fitness level, and goals.

[0325] Input: User profile information

[0326] Output: Profile data entered into the device

[0327] Step 2:

[0328] The device prompts the user to perform an initial fitness assessment. This involves basic exercises such as squats, planks, and using an exercise bike. The user then inputs the results into the device.

[0329] Specific examples of exercises include "doing as many squats as possible in one minute," "holding a plank for as long as possible," and "measuring your heart rate while using an exercise bike."

[0330] Input: User's fitness assessment results

[0331] Output: Fitness evaluation data entered into the terminal

[0332] Step 3:

[0333] The device sends the collected profile information and fitness evaluation results to the server. Here, the device bundles the data into packets and sends them to the server.

[0334] Input: Profile information and fitness evaluation results entered on the device.

[0335] Output: Integrated data sent to the server

[0336] Step 4:

[0337] The server analyzes the received data using AI algorithms (utilizing Python and TensorFlow) to generate an optimal exercise plan for the user. The analysis includes the type of exercise, number of sets, number of reps, and rest time based on the user's fitness level and goals.

[0338] Input: Integrated data sent to the server

[0339] Output: Generated motion plan

[0340] Step 5:

[0341] The server sends the generated exercise plan to the terminal. The terminal displays the received exercise plan to the user. The user can then check the detailed exercise plan on the terminal.

[0342] In terms of specific actions, a detailed exercise plan is displayed on the user's device, such as "20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds of plank x 3 sets."

[0343] Input: Exercise plan generated by the server

[0344] Output: Exercise plan displayed on the terminal

[0345] Step 6:

[0346] The user performs the training according to the displayed exercise plan.

[0347] Specifically, the user will use an exercise bike for 20 minutes, followed by squats and planks for a specified number of repetitions and sets.

[0348] Input: Exercise plan displayed on the device

[0349] Output: Actual training by users

[0350] Step 7:

[0351] During training, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. It collects user emotional data using cameras and microphones and transmits it to the server in real time.

[0352] Specifically, we use facial recognition software (e.g., OpenCV) and speech analysis software (Watson Tone Analyzer).

[0353] Input: User's emotional data (facial expressions and tone of voice)

[0354] Output: Sentiment data sent to the server

[0355] Step 8:

[0356] The server analyzes the received emotional data and adjusts the exercise plan in real time as needed. If the user is feeling stressed, it will make adjustments such as adding stretches to help them relax.

[0357] Input: Emotional data sent to the server

[0358] Output: Adjusted exercise plan

[0359] Step 9:

[0360] After completing a workout, users enter feedback into a device regarding the difficulty level, satisfaction level, and their physical condition. This feedback includes information about how effective or difficult the exercise was.

[0361] Input: User feedback (impressions and evaluations after exercise)

[0362] Output: Feedback data entered into the terminal

[0363] Step 10:

[0364] The device sends feedback to the server. The server analyzes the user's feedback and optimizes the next exercise plan.

[0365] In terms of specific actions, adjustments such as increasing the number of sets in the next squat session will be made.

[0366] Input: Feedback data entered into the device

[0367] Output: Optimization of the next exercise plan

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0370] Conventional autonomous vehicles have difficulty considering the driver's emotional state and stress level, which can result in insufficient driver comfort and safety. Furthermore, the lack of real-time driving mode changes or entertainment adjustments based on emotional state means that driver stress and fatigue during long drives cannot be reduced.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user, means for collecting the user's emotional data and analyzing the emotional state in real time, and means for generating a training program that reflects the user's emotional state based on the emotional data. This enables real-time adjustments based on the emotional state, thereby improving the driver's comfort and safety.

[0372] "Means of inputting user physical information" refer to devices or applications that acquire and input basic physical information such as the user's age, gender, height, and weight.

[0373] "Means for conducting user fitness assessments" refers to devices and methods for evaluating a user's physical strength and athletic ability through fitness activities such as squats, planks, and exercise bikes.

[0374] A "server" is a computing system that analyzes a user's physical information, fitness assessment results, and emotional data to generate an optimal training program.

[0375] "Means for generating training programs" refers to algorithms and software that generate exercise programs tailored to the user's fitness goals based on data collected by the server.

[0376] "Means of providing to the user" refers to devices or applications used to display the generated training program to the user or to provide instructions.

[0377] "Means for collecting user training feedback and optimizing training programs" refers to devices or applications that collect feedback information provided by users after completing training and adjust the next training program based on that information.

[0378] "Means for collecting user emotional data and analyzing emotional states in real time" refers to devices and software that use cameras and microphones to analyze users' facial expressions and voice tone, and evaluate their emotional state in real time.

[0379] "Means for generating training programs that reflect a user's emotional state based on emotional data" refers to algorithms or software that take into account a user's emotional state and adjust the content and methods of training accordingly.

[0380] This invention relates to a system that provides a training program optimized for the user by inputting the user's physical information and fitness evaluation results, and also taking into account emotional data. Specific embodiments of this system are described below.

[0381] System-wide configuration

[0382] This system consists of the following main components:

[0383] 1. User terminal: A device used to input the user's physical information and fitness assessment. For example, a smartphone or tablet may be used.

[0384] 2. Fitness evaluation equipment: This equipment is used to measure the results of exercises such as squats, planks, and exercise bikes.

[0385] 3. Server: This is the central computing system that analyzes user input data, evaluation results, and sentiment data to generate the optimal training program.

[0386] 4. Emotion Engine: This is software that analyzes the user's facial expressions and voice tone to evaluate their emotional state in real time. Specific examples include emotion recognition models using OpenCV or Keras.

[0387] Program Processing Overview

[0388] First, the user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). This information is then sent from the user's device to the server.

[0389] Next, the user performs tests such as squats, planks, and exercise bikes using fitness evaluation equipment and enters the results into a terminal. These evaluation results are also sent to the server.

[0390] Based on this data, the server uses a generated AI model to create an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[0391] The generated training program is sent from the server to the user's terminal, and the user begins training according to it.

[0392] During training, the emotion engine analyzes the user's facial expressions and tone of voice in real time and sends emotional data to the server. This allows the server to adjust the training program in real time, taking the user's emotional state into consideration. For example, if the user is feeling stressed, adjustments such as adding stretches to help them relax may be made.

[0393] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The server analyzes this feedback to optimize the next training program.

[0394] Specific example

[0395] Example 1

[0396] User: Arrives at the gym, logs into the system on their smartphone, and enters their profile information (e.g., 30 years old, male, 175cm tall, 70kg weight).

[0397] User terminal: As an initial evaluation, we suggest testing squats, planks, and an exercise bike.

[0398] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[0399] Server: Based on these evaluation results, it generates an optimal training program (e.g., 20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks).

[0400] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[0401] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[0402] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[0403] Example of a prompt

[0404] "What kind of music would you play if the driver of this vehicle was tired?"

[0405] "When the driver's emotional state is stressed, please tell me the appropriate driving mode."

[0406] By specifically demonstrating the embodiments for carrying out the invention in this way, it is possible to provide users with an optimal training experience and improve driver comfort and safety.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). The entered data is sent from the device to the server. This initiates the processing of the user's data.

[0410] Step 2:

[0411] Users perform tests such as squats, planks, and exercise bike workouts using fitness assessment equipment. The test results (e.g., 15 squats in 1 minute, 30-second plank, and a heart rate of 120 bpm on the exercise bike) are entered into the device and sent to the server.

[0412] Step 3:

[0413] The server analyzes the user's physical information and fitness assessment results, and uses a generative AI model to generate an optimal training program for the user. The generated program includes specific exercise types, number of sets, number of reps, rest times, and more.

[0414] Step 4:

[0415] The training program generated by the server is sent to the user's terminal, which then displays it to the user. The user then begins training according to the displayed training program.

[0416] Step 5:

[0417] During training, the emotion engine analyzes the user's facial expressions and tone of voice through the device. The emotion data is sent to the server in real time, and the server uses this data to evaluate the user's emotional state.

[0418] Step 6:

[0419] The server adjusts the training program in real time based on the user's emotional state. For example, if the user is feeling stressed, it adds stretches to help them relax. The adjusted program is then sent back to the user's device.

[0420] Step 7:

[0421] After completing a training session, users provide feedback via their device regarding the difficulty level, satisfaction level, and physical condition of the training. The device then sends this feedback to the server.

[0422] Step 8:

[0423] The server analyzes the collected feedback and optimizes the next training program. This allows users to have a more effective training experience.

[0424] 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.

[0425] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0427] [Second Embodiment]

[0428] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0429] 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.

[0430] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0431] 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.

[0432] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0433] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0434] 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.

[0435] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0436] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0437] The 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.

[0438] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0439] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0440] This invention relates to a system that enables users to efficiently and effectively achieve their fitness goals by providing them with individually optimized training programs. The system uses AI to generate training programs based on the user's physical information and fitness assessment results, and adjusts them in real time according to the user's progress.

[0441] System-wide configuration

[0442] This system consists of the following main components:

[0443] 1. User terminal

[0444] 2. Fitness assessment equipment

[0445] 3. Server

[0446] Program Processing Overview

[0447] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals.

[0448] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, the user might input the number of squats performed per minute, the duration of a plank, or their heart rate while using the exercise bike.

[0449] The device transmits the collected user's physical information and fitness assessment results to the server. The server receives this data, analyzes it using an AI algorithm, and generates an optimal training program for the user. This program details appropriate exercises, sets, reps, rest times, and more.

[0450] The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. During training, the terminal records the user's progress in real time and provides instructions and feedback as needed.

[0451] After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into a device. The device sends this feedback to a server, which analyzes it to optimize the next training program. For example, the server might decide whether the user should increase the number of squat sets or add exercises to improve endurance.

[0452] Specific example

[0453] Day 1

[0454] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[0455] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0456] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0457] Terminal: Sends these evaluation results to the server.

[0458] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0459] Terminal: Displays the generated training program.

[0460] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[0461] Terminal: Sends feedback to the server.

[0462] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[0463] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[0467] Step 2:

[0468] Terminal: The terminal sends the profile information entered by the user to the server. The server then receives the user's basic information and registers the profile.

[0469] Step 3:

[0470] Device: Encourage the user to complete an initial fitness assessment. Specifically, instruct them to perform basic exercises such as squats, planks, and stationary bike exercises, and to input the results. For example, present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the stationary bike."

[0471] Step 4:

[0472] User: Perform the instructed evaluation and enter the results into the terminal. For example, enter results such as "15 squats, 30 seconds plank, exercise bike heart rate 120".

[0473] Step 5:

[0474] The device sends the user's fitness assessment results to the server. The server then receives the user's detailed fitness data.

[0475] Step 6:

[0476] Server: Based on the received user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data. Based on the analysis, it generates an optimal training program for the user. This program includes specific exercise types, sets, reps, rest times, and more.

[0477] Step 7:

[0478] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[0479] Step 8:

[0480] User: Follow the program displayed on your device or smartphone to perform the workout. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[0481] Step 9:

[0482] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[0483] Step 10:

[0484] User: After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might provide feedback such as, "The squats were a little too easy," or "The exercise bike was appropriate."

[0485] Step 11:

[0486] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[0487] Step 12:

[0488] Server: Analyzes the received feedback and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or slightly shorten the rest time.

[0489] Step 13:

[0490] Server: Sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[0491] By repeating the above steps, users can always train efficiently with a training program optimized for them. This allows users to achieve their fitness goals more effectively and efficiently.

[0492] (Example 1)

[0493] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0494] Traditional training systems have struggled to provide optimal training programs tailored to each user's individual fitness level and goals. Furthermore, they lacked the ability to instantly reflect user training progress and feedback, and adjust training programs in real time. As a result, they provided insufficient support for efficiently and effectively achieving fitness goals.

[0495] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0496] In this invention, the server includes means for inputting the user's biometric information, means for performing an evaluation of the user's exercise, and means for transmitting the user's biometric information and exercise evaluation results to the server. This makes it possible to generate individually optimized training programs and adjust the training programs in real time by immediately reflecting the user's progress and feedback.

[0497] "Biometric information" refers to data that shows a user's personal physical characteristics, such as age, gender, height, and weight.

[0498] "Exercise evaluation" refers to measuring and evaluating the results of exercise performed by a user, and includes test results for squats, planks, and exercise bikes.

[0499] A "server" is a device or system that receives a user's biometric information and exercise evaluation results, generates a training program based on that data, and performs analysis including feedback.

[0500] A "generative AI model" refers to an artificial intelligence algorithm used to analyze collected user data and generate the optimal training program.

[0501] A "training program" is a detailed plan that outlines the type of exercises a user should perform, the number of sets, the number of repetitions, rest periods, and other specific details.

[0502] "Feedback" refers to opinions and evaluations that users provide after a training session regarding the difficulty level of the training, satisfaction level, physical condition, etc.

[0503] A "calculating device" refers to a device used by users to input information, and includes smartphones, tablets, and dedicated terminals at gyms.

[0504] This invention relates to a system that provides users with individually optimized training programs to efficiently achieve their fitness goals. Specific embodiments for carrying out the invention are described below.

[0505] System Configuration

[0506] This system consists of the following main components:

[0507] 1. User devices (e.g., smartphones, tablets)

[0508] 2. Fitness assessment equipment (e.g., exercise bike ergometer, squat sensor)

[0509] 3. Server (Cloud Server)

[0510] Program processing flow

[0511] 1. Upon arriving at the gym, users log in to the system using their smartphone or a gym terminal and enter their profile information. This includes age, gender, height, weight, fitness level, and goals. For example, information such as 30 years old, male, 175cm tall, 70kg in weight, beginner fitness level, and goal of increasing muscle strength might be entered.

[0512] 2. The device prompts the user to perform an initial fitness assessment. This includes basic exercises such as squats, planks, and using an exercise bike ergometer, and the results are entered into the device. For example, the user might enter how many squats they performed per minute or how long they were able to hold a plank. For instance, they might enter results such as 15 squats in one minute, a 30-second plank, and a heart rate of 120 on the exercise bike ergometer.

[0513] 3. The device sends the collected user biometric information and exercise evaluation results to the server. For example, it sends data such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TensorFlow or PyTorch). The server generates an optimal training program based on the user's characteristics. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0514] 4. The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. The terminal records the user's progress in real time during training and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats."

[0515] 5. After completing the training session, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might enter feedback such as, "The number of squat sets was a little too easy." The device sends this feedback to the server. The server analyzes the feedback and optimizes the next training program. For example, adjustments might be made, such as increasing the number of squat sets or adding exercises to improve endurance.

[0516] Specific example

[0517] Day 1

[0518] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[0519] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0520] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0521] Terminal: Sends these evaluation results to the server.

[0522] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0523] Terminal: Displays the generated training program.

[0524] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[0525] Terminal: Sends feedback to the server.

[0526] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[0527] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[0528] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0529] Step 1:

[0530] Upon arriving at the gym, users log into the system using a terminal or their smartphone and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. For example, the input might include data such as "30 years old, male, 175cm tall, 70kg weight, beginner fitness level, goal: muscle improvement." This data is received by the terminal and stored as the user's basic physical information.

[0531] Step 2:

[0532] The device prompts the user to perform an initial fitness assessment. This assessment includes exercises such as squats, planks, and exercise bike ergometer exercises. The user performs these exercises and inputs the results into the device. For example, the user might input data such as 15 squats per minute, a 30-second plank, and a heart rate of 120 on the exercise bike. This data is collected by the device and stored as the user's exercise assessment result.

[0533] Step 3:

[0534] The device sends the collected user biometric information and exercise evaluation results to the server. Input includes evaluation results such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TensorFlow or PyTorch). Specifically, it generates an optimal training program based on the user's physical information and exercise evaluation results. This analysis result becomes the server's output.

[0535] Step 4:

[0536] The server sends the generated training program to the terminal or the user's smartphone. The output training program might include, for example, 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks. This program is then displayed on the terminal.

[0537] Step 5:

[0538] The user performs the training according to the displayed training program. During training, the device records the user's progress in real time and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats." Data regarding the user's progress is collected by the device.

[0539] Step 6:

[0540] After completing a workout, users input feedback on the difficulty level, satisfaction level, and physical condition into their device. Specific inputs may include comments such as, "The number of squat sets was a little too easy." This feedback data is received by the device and sent to the server.

[0541] Step 7:

[0542] The server analyzes the received feedback and optimizes the next training program. Specifically, it adjusts the content of the user's training program (number of sets, reps, exercise types, etc.) based on the feedback. For example, it might increase the number of squat sets or add exercises to improve endurance. This optimized training program becomes the server's output and is provided to the user in the next training session.

[0543] (Application Example 1)

[0544] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0545] Traditional fitness systems have struggled to provide personalized training programs in real time, tailored to each user's unique physical characteristics and fitness level. Furthermore, they lacked sufficient means to monitor training progress in real time and provide appropriate feedback. As a result, it was difficult for users to efficiently and effectively achieve their fitness goals.

[0546] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0547] In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user using a smart wearable device or head-mounted display, means for transmitting the user's physical information and fitness assessment results to the server, means for generating a training program using a generative AI model, means for providing the training program to the user, means for monitoring the user's progress in real time and providing feedback through a smart wearable device or head-mounted display, and means for collecting the user's training feedback and optimizing the training program. This enables the generation of individually optimized training programs and the provision of real-time progress monitoring and feedback.

[0548] "User physical information" refers to data such as the user's age, gender, height, weight, fitness level, and fitness goals.

[0549] A "smart wearable device" is an electronic device that collects a user's biometric information and can monitor their training progress in real time. Examples include smartwatches and heart rate monitors.

[0550] A "head-mounted display" is a display device worn by a user that visually displays training programs and feedback. This includes, for example, AR (augmented reality) and VR (virtual reality) devices.

[0551] A "fitness assessment" is a process that measures a user's current fitness level through basic exercises such as squats, planks, and stationary bikes.

[0552] A "server" is a centralized computer system that receives users' physical information and fitness assessment results, and generates and optimizes training programs using a generated AI model.

[0553] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and automatically generates and adjusts the optimal training program based on that analysis.

[0554] A "training program" is a plan that includes a set of exercises individually optimized for the user to achieve their fitness goals, along with instructions regarding the number of sets, repetitions, rest times, and other related details.

[0555] "Real-time monitoring" refers to the ability to instantly monitor a user's progress and physical condition during training, and to provide immediate adjustments and feedback as needed.

[0556] "Feedback" refers to information used to modify and optimize training programs based on the user's experience with training, including difficulty level, satisfaction level, and physical condition.

[0557] "Optimization" is the process of adjusting a training program to best suit the user's fitness goals based on their past data and feedback.

[0558] This invention relates to a personal fitness system using smart wearable devices and head-mounted displays (HMDs) that can be used by users in fitness facilities. The embodiments for carrying out this invention are described in detail below.

[0559] Overall system configuration

[0560] This system consists of a user terminal, a smart wearable device or head-mounted display (HMD), fitness assessment equipment, and a server.

[0561] Program Processing Overview

[0562] First, upon arriving at the fitness facility, users put on smart glasses or HMDs and enter their profile information. This includes age, gender, height, weight, fitness level, and goals.

[0563] Next, the user performs an initial fitness assessment using a smart wearable device or HMD. For example, they perform squats, planks, or use an exercise bike, and the results are automatically collected via sensors. The collected data is then transmitted to a server via the user's device.

[0564] The server generates a training program using an AI model based on the received user's physical information and fitness assessment results. The generated training program is sent to a smart wearable device or HMD and presented to the user visually.

[0565] While the user is training, smart wearable devices and HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, instructions such as "Improve your squat form" or "You have 10 seconds until the next set" may be displayed.

[0566] After completing a training session, users provide feedback through smart wearable devices or HMDs. For example, they can input feedback on the difficulty level of the training, satisfaction, and their physical condition.

[0567] The server analyzes the collected feedback to optimize the next training program. This process ensures that users always receive an optimized training program, allowing them to achieve their fitness goals efficiently and effectively.

[0568] Specific example

[0569] Initial Setup

[0570] User: 30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement. Log in wearing smart glasses and enter profile information.

[0571] Smart glasses: As an initial evaluation, we suggest testing them during squats, planks, and exercise bike workouts.

[0572] User: Obtained results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0573] Smart glasses: These evaluation results are sent to the server.

[0574] Server: Analyzes data to generate the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0575] Smart glasses: Display the generated training program.

[0576] Training Session

[0577] User: Conduct training according to the proposed program.

[0578] Smart glasses: Monitor progress in real time and provide feedback such as "Improve your squat form."

[0579] User: After completing the training, provide feedback on difficulty level, satisfaction, and physical condition.

[0580] Smart glasses: Input feedback is sent to the server.

[0581] Server: Analyzes feedback and optimizes the next training program.

[0582] Example of a prompt

[0583] "Please generate an optimized training program for a 30-year-old male, 175cm tall, weighing 70kg, aiming for beginner-level strength improvement."

[0584] "You are a 30-year-old male aiming to improve your strength. Initial assessments include 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm on an exercise bike. Please recommend an optimal training program."

[0585] In this way, the system of the present invention effectively supports users in achieving their fitness goals.

[0586] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0587] Step 1:

[0588] When a user arrives at a fitness facility, they put on smart glasses or a head-mounted display (HMD) and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. This provides the user with basic physical information.

[0589] Input: Age, Gender, Height, Weight, Fitness Level, Goals

[0590] Output: User's basic physical information

[0591] Step 2:

[0592] Using smart glasses or an HMD, users perform an initial fitness assessment. As users perform exercises such as squats, planks, and exercise bike rides, the results are automatically collected via sensors. These assessment results serve as a baseline for the user's initial training.

[0593] Input: User's exercise results (e.g., squats, planks, exercise bike)

[0594] Output: Fitness evaluation results (e.g., number of squats, duration of plank, heart rate on exercise bike)

[0595] Step 3:

[0596] The terminal sends the collected user's physical information and fitness assessment results to the server. The server receives this data and performs preprocessing.

[0597] Input: User's basic physical information, fitness assessment results

[0598] Output: User information and evaluation results stored in the database

[0599] Step 4:

[0600] The server uses a generative AI model to generate training programs based on user data. This AI model analyzes user data to determine the optimal type of exercise, number of sets, number of reps, and rest time.

[0601] Input: User information, fitness assessment results

[0602] Output: Optimized training program (e.g., 20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds plank x 3 sets)

[0603] Step 5:

[0604] The generated training program is sent from the server to smart glasses or an HMD and presented visually to the user. The user then performs the training according to this program.

[0605] Input: Optimized training program

[0606] Output: Training program displayed on smart glasses or HMD.

[0607] Step 6:

[0608] While the user is training, smart glasses or HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, they might display instructions such as, "Improve your squat form" or "You have 10 seconds until the next set begins."

[0609] Input: User training progress

[0610] Output: Real-time feedback and instructions

[0611] Step 7:

[0612] After completing the training, users provide feedback through smart glasses or HMDs. This feedback includes information about the difficulty level of the training, satisfaction level, and physical condition.

[0613] Input: User feedback (information on difficulty level, satisfaction level, and physical condition)

[0614] Output: Feedback data

[0615] Step 8:

[0616] The device sends the collected feedback data to the server. The server analyzes this data and optimizes the next training program.

[0617] Input: User feedback data

[0618] Output: Optimized next training program

[0619] This series of processing steps ensures that users always receive a individually optimized training program, allowing them to effectively achieve their fitness goals.

[0620] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0621] This invention relates to a system that provides a user with an optimized training program and further adjusts that program based on the user's emotional state. This system not only generates an optimal training program based on the user's physical information and fitness assessment results, but also acquires the user's emotional data using an emotion engine and reflects it in the training content, thereby providing a more effective training experience.

[0622] System-wide configuration

[0623] This system consists of the following main components:

[0624] 1. User terminal

[0625] 2. Fitness assessment equipment

[0626] 3. Server

[0627] 4. Emotional Engine

[0628] Program Processing Overview

[0629] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: muscle improvement."

[0630] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, it might present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the exercise bike."

[0631] The user performs the instructed evaluation and enters the results into the device. For example, they might enter results such as "15 squats, 30 seconds of plank, and a heart rate of 120 on the exercise bike."

[0632] These evaluation results and profile information are sent from the device to the server. The server uses an AI algorithm to analyze this data and generate an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[0633] The server sends the generated training program to the terminal or the user's smartphone. This allows the user to review their training program.

[0634] Users perform their workouts according to the displayed program. For example, they might follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[0635] Furthermore, this system has an emotion engine that acquires the user's emotional data. The emotion engine recognizes the user's emotional state (e.g., stress level, motivation level) by analyzing the user's facial expressions and tone of voice. During training, the emotion engine collects the user's emotional data in real time and sends it to the server.

[0636] The server adjusts the training program in real time based on emotional data and training progress. For example, if a user is feeling stressed, it may add stretching exercises to help them relax.

[0637] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The device sends this feedback to a server, which is used to optimize the next training program.

[0638] Specific example

[0639] Day 1

[0640] User: Arrives at the gym, logs in on the device, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle improvement).

[0641] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0642] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[0643] Terminal: Sends these evaluation results to the server.

[0644] Server: Analyzes data and generates an optimal training program. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0645] Terminal: Displays the generated training program.

[0646] User: Follow the program to complete the training.

[0647] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[0648] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[0649] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[0650] Terminal: Sends feedback to the server.

[0651] Server: Analyzes the feedback and adjusts the next training program (e.g., increases the number of squat sets).

[0652] By repeating this process, users can always train efficiently with a training program optimized for them. Combining this with an emotional engine allows for training tailored to the user's emotional state, resulting in an even more effective training experience.

[0653] The following describes the processing flow.

[0654] Step 1:

[0655] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[0656] Step 2:

[0657] Terminal: Sends the profile information entered by the user to the server. The server saves the received data and registers it as the user profile.

[0658] Step 3:

[0659] Device: Prompts the user to perform an initial fitness assessment. For example, assessment items such as squats, planks, and heart rate measurement while using an exercise bike are displayed.

[0660] Step 4:

[0661] User: Perform the initial assessment displayed on the screen and enter the results into the device. For example, enter "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120".

[0662] Step 5:

[0663] The device sends the user's fitness assessment results to the server. The server then receives detailed fitness data.

[0664] Step 6:

[0665] Server: Based on the user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data and generate an optimal training program for the user. This program includes detailed information such as the type of exercise, number of sets, number of reps, and rest time.

[0666] Step 7:

[0667] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[0668] Step 8:

[0669] User: Follow the training program displayed on your device or smartphone. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks."

[0670] Step 9:

[0671] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[0672] Step 10:

[0673] Emotion Engine: During training, it analyzes the user's facial expressions and tone of voice to recognize emotions. For example, it analyzes the user's facial expressions when they are facing the camera and sends that data to the server.

[0674] Step 11:

[0675] Server: Based on emotional data and training progress data, the server adjusts the training program in real time. For example, if the user is feeling tired or stressed, it adds stretching exercises to the program to help them relax.

[0676] Step 12:

[0677] User: After completing a workout, enter feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, enter feedback such as "The squats were a little too easy" or "The exercise bike was appropriate."

[0678] Step 13:

[0679] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[0680] Step 14:

[0681] Server: Analyzes the received feedback data and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or shorten rest periods.

[0682] Step 15:

[0683] Server: The server sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[0684] By repeating the above steps, users can always train efficiently with a training program optimized for them. Furthermore, by using an emotion engine, the system provides training programs tailored to the user's emotional state, resulting in a more effective training experience.

[0685] (Example 2)

[0686] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0687] Traditional exercise programs are generated once based on the user's physical information and initial fitness assessment, but they do not take into account the user's emotional state during or after exercise. This makes it difficult to maintain user motivation and achieve effective training. Furthermore, because real-time adjustments are not possible, a program that has been set may become unsuitable for the user.

[0688] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0689] In this invention, the server includes means for receiving and storing the user's biometric information and physical fitness assessment results, means for generating an exercise plan based on the received information, means for recognizing the user's emotional state and collecting data, and means for adjusting and optimizing the exercise plan in real time based on the emotional data and post-exercise feedback. This enables the provision of an optimal exercise plan that takes the user's emotional state into consideration and real-time adjustment of the training program.

[0690] "Biometric information" refers to basic physical data such as the user's age, gender, height, and weight.

[0691] "Physical fitness assessment" refers to the results of exercises such as squats, planks, and stationary bike rides performed to measure the user's fitness level.

[0692] The term "central processing unit" refers to a computer device that analyzes the user's biometric information and physical fitness assessment results, and generates and adjusts exercise plans.

[0693] An "exercise plan" refers to a program that includes specific exercise instructions for the user, such as the type of training, number of sets, number of reps, and rest time that is best suited to that user.

[0694] "Emotional state" refers to a psychological state such as stress level and motivation level, which can be determined from the user's facial expressions and tone of voice.

[0695] "Emotional data" refers to data collected as numerical or textual information by analyzing the emotional state of users.

[0696] "Real-time adjustment" refers to the process of instantly optimizing the exercise plan based on the user's current emotional state and training progress.

[0697] "Feedback" refers to the opinions and evaluations that users provide regarding the difficulty level, satisfaction level, and physical condition of their training.

[0698] The system of this invention provides the user with an optimized exercise plan and further adjusts the exercise plan in real time based on their emotional state. This system mainly includes a user terminal, fitness evaluation equipment, a server, and an emotion engine.

[0699] Hardware and software to be used

[0700] User terminal

[0701] The user terminal is a device used to collect biometric information and fitness assessment results entered by the user. Specific examples include smartphones and tablets. Users enter data such as their age, gender, height, weight, fitness level, and goals into this terminal. For example, they might enter: "Age: 30, Gender: Male, Height: 175cm, Weight: 70kg, Beginner, Goal: Muscle Strength Improvement."

[0702] Fitness assessment equipment

[0703] Fitness assessment equipment is used to measure a user's physical fitness level. Specific examples include squat counter plates, plank timers, and exercise bike ergometers. These devices measure the results of the exercises performed by the user and input them into a terminal. For example, the user might input results such as "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120."

[0704] server

[0705] The server receives biometric information and fitness assessment results sent from the user's terminal and analyzes them using an AI algorithm. The server implements an AI algorithm using Python and TensorFlow, which is used to generate an optimal exercise plan. The generated exercise plan includes details such as the type of exercise, number of sets, number of repetitions, and rest time. The server then sends the generated exercise plan to the user's terminal.

[0706] Emotional Engine

[0707] The emotion engine is a system for recognizing and collecting data on a user's emotional state. It utilizes facial expression and voice analysis software (such as OpenCV or Watson Tone Analyzer) using cameras and microphones. This engine analyzes the user's facial expressions and voice tone, collecting emotional data such as stress levels and motivation levels. The collected emotional data is transmitted to a server in real time and used to adjust the exercise plan.

[0708] Specific example

[0709] Profile Information Input Example

[0710] The user enters the following on their smartphone:

[0711] Age: 30, Gender: Male, Height: 175cm, Weight: 70kg, Fitness Level: Beginner, Goal: Strength Improvement

[0712] Fitness assessment input example

[0713] The user enters the results of their fitness assessment as follows:

[0714] "Squats: 15 reps per minute, Plank: 30 seconds, Exercise bike heart rate: 120"

[0715] Training program display example

[0716] The motion plan generated by the server will be displayed as follows:

[0717] "Training program: 20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks"

[0718] Feedback input example

[0719] The user provides the following feedback after completing their workout:

[0720] "Feedback: The squats were a little too easy."

[0721] Thus, by using this system, users can always train efficiently with an exercise plan optimized for them. Furthermore, by introducing an emotion engine, real-time adjustments can be made according to the user's emotional state, maximizing the effectiveness of the training.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] Upon arriving at the gym, users log in to the system using their personal device or smartphone and enter their profile information. Specifically, they enter basic data such as age, gender, height, weight, fitness level, and goals.

[0725] Input: User profile information

[0726] Output: Profile data entered into the device

[0727] Step 2:

[0728] The device prompts the user to perform an initial fitness assessment. This involves basic exercises such as squats, planks, and using an exercise bike. The user then inputs the results into the device.

[0729] Specific examples of exercises include "doing as many squats as possible in one minute," "holding a plank for as long as possible," and "measuring your heart rate while using an exercise bike."

[0730] Input: User's fitness assessment results

[0731] Output: Fitness evaluation data entered into the terminal

[0732] Step 3:

[0733] The device sends the collected profile information and fitness evaluation results to the server. Here, the device bundles the data into packets and sends them to the server.

[0734] Input: Profile information and fitness evaluation results entered on the device.

[0735] Output: Integrated data sent to the server

[0736] Step 4:

[0737] The server analyzes the received data using AI algorithms (utilizing Python and TensorFlow) to generate an optimal exercise plan for the user. The analysis includes the type of exercise, number of sets, number of reps, and rest time based on the user's fitness level and goals.

[0738] Input: Integrated data sent to the server

[0739] Output: Generated motion plan

[0740] Step 5:

[0741] The server sends the generated exercise plan to the terminal. The terminal displays the received exercise plan to the user. The user can then check the detailed exercise plan on the terminal.

[0742] In terms of specific actions, a detailed exercise plan is displayed on the user's device, such as "20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds of plank x 3 sets."

[0743] Input: Exercise plan generated by the server

[0744] Output: Exercise plan displayed on the terminal

[0745] Step 6:

[0746] The user performs the training according to the displayed exercise plan.

[0747] Specifically, the user will use an exercise bike for 20 minutes, followed by squats and planks for a specified number of repetitions and sets.

[0748] Input: Exercise plan displayed on the device

[0749] Output: Actual training by users

[0750] Step 7:

[0751] During training, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. It collects user emotional data using cameras and microphones and transmits it to the server in real time.

[0752] Specifically, we use facial recognition software (e.g., OpenCV) and speech analysis software (Watson Tone Analyzer).

[0753] Input: User's emotional data (facial expressions and tone of voice)

[0754] Output: Sentiment data sent to the server

[0755] Step 8:

[0756] The server analyzes the received emotional data and adjusts the exercise plan in real time as needed. If the user is feeling stressed, it will make adjustments such as adding stretches to help them relax.

[0757] Input: Emotional data sent to the server

[0758] Output: Adjusted exercise plan

[0759] Step 9:

[0760] After completing a workout, users enter feedback into a device regarding the difficulty level, satisfaction level, and their physical condition. This feedback includes information about how effective or difficult the exercise was.

[0761] Input: User feedback (impressions and evaluations after exercise)

[0762] Output: Feedback data entered into the terminal

[0763] Step 10:

[0764] The device sends feedback to the server. The server analyzes the user's feedback and optimizes the next exercise plan.

[0765] In terms of specific actions, adjustments such as increasing the number of sets in the next squat session will be made.

[0766] Input: Feedback data entered into the device

[0767] Output: Optimization of the next exercise plan

[0768] (Application Example 2)

[0769] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0770] Conventional autonomous vehicles have difficulty considering the driver's emotional state and stress level, which can result in insufficient driver comfort and safety. Furthermore, the lack of real-time driving mode changes or entertainment adjustments based on emotional state means that driver stress and fatigue during long drives cannot be reduced.

[0771] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user, means for collecting the user's emotional data and analyzing the emotional state in real time, and means for generating a training program that reflects the user's emotional state based on the emotional data. This enables real-time adjustments based on the emotional state, thereby improving the driver's comfort and safety.

[0772] "Means of inputting user physical information" refer to devices or applications that acquire and input basic physical information such as the user's age, gender, height, and weight.

[0773] "Means for conducting user fitness assessments" refers to devices and methods for evaluating a user's physical strength and athletic ability through fitness activities such as squats, planks, and exercise bikes.

[0774] A "server" is a computing system that analyzes a user's physical information, fitness assessment results, and emotional data to generate an optimal training program.

[0775] "Means for generating training programs" refers to algorithms and software that generate exercise programs tailored to the user's fitness goals based on data collected by the server.

[0776] "Means of providing to the user" refers to devices or applications used to display the generated training program to the user or to provide instructions.

[0777] "Means for collecting user training feedback and optimizing training programs" refers to devices or applications that collect feedback information provided by users after completing training and adjust the next training program based on that information.

[0778] "Means for collecting user emotional data and analyzing emotional states in real time" refers to devices and software that use cameras and microphones to analyze users' facial expressions and voice tone, and evaluate their emotional state in real time.

[0779] "Means for generating training programs that reflect a user's emotional state based on emotional data" refers to algorithms or software that take into account a user's emotional state and adjust the content and methods of training accordingly.

[0780] This invention relates to a system that provides a training program optimized for the user by inputting the user's physical information and fitness evaluation results, and also taking into account emotional data. Specific embodiments of this system are described below.

[0781] System-wide configuration

[0782] This system consists of the following main components:

[0783] 1. User terminal: A device used to input the user's physical information and fitness assessment. For example, a smartphone or tablet may be used.

[0784] 2. Fitness evaluation equipment: This equipment is used to measure the results of exercises such as squats, planks, and exercise bikes.

[0785] 3. Server: This is the central computing system that analyzes user input data, evaluation results, and sentiment data to generate the optimal training program.

[0786] 4. Emotion Engine: This is software that analyzes the user's facial expressions and voice tone to evaluate their emotional state in real time. Specific examples include emotion recognition models using OpenCV or Keras.

[0787] Program Processing Overview

[0788] First, the user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). This information is then sent from the user's device to the server.

[0789] Next, the user performs tests such as squats, planks, and exercise bikes using fitness evaluation equipment and enters the results into a terminal. These evaluation results are also sent to the server.

[0790] Based on this data, the server uses a generated AI model to create an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[0791] The generated training program is sent from the server to the user's terminal, and the user begins training according to it.

[0792] During training, the emotion engine analyzes the user's facial expressions and tone of voice in real time and sends emotional data to the server. This allows the server to adjust the training program in real time, taking the user's emotional state into consideration. For example, if the user is feeling stressed, adjustments such as adding stretches to help them relax may be made.

[0793] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The server analyzes this feedback to optimize the next training program.

[0794] Specific example

[0795] Example 1

[0796] User: Arrives at the gym, logs into the system on their smartphone, and enters their profile information (e.g., 30 years old, male, 175cm tall, 70kg weight).

[0797] User terminal: As an initial evaluation, we suggest testing squats, planks, and an exercise bike.

[0798] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[0799] Server: Based on these evaluation results, it generates an optimal training program (e.g., 20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks).

[0800] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[0801] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[0802] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[0803] Example of a prompt

[0804] "What kind of music would you play if the driver of this vehicle was tired?"

[0805] "When the driver's emotional state is stressed, please tell me the appropriate driving mode."

[0806] By specifically demonstrating the embodiments for carrying out the invention in this way, it is possible to provide users with an optimal training experience and improve driver comfort and safety.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). The entered data is sent from the device to the server. This initiates the processing of the user's data.

[0810] Step 2:

[0811] Users perform tests such as squats, planks, and exercise bike workouts using fitness assessment equipment. The test results (e.g., 15 squats in 1 minute, 30-second plank, and a heart rate of 120 bpm on the exercise bike) are entered into the device and sent to the server.

[0812] Step 3:

[0813] The server analyzes the user's physical information and fitness assessment results, and uses a generative AI model to generate an optimal training program for the user. The generated program includes specific exercise types, number of sets, number of reps, rest times, and more.

[0814] Step 4:

[0815] The training program generated by the server is sent to the user's terminal, which then displays it to the user. The user then begins training according to the displayed training program.

[0816] Step 5:

[0817] During training, the emotion engine analyzes the user's facial expressions and tone of voice through the device. The emotion data is sent to the server in real time, and the server uses this data to evaluate the user's emotional state.

[0818] Step 6:

[0819] The server adjusts the training program in real time based on the user's emotional state. For example, if the user is feeling stressed, it adds stretches to help them relax. The adjusted program is then sent back to the user's device.

[0820] Step 7:

[0821] After completing a training session, users provide feedback via their device regarding the difficulty level, satisfaction level, and physical condition of the training. The device then sends this feedback to the server.

[0822] Step 8:

[0823] The server analyzes the collected feedback and optimizes the next training program. This allows users to have a more effective training experience.

[0824] 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.

[0825] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0826] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0827] [Third Embodiment]

[0828] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0829] 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.

[0830] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0831] 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.

[0832] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0833] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0834] 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.

[0835] 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.

[0836] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0837] The 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.

[0838] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0839] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0840] This invention relates to a system that enables users to efficiently and effectively achieve their fitness goals by providing them with individually optimized training programs. The system uses AI to generate training programs based on the user's physical information and fitness assessment results, and adjusts them in real time according to the user's progress.

[0841] System-wide configuration

[0842] This system consists of the following main components:

[0843] 1. User terminal

[0844] 2. Fitness assessment equipment

[0845] 3. Server

[0846] Program Processing Overview

[0847] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals.

[0848] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, the user might input the number of squats performed per minute, the duration of a plank, or their heart rate while using the exercise bike.

[0849] The device transmits the collected user's physical information and fitness assessment results to the server. The server receives this data, analyzes it using an AI algorithm, and generates an optimal training program for the user. This program details appropriate exercises, sets, reps, rest times, and more.

[0850] The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. During training, the terminal records the user's progress in real time and provides instructions and feedback as needed.

[0851] After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into a device. The device sends this feedback to a server, which analyzes it to optimize the next training program. For example, the server might decide whether the user should increase the number of squat sets or add exercises to improve endurance.

[0852] Specific example

[0853] Day 1

[0854] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[0855] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0856] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0857] Terminal: Sends these evaluation results to the server.

[0858] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0859] Terminal: Displays the generated training program.

[0860] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[0861] Terminal: Sends feedback to the server.

[0862] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[0863] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[0864] The following describes the processing flow.

[0865] Step 1:

[0866] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[0867] Step 2:

[0868] Terminal: The terminal sends the profile information entered by the user to the server. The server then receives the user's basic information and registers the profile.

[0869] Step 3:

[0870] Device: Encourage the user to complete an initial fitness assessment. Specifically, instruct them to perform basic exercises such as squats, planks, and stationary bike exercises, and to input the results. For example, present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the stationary bike."

[0871] Step 4:

[0872] User: Perform the instructed evaluation and enter the results into the terminal. For example, enter results such as "15 squats, 30 seconds plank, exercise bike heart rate 120".

[0873] Step 5:

[0874] The device sends the user's fitness assessment results to the server. The server then receives the user's detailed fitness data.

[0875] Step 6:

[0876] Server: Based on the received user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data. Based on the analysis, it generates an optimal training program for the user. This program includes specific exercise types, sets, reps, rest times, and more.

[0877] Step 7:

[0878] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[0879] Step 8:

[0880] User: Follow the program displayed on your device or smartphone to perform the workout. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[0881] Step 9:

[0882] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[0883] Step 10:

[0884] User: After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might provide feedback such as, "The squats were a little too easy," or "The exercise bike was appropriate."

[0885] Step 11:

[0886] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[0887] Step 12:

[0888] Server: Analyzes the received feedback and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or slightly shorten the rest time.

[0889] Step 13:

[0890] Server: Sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[0891] By repeating the above steps, users can always train efficiently with a training program optimized for them. This allows users to achieve their fitness goals more effectively and efficiently.

[0892] (Example 1)

[0893] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0894] Traditional training systems have struggled to provide optimal training programs tailored to each user's individual fitness level and goals. Furthermore, they lacked the ability to instantly reflect user training progress and feedback, and adjust training programs in real time. As a result, they provided insufficient support for efficiently and effectively achieving fitness goals.

[0895] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0896] In this invention, the server includes means for inputting the user's biometric information, means for performing an evaluation of the user's exercise, and means for transmitting the user's biometric information and exercise evaluation results to the server. This makes it possible to generate individually optimized training programs and adjust the training programs in real time by immediately reflecting the user's progress and feedback.

[0897] "Biometric information" refers to data that shows a user's personal physical characteristics, such as age, gender, height, and weight.

[0898] "Exercise evaluation" refers to measuring and evaluating the results of exercise performed by a user, and includes test results for squats, planks, and exercise bikes.

[0899] A "server" is a device or system that receives a user's biometric information and exercise evaluation results, generates a training program based on that data, and performs analysis including feedback.

[0900] A "generative AI model" refers to an artificial intelligence algorithm used to analyze collected user data and generate the optimal training program.

[0901] A "training program" is a detailed plan that outlines the type of exercises a user should perform, the number of sets, the number of repetitions, rest periods, and other specific details.

[0902] "Feedback" refers to opinions and evaluations that users provide after a training session regarding the difficulty level of the training, satisfaction level, physical condition, etc.

[0903] A "calculating device" refers to a device used by users to input information, and includes smartphones, tablets, and dedicated terminals at gyms.

[0904] This invention relates to a system that provides users with individually optimized training programs to efficiently achieve their fitness goals. Specific embodiments for carrying out the invention are described below.

[0905] System Configuration

[0906] This system consists of the following main components:

[0907] 1. User devices (e.g., smartphones, tablets)

[0908] 2. Fitness assessment equipment (e.g., exercise bike ergometer, squat sensor)

[0909] 3. Server (Cloud Server)

[0910] Program processing flow

[0911] 1. Upon arriving at the gym, users log in to the system using their smartphone or a gym terminal and enter their profile information. This includes age, gender, height, weight, fitness level, and goals. For example, information such as 30 years old, male, 175cm tall, 70kg in weight, beginner fitness level, and goal of increasing muscle strength might be entered.

[0912] 2. The device prompts the user to perform an initial fitness assessment. This includes basic exercises such as squats, planks, and using an exercise bike ergometer, and the results are entered into the device. For example, the user might enter how many squats they performed per minute or how long they were able to hold a plank. For instance, they might enter results such as 15 squats in one minute, a 30-second plank, and a heart rate of 120 on the exercise bike ergometer.

[0913] 3. The device sends the collected user biometric information and exercise evaluation results to the server. For example, it sends data such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TensorFlow or PyTorch). The server generates an optimal training program based on the user's characteristics. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0914] 4. The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. The terminal records the user's progress in real time during training and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats."

[0915] 5. After completing the training session, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might enter feedback such as, "The number of squat sets was a little too easy." The device sends this feedback to the server. The server analyzes the feedback and optimizes the next training program. For example, adjustments might be made, such as increasing the number of squat sets or adding exercises to improve endurance.

[0916] Specific example

[0917] Day 1

[0918] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[0919] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[0920] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0921] Terminal: Sends these evaluation results to the server.

[0922] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0923] Terminal: Displays the generated training program.

[0924] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[0925] Terminal: Sends feedback to the server.

[0926] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[0927] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[0928] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0929] Step 1:

[0930] Upon arriving at the gym, users log into the system using a terminal or their smartphone and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. For example, the input might include data such as "30 years old, male, 175cm tall, 70kg weight, beginner fitness level, goal: muscle improvement." This data is received by the terminal and stored as the user's basic physical information.

[0931] Step 2:

[0932] The device prompts the user to perform an initial fitness assessment. This assessment includes exercises such as squats, planks, and exercise bike ergometer exercises. The user performs these exercises and inputs the results into the device. For example, the user might input data such as 15 squats per minute, a 30-second plank, and a heart rate of 120 on the exercise bike. This data is collected by the device and stored as the user's exercise assessment result.

[0933] Step 3:

[0934] The device sends the collected user biometric information and exercise evaluation results to the server. Input includes evaluation results such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TensorFlow or PyTorch). Specifically, it generates an optimal training program based on the user's physical information and exercise evaluation results. This analysis result becomes the server's output.

[0935] Step 4:

[0936] The server sends the generated training program to the terminal or the user's smartphone. The output training program might include, for example, 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks. This program is then displayed on the terminal.

[0937] Step 5:

[0938] The user performs the training according to the displayed training program. During training, the device records the user's progress in real time and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats." Data regarding the user's progress is collected by the device.

[0939] Step 6:

[0940] After completing a workout, users input feedback on the difficulty level, satisfaction level, and physical condition into their device. Specific inputs may include comments such as, "The number of squat sets was a little too easy." This feedback data is received by the device and sent to the server.

[0941] Step 7:

[0942] The server analyzes the received feedback and optimizes the next training program. Specifically, it adjusts the content of the user's training program (number of sets, reps, exercise types, etc.) based on the feedback. For example, it might increase the number of squat sets or add exercises to improve endurance. This optimized training program becomes the server's output and is provided to the user in the next training session.

[0943] (Application Example 1)

[0944] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0945] Traditional fitness systems have struggled to provide personalized training programs in real time, tailored to each user's unique physical characteristics and fitness level. Furthermore, they lacked sufficient means to monitor training progress in real time and provide appropriate feedback. As a result, it was difficult for users to efficiently and effectively achieve their fitness goals.

[0946] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0947] In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user using a smart wearable device or head-mounted display, means for transmitting the user's physical information and fitness assessment results to the server, means for generating a training program using a generative AI model, means for providing the training program to the user, means for monitoring the user's progress in real time and providing feedback through a smart wearable device or head-mounted display, and means for collecting the user's training feedback and optimizing the training program. This enables the generation of individually optimized training programs and the provision of real-time progress monitoring and feedback.

[0948] "User physical information" refers to data such as the user's age, gender, height, weight, fitness level, and fitness goals.

[0949] A "smart wearable device" is an electronic device that collects a user's biometric information and can monitor their training progress in real time. Examples include smartwatches and heart rate monitors.

[0950] A "head-mounted display" is a display device worn by a user that visually displays training programs and feedback. This includes, for example, AR (augmented reality) and VR (virtual reality) devices.

[0951] A "fitness assessment" is a process that measures a user's current fitness level through basic exercises such as squats, planks, and stationary bikes.

[0952] A "server" is a centralized computer system that receives users' physical information and fitness assessment results, and generates and optimizes training programs using a generated AI model.

[0953] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and automatically generates and adjusts the optimal training program based on that analysis.

[0954] A "training program" is a plan that includes a set of exercises individually optimized for the user to achieve their fitness goals, along with instructions regarding the number of sets, repetitions, rest times, and other related details.

[0955] "Real-time monitoring" refers to the ability to instantly monitor a user's progress and physical condition during training, and to provide immediate adjustments and feedback as needed.

[0956] "Feedback" refers to information used to modify and optimize training programs based on the user's experience with training, including difficulty level, satisfaction level, and physical condition.

[0957] "Optimization" is the process of adjusting a training program to best suit the user's fitness goals based on their past data and feedback.

[0958] This invention relates to a personal fitness system using smart wearable devices and head-mounted displays (HMDs) that can be used by users in fitness facilities. The embodiments for carrying out this invention are described in detail below.

[0959] Overall system configuration

[0960] This system consists of a user terminal, a smart wearable device or head-mounted display (HMD), fitness assessment equipment, and a server.

[0961] Program Processing Overview

[0962] First, upon arriving at the fitness facility, users put on smart glasses or HMDs and enter their profile information. This includes age, gender, height, weight, fitness level, and goals.

[0963] Next, the user performs an initial fitness assessment using a smart wearable device or HMD. For example, they perform squats, planks, or use an exercise bike, and the results are automatically collected via sensors. The collected data is then transmitted to a server via the user's device.

[0964] The server generates a training program using an AI model based on the received user's physical information and fitness assessment results. The generated training program is sent to a smart wearable device or HMD and presented to the user visually.

[0965] While the user is training, smart wearable devices and HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, instructions such as "Improve your squat form" or "You have 10 seconds until the next set" may be displayed.

[0966] After completing a training session, users provide feedback through smart wearable devices or HMDs. For example, they can input feedback on the difficulty level of the training, satisfaction, and their physical condition.

[0967] The server analyzes the collected feedback to optimize the next training program. This process ensures that users always receive an optimized training program, allowing them to achieve their fitness goals efficiently and effectively.

[0968] Specific example

[0969] Initial Setup

[0970] User: 30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement. Log in wearing smart glasses and enter profile information.

[0971] Smart glasses: As an initial evaluation, we suggest testing them during squats, planks, and exercise bike workouts.

[0972] User: Obtained results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[0973] Smart glasses: These evaluation results are sent to the server.

[0974] Server: Analyzes data to generate the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[0975] Smart glasses: Display the generated training program.

[0976] Training Session

[0977] User: Conduct training according to the proposed program.

[0978] Smart glasses: Monitor progress in real time and provide feedback such as "Improve your squat form."

[0979] User: After completing the training, provide feedback on difficulty level, satisfaction, and physical condition.

[0980] Smart glasses: Input feedback is sent to the server.

[0981] Server: Analyzes feedback and optimizes the next training program.

[0982] Example of a prompt

[0983] "Please generate an optimized training program for a 30-year-old male, 175cm tall, weighing 70kg, aiming for beginner-level strength improvement."

[0984] "You are a 30-year-old male aiming to improve your strength. Initial assessments include 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm on an exercise bike. Please recommend an optimal training program."

[0985] In this way, the system of the present invention effectively supports users in achieving their fitness goals.

[0986] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0987] Step 1:

[0988] When a user arrives at a fitness facility, they put on smart glasses or a head-mounted display (HMD) and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. This provides the user with basic physical information.

[0989] Input: Age, Gender, Height, Weight, Fitness Level, Goals

[0990] Output: User's basic physical information

[0991] Step 2:

[0992] Using smart glasses or an HMD, users perform an initial fitness assessment. As users perform exercises such as squats, planks, and exercise bike rides, the results are automatically collected via sensors. These assessment results serve as a baseline for the user's initial training.

[0993] Input: User's exercise results (e.g., squats, planks, exercise bike)

[0994] Output: Fitness evaluation results (e.g., number of squats, duration of plank, heart rate on exercise bike)

[0995] Step 3:

[0996] The terminal sends the collected user's physical information and fitness assessment results to the server. The server receives this data and performs preprocessing.

[0997] Input: User's basic physical information, fitness assessment results

[0998] Output: User information and evaluation results stored in the database

[0999] Step 4:

[1000] The server uses a generative AI model to generate training programs based on user data. This AI model analyzes user data to determine the optimal type of exercise, number of sets, number of reps, and rest time.

[1001] Input: User information, fitness assessment results

[1002] Output: Optimized training program (e.g., 20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds plank x 3 sets)

[1003] Step 5:

[1004] The generated training program is sent from the server to smart glasses or an HMD and presented visually to the user. The user then performs the training according to this program.

[1005] Input: Optimized training program

[1006] Output: Training program displayed on smart glasses or HMD.

[1007] Step 6:

[1008] While the user is training, smart glasses or HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, they might display instructions such as, "Improve your squat form" or "You have 10 seconds until the next set begins."

[1009] Input: User training progress

[1010] Output: Real-time feedback and instructions

[1011] Step 7:

[1012] After completing the training, users provide feedback through smart glasses or HMDs. This feedback includes information about the difficulty level of the training, satisfaction level, and physical condition.

[1013] Input: User feedback (information on difficulty level, satisfaction level, and physical condition)

[1014] Output: Feedback data

[1015] Step 8:

[1016] The device sends the collected feedback data to the server. The server analyzes this data and optimizes the next training program.

[1017] Input: User feedback data

[1018] Output: Optimized next training program

[1019] This series of processing steps ensures that users always receive a individually optimized training program, allowing them to effectively achieve their fitness goals.

[1020] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1021] This invention relates to a system that provides a user with an optimized training program and further adjusts that program based on the user's emotional state. This system not only generates an optimal training program based on the user's physical information and fitness assessment results, but also acquires the user's emotional data using an emotion engine and reflects it in the training content, thereby providing a more effective training experience.

[1022] System-wide configuration

[1023] This system consists of the following main components:

[1024] 1. User terminal

[1025] 2. Fitness assessment equipment

[1026] 3. Server

[1027] 4. Emotional Engine

[1028] Program Processing Overview

[1029] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: muscle improvement."

[1030] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, it might present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the exercise bike."

[1031] The user performs the instructed evaluation and enters the results into the device. For example, they might enter results such as "15 squats, 30 seconds of plank, and a heart rate of 120 on the exercise bike."

[1032] These evaluation results and profile information are sent from the device to the server. The server uses an AI algorithm to analyze this data and generate an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[1033] The server sends the generated training program to the terminal or the user's smartphone. This allows the user to review their training program.

[1034] Users perform their workouts according to the displayed program. For example, they might follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[1035] Furthermore, this system has an emotion engine that acquires the user's emotional data. The emotion engine recognizes the user's emotional state (e.g., stress level, motivation level) by analyzing the user's facial expressions and tone of voice. During training, the emotion engine collects the user's emotional data in real time and sends it to the server.

[1036] The server adjusts the training program in real time based on emotional data and training progress. For example, if a user is feeling stressed, it may add stretching exercises to help them relax.

[1037] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The device sends this feedback to a server, which is used to optimize the next training program.

[1038] Specific example

[1039] Day 1

[1040] User: Arrives at the gym, logs in on the device, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle improvement).

[1041] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[1042] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[1043] Terminal: Sends these evaluation results to the server.

[1044] Server: Analyzes data and generates an optimal training program. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[1045] Terminal: Displays the generated training program.

[1046] User: Follow the program to complete the training.

[1047] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[1048] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[1049] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[1050] Terminal: Sends feedback to the server.

[1051] Server: Analyzes the feedback and adjusts the next training program (e.g., increases the number of squat sets).

[1052] By repeating this process, users can always train efficiently with a training program optimized for them. Combining this with an emotional engine allows for training tailored to the user's emotional state, resulting in an even more effective training experience.

[1053] The following describes the processing flow.

[1054] Step 1:

[1055] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[1056] Step 2:

[1057] Terminal: Sends the profile information entered by the user to the server. The server saves the received data and registers it as the user profile.

[1058] Step 3:

[1059] Device: Prompts the user to perform an initial fitness assessment. For example, assessment items such as squats, planks, and heart rate measurement while using an exercise bike are displayed.

[1060] Step 4:

[1061] User: Perform the initial assessment displayed on the screen and enter the results into the device. For example, enter "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120".

[1062] Step 5:

[1063] The device sends the user's fitness assessment results to the server. The server then receives detailed fitness data.

[1064] Step 6:

[1065] Server: Based on the user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data and generate an optimal training program for the user. This program includes detailed information such as the type of exercise, number of sets, number of reps, and rest time.

[1066] Step 7:

[1067] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[1068] Step 8:

[1069] User: Follow the training program displayed on your device or smartphone. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks."

[1070] Step 9:

[1071] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[1072] Step 10:

[1073] Emotion Engine: During training, it analyzes the user's facial expressions and tone of voice to recognize emotions. For example, it analyzes the user's facial expressions when they are facing the camera and sends that data to the server.

[1074] Step 11:

[1075] Server: Based on emotional data and training progress data, the server adjusts the training program in real time. For example, if the user is feeling tired or stressed, it adds stretching exercises to the program to help them relax.

[1076] Step 12:

[1077] User: After completing a workout, enter feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, enter feedback such as "The squats were a little too easy" or "The exercise bike was appropriate."

[1078] Step 13:

[1079] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[1080] Step 14:

[1081] Server: Analyzes the received feedback data and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or shorten rest periods.

[1082] Step 15:

[1083] Server: The server sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[1084] By repeating the above steps, users can always train efficiently with a training program optimized for them. Furthermore, by using an emotion engine, the system provides training programs tailored to the user's emotional state, resulting in a more effective training experience.

[1085] (Example 2)

[1086] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1087] Traditional exercise programs are generated once based on the user's physical information and initial fitness assessment, but they do not take into account the user's emotional state during or after exercise. This makes it difficult to maintain user motivation and achieve effective training. Furthermore, because real-time adjustments are not possible, a program that has been set may become unsuitable for the user.

[1088] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1089] In this invention, the server includes means for receiving and storing the user's biometric information and physical fitness assessment results, means for generating an exercise plan based on the received information, means for recognizing the user's emotional state and collecting data, and means for adjusting and optimizing the exercise plan in real time based on the emotional data and post-exercise feedback. This enables the provision of an optimal exercise plan that takes the user's emotional state into consideration and real-time adjustment of the training program.

[1090] "Biometric information" refers to basic physical data such as the user's age, gender, height, and weight.

[1091] "Physical fitness assessment" refers to the results of exercises such as squats, planks, and stationary bike rides performed to measure the user's fitness level.

[1092] The term "central processing unit" refers to a computer device that analyzes the user's biometric information and physical fitness assessment results, and generates and adjusts exercise plans.

[1093] An "exercise plan" refers to a program that includes specific exercise instructions for the user, such as the type of training, number of sets, number of reps, and rest time that is best suited to that user.

[1094] "Emotional state" refers to a psychological state such as stress level and motivation level, which can be determined from the user's facial expressions and tone of voice.

[1095] "Emotional data" refers to data collected as numerical or textual information by analyzing the emotional state of users.

[1096] "Real-time adjustment" refers to the process of instantly optimizing the exercise plan based on the user's current emotional state and training progress.

[1097] "Feedback" refers to the opinions and evaluations that users provide regarding the difficulty level, satisfaction level, and physical condition of their training.

[1098] The system of this invention provides the user with an optimized exercise plan and further adjusts the exercise plan in real time based on their emotional state. This system mainly includes a user terminal, fitness evaluation equipment, a server, and an emotion engine.

[1099] Hardware and software to be used

[1100] User terminal

[1101] The user terminal is a device used to collect biometric information and fitness assessment results entered by the user. Specific examples include smartphones and tablets. Users enter data such as their age, gender, height, weight, fitness level, and goals into this terminal. For example, they might enter: "Age: 30, Gender: Male, Height: 175cm, Weight: 70kg, Beginner, Goal: Muscle Strength Improvement."

[1102] Fitness assessment equipment

[1103] Fitness assessment equipment is used to measure a user's physical fitness level. Specific examples include squat counter plates, plank timers, and exercise bike ergometers. These devices measure the results of the exercises performed by the user and input them into a terminal. For example, the user might input results such as "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120."

[1104] server

[1105] The server receives biometric information and fitness assessment results sent from the user's terminal and analyzes them using an AI algorithm. The server implements an AI algorithm using Python and TensorFlow, which is used to generate an optimal exercise plan. The generated exercise plan includes details such as the type of exercise, number of sets, number of repetitions, and rest time. The server then sends the generated exercise plan to the user's terminal.

[1106] Emotional Engine

[1107] The emotion engine is a system for recognizing and collecting data on a user's emotional state. It utilizes facial expression and voice analysis software (such as OpenCV or Watson Tone Analyzer) using cameras and microphones. This engine analyzes the user's facial expressions and voice tone, collecting emotional data such as stress levels and motivation levels. The collected emotional data is transmitted to a server in real time and used to adjust the exercise plan.

[1108] Specific example

[1109] Profile Information Input Example

[1110] The user enters the following on their smartphone:

[1111] Age: 30, Gender: Male, Height: 175cm, Weight: 70kg, Fitness Level: Beginner, Goal: Strength Improvement

[1112] Fitness assessment input example

[1113] The user enters the results of their fitness assessment as follows:

[1114] "Squats: 15 reps per minute, Plank: 30 seconds, Exercise bike heart rate: 120"

[1115] Training program display example

[1116] The motion plan generated by the server will be displayed as follows:

[1117] "Training program: 20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks"

[1118] Feedback input example

[1119] The user provides the following feedback after completing their workout:

[1120] "Feedback: The squats were a little too easy."

[1121] Thus, by using this system, users can always train efficiently with an exercise plan optimized for them. Furthermore, by introducing an emotion engine, real-time adjustments can be made according to the user's emotional state, maximizing the effectiveness of the training.

[1122] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1123] Step 1:

[1124] Upon arriving at the gym, users log in to the system using their personal device or smartphone and enter their profile information. Specifically, they enter basic data such as age, gender, height, weight, fitness level, and goals.

[1125] Input: User profile information

[1126] Output: Profile data entered into the device

[1127] Step 2:

[1128] The device prompts the user to perform an initial fitness assessment. This involves basic exercises such as squats, planks, and using an exercise bike. The user then inputs the results into the device.

[1129] Specific examples of exercises include "doing as many squats as possible in one minute," "holding a plank for as long as possible," and "measuring your heart rate while using an exercise bike."

[1130] Input: User's fitness assessment results

[1131] Output: Fitness evaluation data entered into the terminal

[1132] Step 3:

[1133] The device sends the collected profile information and fitness evaluation results to the server. Here, the device bundles the data into packets and sends them to the server.

[1134] Input: Profile information and fitness evaluation results entered on the device.

[1135] Output: Integrated data sent to the server

[1136] Step 4:

[1137] The server analyzes the received data using AI algorithms (utilizing Python and TensorFlow) to generate an optimal exercise plan for the user. The analysis includes the type of exercise, number of sets, number of reps, and rest time based on the user's fitness level and goals.

[1138] Input: Integrated data sent to the server

[1139] Output: Generated motion plan

[1140] Step 5:

[1141] The server sends the generated exercise plan to the terminal. The terminal displays the received exercise plan to the user. The user can then check the detailed exercise plan on the terminal.

[1142] In terms of specific actions, a detailed exercise plan is displayed on the user's device, such as "20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds of plank x 3 sets."

[1143] Input: Exercise plan generated by the server

[1144] Output: Exercise plan displayed on the terminal

[1145] Step 6:

[1146] The user performs the training according to the displayed exercise plan.

[1147] Specifically, the user will use an exercise bike for 20 minutes, followed by squats and planks for a specified number of repetitions and sets.

[1148] Input: Exercise plan displayed on the device

[1149] Output: Actual training by users

[1150] Step 7:

[1151] During training, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. It collects user emotional data using cameras and microphones and transmits it to the server in real time.

[1152] Specifically, we use facial recognition software (e.g., OpenCV) and speech analysis software (Watson Tone Analyzer).

[1153] Input: User's emotional data (facial expressions and tone of voice)

[1154] Output: Sentiment data sent to the server

[1155] Step 8:

[1156] The server analyzes the received emotional data and adjusts the exercise plan in real time as needed. If the user is feeling stressed, it will make adjustments such as adding stretches to help them relax.

[1157] Input: Emotional data sent to the server

[1158] Output: Adjusted exercise plan

[1159] Step 9:

[1160] After completing a workout, users enter feedback into a device regarding the difficulty level, satisfaction level, and their physical condition. This feedback includes information about how effective or difficult the exercise was.

[1161] Input: User feedback (impressions and evaluations after exercise)

[1162] Output: Feedback data entered into the terminal

[1163] Step 10:

[1164] The device sends feedback to the server. The server analyzes the user's feedback and optimizes the next exercise plan.

[1165] In terms of specific actions, adjustments such as increasing the number of sets in the next squat session will be made.

[1166] Input: Feedback data entered into the device

[1167] Output: Optimization of the next exercise plan

[1168] (Application Example 2)

[1169] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1170] Conventional autonomous vehicles have difficulty considering the driver's emotional state and stress level, which can result in insufficient driver comfort and safety. Furthermore, the lack of real-time driving mode changes or entertainment adjustments based on emotional state means that driver stress and fatigue during long drives cannot be reduced.

[1171] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user, means for collecting the user's emotional data and analyzing the emotional state in real time, and means for generating a training program that reflects the user's emotional state based on the emotional data. This enables real-time adjustments based on the emotional state, thereby improving the driver's comfort and safety.

[1172] "Means of inputting user physical information" refer to devices or applications that acquire and input basic physical information such as the user's age, gender, height, and weight.

[1173] "Means for conducting user fitness assessments" refers to devices and methods for evaluating a user's physical strength and athletic ability through fitness activities such as squats, planks, and exercise bikes.

[1174] A "server" is a computing system that analyzes a user's physical information, fitness assessment results, and emotional data to generate an optimal training program.

[1175] "Means for generating training programs" refers to algorithms and software that generate exercise programs tailored to the user's fitness goals based on data collected by the server.

[1176] "Means of providing to the user" refers to devices or applications used to display the generated training program to the user or to provide instructions.

[1177] "Means for collecting user training feedback and optimizing training programs" refers to devices or applications that collect feedback information provided by users after completing training and adjust the next training program based on that information.

[1178] "Means for collecting user emotional data and analyzing emotional states in real time" refers to devices and software that use cameras and microphones to analyze users' facial expressions and voice tone, and evaluate their emotional state in real time.

[1179] "Means for generating training programs that reflect a user's emotional state based on emotional data" refers to algorithms or software that take into account a user's emotional state and adjust the content and methods of training accordingly.

[1180] This invention relates to a system that provides a training program optimized for the user by inputting the user's physical information and fitness evaluation results, and also taking into account emotional data. Specific embodiments of this system are described below.

[1181] System-wide configuration

[1182] This system consists of the following main components:

[1183] 1. User terminal: A device used to input the user's physical information and fitness assessment. For example, a smartphone or tablet may be used.

[1184] 2. Fitness evaluation equipment: This equipment is used to measure the results of exercises such as squats, planks, and exercise bikes.

[1185] 3. Server: This is the central computing system that analyzes user input data, evaluation results, and sentiment data to generate the optimal training program.

[1186] 4. Emotion Engine: This is software that analyzes the user's facial expressions and voice tone to evaluate their emotional state in real time. Specific examples include emotion recognition models using OpenCV or Keras.

[1187] Program Processing Overview

[1188] First, the user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). This information is then sent from the user's device to the server.

[1189] Next, the user performs tests such as squats, planks, and exercise bikes using fitness evaluation equipment and enters the results into a terminal. These evaluation results are also sent to the server.

[1190] Based on this data, the server uses a generated AI model to create an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[1191] The generated training program is sent from the server to the user's terminal, and the user begins training according to it.

[1192] During training, the emotion engine analyzes the user's facial expressions and tone of voice in real time and sends emotional data to the server. This allows the server to adjust the training program in real time, taking the user's emotional state into consideration. For example, if the user is feeling stressed, adjustments such as adding stretches to help them relax may be made.

[1193] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The server analyzes this feedback to optimize the next training program.

[1194] Specific example

[1195] Example 1

[1196] User: Arrives at the gym, logs into the system on their smartphone, and enters their profile information (e.g., 30 years old, male, 175cm tall, 70kg weight).

[1197] User terminal: As an initial evaluation, we suggest testing squats, planks, and an exercise bike.

[1198] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[1199] Server: Based on these evaluation results, it generates an optimal training program (e.g., 20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks).

[1200] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[1201] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[1202] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[1203] Example of a prompt

[1204] "What kind of music would you play if the driver of this vehicle was tired?"

[1205] "When the driver's emotional state is stressed, please tell me the appropriate driving mode."

[1206] By specifically demonstrating the embodiments for carrying out the invention in this way, it is possible to provide users with an optimal training experience and improve driver comfort and safety.

[1207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1208] Step 1:

[1209] The user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). The entered data is sent from the device to the server. This initiates the processing of the user's data.

[1210] Step 2:

[1211] Users perform tests such as squats, planks, and exercise bike workouts using fitness assessment equipment. The test results (e.g., 15 squats in 1 minute, 30-second plank, and a heart rate of 120 bpm on the exercise bike) are entered into the device and sent to the server.

[1212] Step 3:

[1213] The server analyzes the user's physical information and fitness assessment results, and uses a generative AI model to generate an optimal training program for the user. The generated program includes specific exercise types, number of sets, number of reps, rest times, and more.

[1214] Step 4:

[1215] The training program generated by the server is sent to the user's terminal, which then displays it to the user. The user then begins training according to the displayed training program.

[1216] Step 5:

[1217] During training, the emotion engine analyzes the user's facial expressions and tone of voice through the device. The emotion data is sent to the server in real time, and the server uses this data to evaluate the user's emotional state.

[1218] Step 6:

[1219] The server adjusts the training program in real time based on the user's emotional state. For example, if the user is feeling stressed, it adds stretches to help them relax. The adjusted program is then sent back to the user's device.

[1220] Step 7:

[1221] After completing a training session, users provide feedback via their device regarding the difficulty level, satisfaction level, and physical condition of the training. The device then sends this feedback to the server.

[1222] Step 8:

[1223] The server analyzes the collected feedback and optimizes the next training program. This allows users to have a more effective training experience.

[1224] 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.

[1225] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1226] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1227] [Fourth Embodiment]

[1228] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1229] 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.

[1230] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[1231] 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.

[1232] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[1233] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1234] 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.

[1235] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1236] 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.

[1237] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[1238] The 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.

[1239] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1240] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1241] This invention relates to a system that enables users to efficiently and effectively achieve their fitness goals by providing them with individually optimized training programs. The system uses AI to generate training programs based on the user's physical information and fitness assessment results, and adjusts them in real time according to the user's progress.

[1242] System-wide configuration

[1243] This system consists of the following main components:

[1244] 1. User terminal

[1245] 2. Fitness assessment equipment

[1246] 3. Server

[1247] Program Processing Overview

[1248] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals.

[1249] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, the user might input the number of squats performed per minute, the duration of a plank, or their heart rate while using the exercise bike.

[1250] The device transmits the collected user's physical information and fitness assessment results to the server. The server receives this data, analyzes it using an AI algorithm, and generates an optimal training program for the user. This program details appropriate exercises, sets, reps, rest times, and more.

[1251] The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. During training, the terminal records the user's progress in real time and provides instructions and feedback as needed.

[1252] After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into a device. The device sends this feedback to a server, which analyzes it to optimize the next training program. For example, the server might decide whether the user should increase the number of squat sets or add exercises to improve endurance.

[1253] Specific example

[1254] Day 1

[1255] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[1256] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[1257] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[1258] Terminal: Sends these evaluation results to the server.

[1259] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[1260] Terminal: Displays the generated training program.

[1261] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[1262] Terminal: Sends feedback to the server.

[1263] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[1264] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[1265] The following describes the processing flow.

[1266] Step 1:

[1267] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[1268] Step 2:

[1269] Terminal: The terminal sends the profile information entered by the user to the server. The server then receives the user's basic information and registers the profile.

[1270] Step 3:

[1271] Device: Encourage the user to complete an initial fitness assessment. Specifically, instruct them to perform basic exercises such as squats, planks, and stationary bike exercises, and to input the results. For example, present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the stationary bike."

[1272] Step 4:

[1273] User: Perform the instructed evaluation and enter the results into the terminal. For example, enter results such as "15 squats, 30 seconds plank, exercise bike heart rate 120".

[1274] Step 5:

[1275] The device sends the user's fitness assessment results to the server. The server then receives the user's detailed fitness data.

[1276] Step 6:

[1277] Server: Based on the received user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data. Based on the analysis, it generates an optimal training program for the user. This program includes specific exercise types, sets, reps, rest times, and more.

[1278] Step 7:

[1279] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[1280] Step 8:

[1281] User: Follow the program displayed on your device or smartphone to perform the workout. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[1282] Step 9:

[1283] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[1284] Step 10:

[1285] User: After completing a workout, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might provide feedback such as, "The squats were a little too easy," or "The exercise bike was appropriate."

[1286] Step 11:

[1287] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[1288] Step 12:

[1289] Server: Analyzes the received feedback and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or slightly shorten the rest time.

[1290] Step 13:

[1291] Server: Sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[1292] By repeating the above steps, users can always train efficiently with a training program optimized for them. This allows users to achieve their fitness goals more effectively and efficiently.

[1293] (Example 1)

[1294] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1295] Traditional training systems have struggled to provide optimal training programs tailored to each user's individual fitness level and goals. Furthermore, they lacked the ability to instantly reflect user training progress and feedback, and adjust training programs in real time. As a result, they provided insufficient support for efficiently and effectively achieving fitness goals.

[1296] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1297] In this invention, the server includes means for inputting the user's biometric information, means for performing an evaluation of the user's exercise, and means for transmitting the user's biometric information and exercise evaluation results to the server. This makes it possible to generate individually optimized training programs and adjust the training programs in real time by immediately reflecting the user's progress and feedback.

[1298] "Biometric information" refers to data that shows a user's personal physical characteristics, such as age, gender, height, and weight.

[1299] "Exercise evaluation" refers to measuring and evaluating the results of exercise performed by a user, and includes test results for squats, planks, and exercise bikes.

[1300] A "server" is a device or system that receives a user's biometric information and exercise evaluation results, generates a training program based on that data, and performs analysis including feedback.

[1301] A "generative AI model" refers to an artificial intelligence algorithm used to analyze collected user data and generate the optimal training program.

[1302] A "training program" is a detailed plan that outlines the type of exercises a user should perform, the number of sets, the number of repetitions, rest periods, and other specific details.

[1303] "Feedback" refers to opinions and evaluations that users provide after a training session regarding the difficulty level of the training, satisfaction level, physical condition, etc.

[1304] A "calculating device" refers to a device used by users to input information, and includes smartphones, tablets, and dedicated terminals at gyms.

[1305] This invention relates to a system that provides users with individually optimized training programs to efficiently achieve their fitness goals. Specific embodiments for carrying out the invention are described below.

[1306] System Configuration

[1307] This system consists of the following main components:

[1308] 1. User devices (e.g., smartphones, tablets)

[1309] 2. Fitness assessment equipment (e.g., exercise bike ergometer, squat sensor)

[1310] 3. Server (Cloud Server)

[1311] Program processing flow

[1312] 1. Upon arriving at the gym, users log in to the system using their smartphone or a gym terminal and enter their profile information. This includes age, gender, height, weight, fitness level, and goals. For example, information such as 30 years old, male, 175cm tall, 70kg in weight, beginner fitness level, and goal of increasing muscle strength might be entered.

[1313] 2. The device prompts the user to perform an initial fitness assessment. This includes basic exercises such as squats, planks, and using an exercise bike ergometer, and the results are entered into the device. For example, the user might enter how many squats they performed per minute or how long they were able to hold a plank. For instance, they might enter results such as 15 squats in one minute, a 30-second plank, and a heart rate of 120 on the exercise bike ergometer.

[1314] 3. The device sends the collected user biometric information and exercise evaluation results to the server. For example, it sends data such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TensorFlow or PyTorch). The server generates an optimal training program based on the user's characteristics. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[1315] 4. The server sends the generated training program to the terminal or the user's smartphone. The user performs the training according to the displayed program. The terminal records the user's progress in real time during training and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats."

[1316] 5. After completing the training session, the user enters feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, they might enter feedback such as, "The number of squat sets was a little too easy." The device sends this feedback to the server. The server analyzes the feedback and optimizes the next training program. For example, adjustments might be made, such as increasing the number of squat sets or adding exercises to improve endurance.

[1317] Specific example

[1318] Day 1

[1319] User: Arrives at the gym, logs in on the terminal, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement).

[1320] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[1321] User: Enter the results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[1322] Terminal: Sends these evaluation results to the server.

[1323] Server: Analyzes data and generates the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[1324] Terminal: Displays the generated training program.

[1325] User: Follow the program instructions to complete the training and provide feedback afterward (e.g., "The squats were a little too easy").

[1326] Terminal: Sends feedback to the server.

[1327] Server: Analyzes the feedback and adjusts the next training program. For example, increase the number of squat sets.

[1328] By repeating this process, users can always train efficiently with a training program optimized for them. This system makes it more effective and efficient for users to achieve their fitness goals.

[1329] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1330] Step 1:

[1331] Upon arriving at the gym, users log into the system using a terminal or their smartphone and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. For example, the input might include data such as "30 years old, male, 175cm tall, 70kg weight, beginner fitness level, goal: muscle improvement." This data is received by the terminal and stored as the user's basic physical information.

[1332] Step 2:

[1333] The device prompts the user to perform an initial fitness assessment. This assessment includes exercises such as squats, planks, and exercise bike ergometer exercises. The user performs these exercises and inputs the results into the device. For example, the user might input data such as 15 squats per minute, a 30-second plank, and a heart rate of 120 on the exercise bike. This data is collected by the device and stored as the user's exercise assessment result.

[1334] Step 3:

[1335] The device sends the collected user biometric information and exercise evaluation results to the server. Input includes evaluation results such as the number of squats, plank time, and heart rate. The server receives this data and analyzes it using a generative AI model (e.g., using TensorFlow or PyTorch). Specifically, it generates an optimal training program based on the user's physical information and exercise evaluation results. This analysis result becomes the server's output.

[1336] Step 4:

[1337] The server sends the generated training program to the terminal or the user's smartphone. The output training program might include, for example, 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks. This program is then displayed on the terminal.

[1338] Step 5:

[1339] The user performs the training according to the displayed training program. During training, the device records the user's progress in real time and provides instructions and feedback as needed. For example, it might display instructions such as, "Next, do 15 squats." Data regarding the user's progress is collected by the device.

[1340] Step 6:

[1341] After completing a workout, users input feedback on the difficulty level, satisfaction level, and physical condition into their device. Specific inputs may include comments such as, "The number of squat sets was a little too easy." This feedback data is received by the device and sent to the server.

[1342] Step 7:

[1343] The server analyzes the received feedback and optimizes the next training program. Specifically, it adjusts the content of the user's training program (number of sets, reps, exercise types, etc.) based on the feedback. For example, it might increase the number of squat sets or add exercises to improve endurance. This optimized training program becomes the server's output and is provided to the user in the next training session.

[1344] (Application Example 1)

[1345] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1346] Traditional fitness systems have struggled to provide personalized training programs in real time, tailored to each user's unique physical characteristics and fitness level. Furthermore, they lacked sufficient means to monitor training progress in real time and provide appropriate feedback. As a result, it was difficult for users to efficiently and effectively achieve their fitness goals.

[1347] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1348] In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user using a smart wearable device or head-mounted display, means for transmitting the user's physical information and fitness assessment results to the server, means for generating a training program using a generative AI model, means for providing the training program to the user, means for monitoring the user's progress in real time and providing feedback through a smart wearable device or head-mounted display, and means for collecting the user's training feedback and optimizing the training program. This enables the generation of individually optimized training programs and the provision of real-time progress monitoring and feedback.

[1349] "User physical information" refers to data such as the user's age, gender, height, weight, fitness level, and fitness goals.

[1350] A "smart wearable device" is an electronic device that collects a user's biometric information and can monitor their training progress in real time. Examples include smartwatches and heart rate monitors.

[1351] A "head-mounted display" is a display device worn by a user that visually displays training programs and feedback. This includes, for example, AR (augmented reality) and VR (virtual reality) devices.

[1352] A "fitness assessment" is a process that measures a user's current fitness level through basic exercises such as squats, planks, and stationary bikes.

[1353] A "server" is a centralized computer system that receives users' physical information and fitness assessment results, and generates and optimizes training programs using a generated AI model.

[1354] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and automatically generates and adjusts the optimal training program based on that analysis.

[1355] A "training program" is a plan that includes a set of exercises individually optimized for the user to achieve their fitness goals, along with instructions regarding the number of sets, repetitions, rest times, and other related details.

[1356] "Real-time monitoring" refers to the ability to instantly monitor a user's progress and physical condition during training, and to provide immediate adjustments and feedback as needed.

[1357] "Feedback" refers to information used to modify and optimize training programs based on the user's experience with training, including difficulty level, satisfaction level, and physical condition.

[1358] "Optimization" is the process of adjusting a training program to best suit the user's fitness goals based on their past data and feedback.

[1359] This invention relates to a personal fitness system using smart wearable devices and head-mounted displays (HMDs) that can be used by users in fitness facilities. The embodiments for carrying out this invention are described in detail below.

[1360] Overall system configuration

[1361] This system consists of a user terminal, a smart wearable device or head-mounted display (HMD), fitness assessment equipment, and a server.

[1362] Program Processing Overview

[1363] First, upon arriving at the fitness facility, users put on smart glasses or HMDs and enter their profile information. This includes age, gender, height, weight, fitness level, and goals.

[1364] Next, the user performs an initial fitness assessment using a smart wearable device or HMD. For example, they perform squats, planks, or use an exercise bike, and the results are automatically collected via sensors. The collected data is then transmitted to a server via the user's device.

[1365] The server generates a training program using an AI model based on the received user's physical information and fitness assessment results. The generated training program is sent to a smart wearable device or HMD and presented to the user visually.

[1366] While the user is training, smart wearable devices and HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, instructions such as "Improve your squat form" or "You have 10 seconds until the next set" may be displayed.

[1367] After completing a training session, users provide feedback through smart wearable devices or HMDs. For example, they can input feedback on the difficulty level of the training, satisfaction, and their physical condition.

[1368] The server analyzes the collected feedback to optimize the next training program. This process ensures that users always receive an optimized training program, allowing them to achieve their fitness goals efficiently and effectively.

[1369] Specific example

[1370] Initial Setup

[1371] User: 30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle strength improvement. Log in wearing smart glasses and enter profile information.

[1372] Smart glasses: As an initial evaluation, we suggest testing them during squats, planks, and exercise bike workouts.

[1373] User: Obtained results of 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm while using an exercise bike.

[1374] Smart glasses: These evaluation results are sent to the server.

[1375] Server: Analyzes data to generate the optimal training program for the user. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[1376] Smart glasses: Display the generated training program.

[1377] Training Session

[1378] User: Conduct training according to the proposed program.

[1379] Smart glasses: Monitor progress in real time and provide feedback such as "Improve your squat form."

[1380] User: After completing the training, provide feedback on difficulty level, satisfaction, and physical condition.

[1381] Smart glasses: Input feedback is sent to the server.

[1382] Server: Analyzes feedback and optimizes the next training program.

[1383] Example of a prompt

[1384] "Please generate an optimized training program for a 30-year-old male, 175cm tall, weighing 70kg, aiming for beginner-level strength improvement."

[1385] "You are a 30-year-old male aiming to improve your strength. Initial assessments include 15 squats per minute, a 30-second plank, and a heart rate of 120 bpm on an exercise bike. Please recommend an optimal training program."

[1386] In this way, the system of the present invention effectively supports users in achieving their fitness goals.

[1387] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1388] Step 1:

[1389] When a user arrives at a fitness facility, they put on smart glasses or a head-mounted display (HMD) and enter their profile information. This information includes age, gender, height, weight, fitness level, and goals. This provides the user with basic physical information.

[1390] Input: Age, Gender, Height, Weight, Fitness Level, Goals

[1391] Output: User's basic physical information

[1392] Step 2:

[1393] Using smart glasses or an HMD, users perform an initial fitness assessment. As users perform exercises such as squats, planks, and exercise bike rides, the results are automatically collected via sensors. These assessment results serve as a baseline for the user's initial training.

[1394] Input: User's exercise results (e.g., squats, planks, exercise bike)

[1395] Output: Fitness evaluation results (e.g., number of squats, duration of plank, heart rate on exercise bike)

[1396] Step 3:

[1397] The terminal sends the collected user's physical information and fitness assessment results to the server. The server receives this data and performs preprocessing.

[1398] Input: User's basic physical information, fitness assessment results

[1399] Output: User information and evaluation results stored in the database

[1400] Step 4:

[1401] The server uses a generative AI model to generate training programs based on user data. This AI model analyzes user data to determine the optimal type of exercise, number of sets, number of reps, and rest time.

[1402] Input: User information, fitness assessment results

[1403] Output: Optimized training program (e.g., 20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds plank x 3 sets)

[1404] Step 5:

[1405] The generated training program is sent from the server to smart glasses or an HMD and presented visually to the user. The user then performs the training according to this program.

[1406] Input: Optimized training program

[1407] Output: Training program displayed on smart glasses or HMD.

[1408] Step 6:

[1409] While the user is training, smart glasses or HMDs monitor their progress in real time and provide immediate instructions and feedback. For example, they might display instructions such as, "Improve your squat form" or "You have 10 seconds until the next set begins."

[1410] Input: User training progress

[1411] Output: Real-time feedback and instructions

[1412] Step 7:

[1413] After completing the training, users provide feedback through smart glasses or HMDs. This feedback includes information about the difficulty level of the training, satisfaction level, and physical condition.

[1414] Input: User feedback (information on difficulty level, satisfaction level, and physical condition)

[1415] Output: Feedback data

[1416] Step 8:

[1417] The device sends the collected feedback data to the server. The server analyzes this data and optimizes the next training program.

[1418] Input: User feedback data

[1419] Output: Optimized next training program

[1420] This series of processing steps ensures that users always receive a individually optimized training program, allowing them to effectively achieve their fitness goals.

[1421] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1422] This invention relates to a system that provides a user with an optimized training program and further adjusts that program based on the user's emotional state. This system not only generates an optimal training program based on the user's physical information and fitness assessment results, but also acquires the user's emotional data using an emotion engine and reflects it in the training content, thereby providing a more effective training experience.

[1423] System-wide configuration

[1424] This system consists of the following main components:

[1425] 1. User terminal

[1426] 2. Fitness assessment equipment

[1427] 3. Server

[1428] 4. Emotional Engine

[1429] Program Processing Overview

[1430] First, upon arriving at the gym, users log in to the system using their user terminal or smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: muscle improvement."

[1431] Next, the device prompts the user to perform an initial fitness assessment. This involves performing basic exercises such as squats, planks, and using an exercise bike ergometer, and then inputting the results into the device. For example, it might present specific assessment items such as "maximum number of squats per minute, plank duration, and heart rate on the exercise bike."

[1432] The user performs the instructed evaluation and enters the results into the device. For example, they might enter results such as "15 squats, 30 seconds of plank, and a heart rate of 120 on the exercise bike."

[1433] These evaluation results and profile information are sent from the device to the server. The server uses an AI algorithm to analyze this data and generate an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[1434] The server sends the generated training program to the terminal or the user's smartphone. This allows the user to review their training program.

[1435] Users perform their workouts according to the displayed program. For example, they might follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks."

[1436] Furthermore, this system has an emotion engine that acquires the user's emotional data. The emotion engine recognizes the user's emotional state (e.g., stress level, motivation level) by analyzing the user's facial expressions and tone of voice. During training, the emotion engine collects the user's emotional data in real time and sends it to the server.

[1437] The server adjusts the training program in real time based on emotional data and training progress. For example, if a user is feeling stressed, it may add stretching exercises to help them relax.

[1438] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The device sends this feedback to a server, which is used to optimize the next training program.

[1439] Specific example

[1440] Day 1

[1441] User: Arrives at the gym, logs in on the device, and enters profile information (30 years old, male, 175cm tall, 70kg weight, fitness level: beginner, goal: muscle improvement).

[1442] Terminal: As an initial evaluation, we suggest testing squats, planks, and exercise bikes.

[1443] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[1444] Terminal: Sends these evaluation results to the server.

[1445] Server: Analyzes data and generates an optimal training program. For example, it might suggest 20 minutes on an exercise bike, 3 sets of 15 squats, and 3 sets of 30-second planks.

[1446] Terminal: Displays the generated training program.

[1447] User: Follow the program to complete the training.

[1448] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[1449] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[1450] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[1451] Terminal: Sends feedback to the server.

[1452] Server: Analyzes the feedback and adjusts the next training program (e.g., increases the number of squat sets).

[1453] By repeating this process, users can always train efficiently with a training program optimized for them. Combining this with an emotional engine allows for training tailored to the user's emotional state, resulting in an even more effective training experience.

[1454] The following describes the processing flow.

[1455] Step 1:

[1456] User: Upon arriving at the gym, log in to the system using a terminal near the front desk or their smartphone and enter their profile information. This includes basic data such as age, gender, height, weight, fitness level, and goals. For example, they might enter information such as "30 years old, male, 175cm tall, 70kg weight, beginner, goal: to improve muscle strength."

[1457] Step 2:

[1458] Terminal: Sends the profile information entered by the user to the server. The server saves the received data and registers it as the user profile.

[1459] Step 3:

[1460] Device: Prompts the user to perform an initial fitness assessment. For example, assessment items such as squats, planks, and heart rate measurement while using an exercise bike are displayed.

[1461] Step 4:

[1462] User: Perform the initial assessment displayed on the screen and enter the results into the device. For example, enter "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120".

[1463] Step 5:

[1464] The device sends the user's fitness assessment results to the server. The server then receives detailed fitness data.

[1465] Step 6:

[1466] Server: Based on the user's physical information and fitness assessment results, the server uses an AI algorithm to analyze the data and generate an optimal training program for the user. This program includes detailed information such as the type of exercise, number of sets, number of reps, and rest time.

[1467] Step 7:

[1468] Server: Sends the generated training program to the user's terminal or smartphone. This allows the user to check their own training program.

[1469] Step 8:

[1470] User: Follow the training program displayed on your device or smartphone. For example, follow a specific program such as "20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks."

[1471] Step 9:

[1472] Device: During training, it records the user's progress in real time and provides instructions and feedback as needed. For example, it displays "squat form correction instructions, countdown timer between sets," etc.

[1473] Step 10:

[1474] Emotion Engine: During training, it analyzes the user's facial expressions and tone of voice to recognize emotions. For example, it analyzes the user's facial expressions when they are facing the camera and sends that data to the server.

[1475] Step 11:

[1476] Server: Based on emotional data and training progress data, the server adjusts the training program in real time. For example, if the user is feeling tired or stressed, it adds stretching exercises to the program to help them relax.

[1477] Step 12:

[1478] User: After completing a workout, enter feedback on the difficulty level, satisfaction level, and physical condition into the device. For example, enter feedback such as "The squats were a little too easy" or "The exercise bike was appropriate."

[1479] Step 13:

[1480] Terminal: Sends collected feedback to the server. The server then receives the feedback data.

[1481] Step 14:

[1482] Server: Analyzes the received feedback data and training data, and adjusts the next training program as needed. For example, it might increase the number of squat sets or shorten rest periods.

[1483] Step 15:

[1484] Server: The server sends the adjusted new training program back to the user's device or smartphone. The user will then begin training according to this new program during their next training session.

[1485] By repeating the above steps, users can always train efficiently with a training program optimized for them. Furthermore, by using an emotion engine, the system provides training programs tailored to the user's emotional state, resulting in a more effective training experience.

[1486] (Example 2)

[1487] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1488] Traditional exercise programs are generated once based on the user's physical information and initial fitness assessment, but they do not take into account the user's emotional state during or after exercise. This makes it difficult to maintain user motivation and achieve effective training. Furthermore, because real-time adjustments are not possible, a program that has been set may become unsuitable for the user.

[1489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1490] In this invention, the server includes means for receiving and storing the user's biometric information and physical fitness assessment results, means for generating an exercise plan based on the received information, means for recognizing the user's emotional state and collecting data, and means for adjusting and optimizing the exercise plan in real time based on the emotional data and post-exercise feedback. This enables the provision of an optimal exercise plan that takes the user's emotional state into consideration and real-time adjustment of the training program.

[1491] "Biometric information" refers to basic physical data such as the user's age, gender, height, and weight.

[1492] "Physical fitness assessment" refers to the results of exercises such as squats, planks, and stationary bike rides performed to measure the user's fitness level.

[1493] The term "central processing unit" refers to a computer device that analyzes the user's biometric information and physical fitness assessment results, and generates and adjusts exercise plans.

[1494] An "exercise plan" refers to a program that includes specific exercise instructions for the user, such as the type of training, number of sets, number of reps, and rest time that is best suited to that user.

[1495] "Emotional state" refers to a psychological state such as stress level and motivation level, which can be determined from the user's facial expressions and tone of voice.

[1496] "Emotional data" refers to data collected as numerical or textual information by analyzing the emotional state of users.

[1497] "Real-time adjustment" refers to the process of instantly optimizing the exercise plan based on the user's current emotional state and training progress.

[1498] "Feedback" refers to the opinions and evaluations that users provide regarding the difficulty level, satisfaction level, and physical condition of their training.

[1499] The system of this invention provides the user with an optimized exercise plan and further adjusts the exercise plan in real time based on their emotional state. This system mainly includes a user terminal, fitness evaluation equipment, a server, and an emotion engine.

[1500] Hardware and software to be used

[1501] User terminal

[1502] The user terminal is a device used to collect biometric information and fitness assessment results entered by the user. Specific examples include smartphones and tablets. Users enter data such as their age, gender, height, weight, fitness level, and goals into this terminal. For example, they might enter: "Age: 30, Gender: Male, Height: 175cm, Weight: 70kg, Beginner, Goal: Muscle Strength Improvement."

[1503] Fitness assessment equipment

[1504] Fitness assessment equipment is used to measure a user's physical fitness level. Specific examples include squat counter plates, plank timers, and exercise bike ergometers. These devices measure the results of the exercises performed by the user and input them into a terminal. For example, the user might input results such as "15 squats in 1 minute, plank for 30 seconds, exercise bike heart rate 120."

[1505] server

[1506] The server receives biometric information and fitness assessment results sent from the user's terminal and analyzes them using an AI algorithm. The server implements an AI algorithm using Python and TensorFlow, which is used to generate an optimal exercise plan. The generated exercise plan includes details such as the type of exercise, number of sets, number of repetitions, and rest time. The server then sends the generated exercise plan to the user's terminal.

[1507] Emotional Engine

[1508] The emotion engine is a system for recognizing and collecting data on a user's emotional state. It utilizes facial expression and voice analysis software (such as OpenCV or Watson Tone Analyzer) using cameras and microphones. This engine analyzes the user's facial expressions and voice tone, collecting emotional data such as stress levels and motivation levels. The collected emotional data is transmitted to a server in real time and used to adjust the exercise plan.

[1509] Specific example

[1510] Profile Information Input Example

[1511] The user enters the following on their smartphone:

[1512] Age: 30, Gender: Male, Height: 175cm, Weight: 70kg, Fitness Level: Beginner, Goal: Strength Improvement

[1513] Fitness assessment input example

[1514] The user enters the results of their fitness assessment as follows:

[1515] "Squats: 15 reps per minute, Plank: 30 seconds, Exercise bike heart rate: 120"

[1516] Training program display example

[1517] The motion plan generated by the server will be displayed as follows:

[1518] "Training program: 20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks"

[1519] Feedback input example

[1520] The user provides the following feedback after completing their workout:

[1521] "Feedback: The squats were a little too easy."

[1522] Thus, by using this system, users can always train efficiently with an exercise plan optimized for them. Furthermore, by introducing an emotion engine, real-time adjustments can be made according to the user's emotional state, maximizing the effectiveness of the training.

[1523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1524] Step 1:

[1525] Upon arriving at the gym, users log in to the system using their personal device or smartphone and enter their profile information. Specifically, they enter basic data such as age, gender, height, weight, fitness level, and goals.

[1526] Input: User profile information

[1527] Output: Profile data entered into the device

[1528] Step 2:

[1529] The device prompts the user to perform an initial fitness assessment. This involves basic exercises such as squats, planks, and using an exercise bike. The user then inputs the results into the device.

[1530] Specific examples of exercises include "doing as many squats as possible in one minute," "holding a plank for as long as possible," and "measuring your heart rate while using an exercise bike."

[1531] Input: User's fitness assessment results

[1532] Output: Fitness evaluation data entered into the terminal

[1533] Step 3:

[1534] The device sends the collected profile information and fitness evaluation results to the server. Here, the device bundles the data into packets and sends them to the server.

[1535] Input: Profile information and fitness evaluation results entered on the device.

[1536] Output: Integrated data sent to the server

[1537] Step 4:

[1538] The server analyzes the received data using AI algorithms (utilizing Python and TensorFlow) to generate an optimal exercise plan for the user. The analysis includes the type of exercise, number of sets, number of reps, and rest time based on the user's fitness level and goals.

[1539] Input: Integrated data sent to the server

[1540] Output: Generated motion plan

[1541] Step 5:

[1542] The server sends the generated exercise plan to the terminal. The terminal displays the received exercise plan to the user. The user can then check the detailed exercise plan on the terminal.

[1543] In terms of specific actions, a detailed exercise plan is displayed on the user's device, such as "20 minutes on the exercise bike, 15 squats x 3 sets, 30 seconds of plank x 3 sets."

[1544] Input: Exercise plan generated by the server

[1545] Output: Exercise plan displayed on the terminal

[1546] Step 6:

[1547] The user performs the training according to the displayed exercise plan.

[1548] Specifically, the user will use an exercise bike for 20 minutes, followed by squats and planks for a specified number of repetitions and sets.

[1549] Input: Exercise plan displayed on the device

[1550] Output: Actual training by users

[1551] Step 7:

[1552] During training, the emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotional state. It collects user emotional data using cameras and microphones and transmits it to the server in real time.

[1553] Specifically, we use facial recognition software (e.g., OpenCV) and speech analysis software (Watson Tone Analyzer).

[1554] Input: User's emotional data (facial expressions and tone of voice)

[1555] Output: Sentiment data sent to the server

[1556] Step 8:

[1557] The server analyzes the received emotional data and adjusts the exercise plan in real time as needed. If the user is feeling stressed, it will make adjustments such as adding stretches to help them relax.

[1558] Input: Emotional data sent to the server

[1559] Output: Adjusted exercise plan

[1560] Step 9:

[1561] After completing a workout, users enter feedback into a device regarding the difficulty level, satisfaction level, and their physical condition. This feedback includes information about how effective or difficult the exercise was.

[1562] Input: User feedback (impressions and evaluations after exercise)

[1563] Output: Feedback data entered into the terminal

[1564] Step 10:

[1565] The device sends feedback to the server. The server analyzes the user's feedback and optimizes the next exercise plan.

[1566] In terms of specific actions, adjustments such as increasing the number of sets in the next squat session will be made.

[1567] Input: Feedback data entered into the device

[1568] Output: Optimization of the next exercise plan

[1569] (Application Example 2)

[1570] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1571] Conventional autonomous vehicles have difficulty considering the driver's emotional state and stress level, which can result in insufficient driver comfort and safety. Furthermore, the lack of real-time driving mode changes or entertainment adjustments based on emotional state means that driver stress and fatigue during long drives cannot be reduced.

[1572] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's physical information, means for performing a fitness assessment of the user, means for collecting the user's emotional data and analyzing the emotional state in real time, and means for generating a training program that reflects the user's emotional state based on the emotional data. This enables real-time adjustments based on the emotional state, thereby improving the driver's comfort and safety.

[1573] "Means of inputting user physical information" refer to devices or applications that acquire and input basic physical information such as the user's age, gender, height, and weight.

[1574] "Means for conducting user fitness assessments" refers to devices and methods for evaluating a user's physical strength and athletic ability through fitness activities such as squats, planks, and exercise bikes.

[1575] A "server" is a computing system that analyzes a user's physical information, fitness assessment results, and emotional data to generate an optimal training program.

[1576] "Means for generating training programs" refers to algorithms and software that generate exercise programs tailored to the user's fitness goals based on data collected by the server.

[1577] "Means of providing to the user" refers to devices or applications used to display the generated training program to the user or to provide instructions.

[1578] "Means for collecting user training feedback and optimizing training programs" refers to devices or applications that collect feedback information provided by users after completing training and adjust the next training program based on that information.

[1579] "Means for collecting user emotional data and analyzing emotional states in real time" refers to devices and software that use cameras and microphones to analyze users' facial expressions and voice tone, and evaluate their emotional state in real time.

[1580] "Means for generating training programs that reflect a user's emotional state based on emotional data" refers to algorithms or software that take into account a user's emotional state and adjust the content and methods of training accordingly.

[1581] This invention relates to a system that provides a training program optimized for the user by inputting the user's physical information and fitness evaluation results, and also taking into account emotional data. Specific embodiments of this system are described below.

[1582] System-wide configuration

[1583] This system consists of the following main components:

[1584] 1. User terminal: A device used to input the user's physical information and fitness assessment. For example, a smartphone or tablet may be used.

[1585] 2. Fitness evaluation equipment: This equipment is used to measure the results of exercises such as squats, planks, and exercise bikes.

[1586] 3. Server: This is the central computing system that analyzes user input data, evaluation results, and sentiment data to generate the optimal training program.

[1587] 4. Emotion Engine: This is software that analyzes the user's facial expressions and voice tone to evaluate their emotional state in real time. Specific examples include emotion recognition models using OpenCV or Keras.

[1588] Program Processing Overview

[1589] First, the user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). This information is then sent from the user's device to the server.

[1590] Next, the user performs tests such as squats, planks, and exercise bikes using fitness evaluation equipment and enters the results into a terminal. These evaluation results are also sent to the server.

[1591] Based on this data, the server uses a generated AI model to create an optimal training program for the user. This training program includes detailed information such as the type of exercise, the number of sets, the number of reps, and rest times.

[1592] The generated training program is sent from the server to the user's terminal, and the user begins training according to it.

[1593] During training, the emotion engine analyzes the user's facial expressions and tone of voice in real time and sends emotional data to the server. This allows the server to adjust the training program in real time, taking the user's emotional state into consideration. For example, if the user is feeling stressed, adjustments such as adding stretches to help them relax may be made.

[1594] After completing a training session, users provide feedback on the difficulty level, satisfaction level, and their physical condition. The server analyzes this feedback to optimize the next training program.

[1595] Specific example

[1596] Example 1

[1597] User: Arrives at the gym, logs into the system on their smartphone, and enters their profile information (e.g., 30 years old, male, 175cm tall, 70kg weight).

[1598] User terminal: As an initial evaluation, we suggest testing squats, planks, and an exercise bike.

[1599] User: Enter the results of 15 squats in 1 minute, a 30-second plank, and a heart rate of 120 on an exercise bike.

[1600] Server: Based on these evaluation results, it generates an optimal training program (e.g., 20 minutes on the exercise bike, 3 sets of 15 squats, 3 sets of 30-second planks).

[1601] Emotion Engine: During training, it analyzes the user's facial expressions and voice tone to recognize emotions and sends the data to the server.

[1602] Server: Adjusts the training program in real time based on emotional data (e.g., if the user is feeling stressed, adds stretches to help them relax).

[1603] User: After completing the training, provide feedback on the difficulty level, satisfaction, and physical condition (e.g., "Squats were a little easy").

[1604] Example of a prompt

[1605] "What kind of music would you play if the driver of this vehicle was tired?"

[1606] "When the driver's emotional state is stressed, please tell me the appropriate driving mode."

[1607] By specifically demonstrating the embodiments for carrying out the invention in this way, it is possible to provide users with an optimal training experience and improve driver comfort and safety.

[1608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1609] Step 1:

[1610] The user logs into the system using their device and enters basic physical information (age, gender, height, weight, etc.). The entered data is sent from the device to the server. This initiates the processing of the user's data.

[1611] Step 2:

[1612] Users perform tests such as squats, planks, and exercise bike workouts using fitness assessment equipment. The test results (e.g., 15 squats in 1 minute, 30-second plank, and a heart rate of 120 bpm on the exercise bike) are entered into the device and sent to the server.

[1613] Step 3:

[1614] The server analyzes the user's physical information and fitness assessment results, and uses a generative AI model to generate an optimal training program for the user. The generated program includes specific exercise types, number of sets, number of reps, rest times, and more.

[1615] Step 4:

[1616] The training program generated by the server is sent to the user's terminal, which then displays it to the user. The user then begins training according to the displayed training program.

[1617] Step 5:

[1618] During training, the emotion engine analyzes the user's facial expressions and tone of voice through the device. The emotion data is sent to the server in real time, and the server uses this data to evaluate the user's emotional state.

[1619] Step 6:

[1620] The server adjusts the training program in real time based on the user's emotional state. For example, if the user is feeling stressed, it adds stretches to help them relax. The adjusted program is then sent back to the user's device.

[1621] Step 7:

[1622] After completing a training session, users provide feedback via their device regarding the difficulty level, satisfaction level, and physical condition of the training. The device then sends this feedback to the server.

[1623] Step 8:

[1624] The server analyzes the collected feedback and optimizes the next training program. This allows users to have a more effective training experience.

[1625] 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.

[1626] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1627] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1628] 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.

[1629] Figure 9 shows an 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.

[1630] 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.

[1631] 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.

[1632] 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, motorcycles, etc., 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, for example, based 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.

[1633] 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."

[1634] 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.

[1635] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1636] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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 the like 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.

[1645] 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 as being incorporated by reference.

[1646] The following is further disclosed regarding the embodiments described above.

[1647] (Claim 1)

[1648] A means of inputting the user's physical information,

[1649] A means of conducting a user fitness assessment,

[1650] Means for transmitting the user's physical information and fitness evaluation results to a server,

[1651] The server has means for generating a training program based on the information,

[1652] Means for providing the aforementioned training program to the user,

[1653] A means of collecting user training feedback and optimizing the training program,

[1654] A system that includes this.

[1655] (Claim 2)

[1656] The system according to claim 1, wherein the training program is adjusted in real time based on user feedback.

[1657] (Claim 3)

[1658] The system according to claim 1, wherein the user provides feedback through a terminal.

[1659] "Example 1"

[1660] (Claim 1)

[1661] A means of inputting the user's biometric information,

[1662] A means of conducting a user's motor skills assessment,

[1663] Means for transmitting the user's biometric information and exercise evaluation results to a server,

[1664] The server has means for generating a training program based on the information,

[1665] Means for providing the aforementioned training program to the user,

[1666] A means of collecting user exercise feedback and optimizing the training program,

[1667] The server analyzes data using a generated AI model and generates an optimal training program for the user,

[1668] The server records the user's progress in real time and provides instructions and feedback as needed.

[1669] A system that includes this.

[1670] (Claim 2)

[1671] The system according to claim 1, wherein the training program is adjusted in real time based on user feedback.

[1672] (Claim 3)

[1673] The system according to claim 1, wherein the user provides feedback through a computing device.

[1674] "Application Example 1"

[1675] (Claim 1)

[1676] A means of inputting the user's physical information,

[1677] A means of performing a user's fitness assessment using a smart wearable device or head-mounted display,

[1678] Means for transmitting the user's physical information and fitness evaluation results to a server,

[1679] The server provides means for generating a training program using a generated AI model based on the information,

[1680] Means for providing the aforementioned training program to the user,

[1681] A means of monitoring the user's progress in real time and providing feedback through a smart wearable device or head-mounted display,

[1682] A means of collecting user training feedback and optimizing the training program,

[1683] A system that includes this.

[1684] (Claim 2)

[1685] The system according to claim 1, wherein the training program is adjusted in real time based on user feedback.

[1686] (Claim 3)

[1687] The system according to claim 1, wherein the user provides feedback through a smart wearable device or a head-mounted display.

[1688] "Example 2 of combining an emotion engine"

[1689] (Claim 1)

[1690] A means of inputting the user's biometric information,

[1691] A means of conducting a user's physical fitness assessment,

[1692] Means for transmitting the user's biometric information and physical fitness assessment results to a central processing unit,

[1693] The central processing unit includes means for generating a motion plan based on the information,

[1694] Means for providing the aforementioned exercise plan to the user,

[1695] A means of recognizing emotional states and collecting data,

[1696] A means of adjusting exercise plans in real time based on emotional data,

[1697] A means of collecting user feedback after exercise and optimizing the exercise plan,

[1698] A system that includes this.

[1699] (Claim 2)

[1700] The system according to claim 1, wherein the exercise plan is adjusted in real time based on the user's emotional data.

[1701] (Claim 3)

[1702] The system according to claim 1, wherein the user provides feedback through a terminal.

[1703] "Application example 2 when combining with an emotional engine"

[1704] (Claim 1)

[1705] A means of inputting the user's physical information,

[1706] A means of conducting a user fitness assessment,

[1707] Means for transmitting the user's physical information and fitness evaluation results to a server,

[1708] The server has means for generating a training program based on the information,

[1709] Means for providing the aforementioned training program to the user,

[1710] A means of collecting user training feedback and optimizing the training program,

[1711] A means of collecting user emotional data and analyzing their emotional state in real time,

[1712] A means for generating a training program that reflects the user's emotional state based on the aforementioned emotional data,

[1713] A system that includes this.

[1714] (Claim 2)

[1715] The system according to claim 1, wherein the training program is adjusted in real time based on user feedback and emotional state.

[1716] (Claim 3)

[1717] The system according to claim 1, wherein the user provides feedback and sentiment data through a device. [Explanation of Symbols]

[1718] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting the user's physical information, A means of conducting a user fitness assessment, Means for transmitting the user's physical information and fitness evaluation results to a server, The server has means for generating a training program based on the information, Means for providing the aforementioned training program to the user, A means of collecting user training feedback and optimizing the training program, A system that includes this.

2. The system according to claim 1, wherein the training program is adjusted in real time based on user feedback.

3. The system according to claim 1, wherein the user provides feedback through a terminal.

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