system

Abstract

Provide a system. 【Solution means】 Means for receiving personal information from a user, Means for generating an exercise plan based on the personal information, Means for presenting the exercise plan to the user, Means for monitoring the user's exercise status in real time, Means for providing feedback to the user based on the exercise status, Means for awarding points based on the degree of achievement, Means for calculating a reward based on the points, A system including the above.

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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the increasing health awareness in modern times, many people are interested in starting to exercise, but the problem is that the environment for maintaining appropriate knowledge and motivation is not well-established. Also, it is difficult to create an exercise plan according to individual fitness levels and goals, so there is a problem that effective health management cannot be carried out. Furthermore, there is a lack of a mechanism to obtain feedback and a sense of achievement regarding the progress of exercise, and there is also a problem that continuous exercise is difficult.

Means for Solving the Problems

[0005] This invention provides a means for receiving personal information from a user and generating a customized exercise plan based on that information. The generated exercise plan is presented to the user, and a system is built to provide appropriate feedback by monitoring the user's real-time exercise status. In addition, points can be awarded based on the level of achievement, thereby increasing the user's motivation. Through these means, a system is realized that enables effective health management tailored to individual fitness levels and lifestyles.

[0006] A "user" refers to an individual who uses the system to manage their health or improve their fitness.

[0007] "Personal information" refers to information including data such as the user's age, gender, exercise experience, and fitness goals.

[0008] An "exercise plan" refers to a plan that is generated based on the user's personal information and includes details such as the type, frequency, and intensity of exercise.

[0009] "Real-time" refers to the immediate provision of feedback and data processing during exercise.

[0010] "Feedback" refers to advice and instructions for improving form based on the user's exercise status.

[0011] "Points" refer to a numerical evaluation awarded based on the user's level of exercise achievement.

[0012] "Rewards" refer to the benefits and incentives given to users based on the points they earn. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides a system that generates individually optimized exercise plans based on the user's specific needs and supports continuous fitness activities.

[0035] User input and plan generation

[0036] The first step for the user is to enter personal information through their device. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The device then sends this information to a server. Based on the received information, the server uses a generative AI model to create a personalized exercise plan for the user. This plan adjusts the type, intensity, and frequency of exercise to suit the user's health condition and goals.

[0037] Training implementation and data collection

[0038] Once a training plan is delivered to the device, the user begins exercising according to it. During exercise, the device collects the user's exercise data. This data includes start and end times, type of exercise, distance, heart rate, and calories burned. The user either enters the information after completing each exercise session, or the device automatically records it.

[0039] Providing feedback and monitoring the situation

[0040] The server analyzes data in real time and provides feedback on the user's form and progress. This feedback includes advice on correcting form and what exercises to do next. For example, if the user's heart rate is high, it may notify them to slow down. In addition, the server tracks progress and measures the degree of achievement toward the goal. Points are awarded based on this achievement, and the user can check them.

[0041] Maintaining motivation and reward systems

[0042] To motivate users, a point system is implemented based on their achievements. Users can check their earned points and exchange them for rewards. These rewards are training-related products and perks, encouraging users to continue exercising. For example, a discount coupon for new training gear may be offered as a reward each time a goal is achieved.

[0043] In this way, the system helps users effectively achieve their fitness goals and provides a system for continuous health management.

[0044] The following describes the processing flow.

[0045] Step 1: The user uses their device to enter personal information such as age, gender, weight, height, fitness goals, and exercise experience.

[0046] Step 2: The device sends the entered personal information to the server, which then securely stores the data.

[0047] Step 3: Based on the stored data, the server uses a generating AI model to create a customized exercise plan tailored to the user's fitness level and goals.

[0048] Step 4: The device notifies the user of the generated exercise plan and displays the daily exercise content and schedule.

[0049] Step 5: The user follows the instructions on the device to begin training, and enters the start and end times of the exercise and calories burned into the device as needed.

[0050] Step 6: The device records data such as heart rate and type of exercise in real time during the user's workout and sends it to the server.

[0051] Step 7: The server analyzes the received exercise data and provides feedback to the user, such as suggestions for improving form or recommending the next exercise.

[0052] Step 8: The server evaluates the user's exercise performance, calculates and awards points based on their achievement level.

[0053] Step 9: The device displays calculated feedback, points, and motivational messages to the user, and provides advice for the next workout.

[0054] Step 10: Users continue training based on feedback and maintain motivation by exchanging points for rewards based on the goals they achieve.

[0055] (Example 1)

[0056] 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."

[0057] Traditional fitness systems struggle to generate optimal exercise plans tailored to each user's individual health condition and activity level, provide real-time feedback during exercise, and continuously improve user motivation. Therefore, supporting users in effectively achieving their fitness goals is a challenge.

[0058] 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.

[0059] In this invention, the server includes means for acquiring attribute information from the user, means for creating prompt statements and using a generation AI to generate an exercise plan based on the attribute information, and means for transmitting the generated exercise plan to the terminal via a communication network to present it to the user. This enables the provision of an optimal exercise plan tailored to the user's individual needs and real-time feedback.

[0060] A "user" is an individual who uses the system to achieve their fitness goals.

[0061] "Attribute information" refers to personal data such as the user's age, gender, weight, height, health status, and activity level.

[0062] An "exercise plan" is a guide to specific physical activities, including type, intensity, and frequency, generated based on the user's individual attribute information.

[0063] "Generative AI" is a model that utilizes artificial intelligence to analyze user attribute information and construct an appropriate exercise plan.

[0064] A "prompt message" is a set of instructions or information that a generating AI needs to construct the optimal movement plan.

[0065] A "communication network" is an infrastructure for digital data transfer used to send and receive information between terminals.

[0066] "Feedback" refers to real-time instructions and suggestions provided to users during exercise regarding their progress and how to improve their form.

[0067] "Rewards" refer to incentives such as points or discounts on training equipment that users earn by achieving their exercise goals.

[0068] Overall overview

[0069] This system is designed to support fitness activities by generating exercise plans tailored to the individual needs of each user. The server and terminal communicate via a network, and the system uses a generative AI model to generate prompt messages, providing the user with the optimal fitness plan.

[0070] Hardware and software to be used

[0071] The terminal is envisioned to be an information and communication device such as a wearable device or a smartphone. This allows users to operate it intuitively and input their own attribute information. The server operates in a cloud computing environment, has a generative AI model, and processes user information based on it.

[0072] Data processing and data calculation

[0073] The server constructs prompt messages for the generating AI model based on the received user attribute information. By inputting these prompt messages into the generating AI model, an individually optimized exercise plan is created. The plan is sent to the terminal, which continuously monitors the user's activity, provides feedback as needed, and evaluates the user's progress through data analysis.

[0074] Specific example

[0075] For example, suppose a 30-year-old female user aims to lose 5kg. She uses her device to input her age and past exercise habits. The server prompts the AI ​​model with a message such as, "30 years old, female, 5kg weight loss, wants to exercise 3 times a week." Based on this information, the AI ​​model creates an exercise plan, including running and yoga, and sends the plan to her device. She can then continue exercising according to this plan, receiving feedback while monitoring her progress.

[0076] In this way, by implementing the invention, users can receive a fitness plan tailored to their individual circumstances and obtain specific guidance toward achieving their health goals.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users use a device to input their personal information. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The entered information is collected as basic data to generate an optimal exercise plan and is sent from the device to the server.

[0080] Step 2:

[0081] The server receives user attribute information sent from the terminal. Based on the received information, it generates a prompt message. This prompt message is filtered into a format suitable for input into the generating AI model, and an example would be "30 years old, female, weight loss of 5 kg, desires to exercise 3 times a week." Data analysis and filtering techniques are used to generate the prompt message.

[0082] Step 3:

[0083] The server inputs prompt messages into the generating AI model. Based on the input prompt messages, the generating AI model generates an exercise plan optimized for the user. The generated exercise plan is a detailed activity plan including the type of exercise, intensity, and frequency, and is stored internally on the server in JSON format.

[0084] Step 4:

[0085] The server sends the generated exercise plan to the terminal via the communication network. The terminal visualizes the received exercise plan and displays it on the screen in a way that is easy for the user to understand. For example, icons and explanations are assigned to each type of exercise, and the weekly schedule is presented in a calendar format.

[0086] Step 5:

[0087] The user begins their daily training according to the provided exercise plan. The device records the user's exercise data in real time, continuously collecting metrics such as heart rate, calories burned, and exercise time. This data is managed as information necessary to achieve exercise goals.

[0088] Step 6:

[0089] Exercise data collected by the device is periodically sent to a server. The server analyzes this data and generates feedback about the user's exercise form and progress. For example, if the user's heart rate exceeds the target range, a real-time notification such as "Continue exercising while regulating your breathing" is sent.

[0090] Step 7:

[0091] The server evaluates the user's achievement level based on the analysis results and calculates points. These points are converted into rewards to strengthen the user's motivation. Based on the achievement level and points, rewards may be offered, for example, as discount coupons for training goods usable in an online shop. Users can view and use these rewards through their device.

[0092] (Application Example 1)

[0093] 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."

[0094] Users face challenges in accessing real-time exercise plans tailored to their individual health and fitness levels, and in receiving appropriate feedback. Furthermore, there is a lack of tools to effectively support form improvement and motivation maintenance during exercise.

[0095] 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.

[0096] In this invention, the server includes means for receiving personal data from a user, means for generating an exercise plan based on the personal data, and means for displaying information in real time during exercise using a visual display device. This makes it possible to provide an exercise plan tailored to each user's health condition and fitness goals in real time, and to obtain appropriate feedback during exercise.

[0097] "Personal data" refers to information related to an individual, including the user's age, gender, weight, height, fitness goals, past exercise habits, and daily activity level.

[0098] An "exercise plan" is a plan of activities, including the type, intensity, and frequency of exercise, designed based on the user's individual health condition and fitness goals.

[0099] "Real-time monitoring" refers to a method of instantly observing and recording a user's exercise status and immediately analyzing the data.

[0100] "Feedback" refers to responses and advice provided based on the user's exercise performance, and may include improvements to exercise form.

[0101] "Evaluation" refers to indicators such as points or scores that are awarded based on the user's exercise achievement level.

[0102] "Rewards" refer to incentives provided in accordance with the performance achieved, and may include training-related goods or benefits.

[0103] A "visual display device" is a device that visually presents various information to the user during exercise, and includes smart glasses and similar display devices.

[0104] This system provides exercise plans based on the user's individual needs and supports continuous fitness activities. At the core of the system is a mechanism that uses a generative AI model to create a personalized exercise plan for each user and provides appropriate feedback in real time through a visual display.

[0105] The server processes personal data received from users via smart devices and generates an exercise plan using a generative AI model. This exercise plan is optimized to match the user's health status and goals.

[0106] The device displays an exercise plan to the user and utilizes sensors to monitor exercise status in real time. By using a visual display device (e.g., smart glasses), users can check information such as heart rate and pace in real time during exercise. Furthermore, feedback is displayed as needed if exercise form is inappropriate.

[0107] Each time a user completes a set amount of exercise, the server analyzes the data from the sensors and awards points based on their level of achievement. To motivate users, these evaluation points can be exchanged for training-related rewards.

[0108] For example, when a user is running, if the smart glasses detect that their heart rate has exceeded the target range, they may display an alert to slow down. Additionally, points are immediately added when the user achieves their set goals. This allows users to constantly monitor their progress and use that information to set their next goals.

[0109] An example of a prompt message would be: "20 years old, male, weight 70kg, desires cycling, please set training to 4 times a week. Please generate a fitness plan based on this information." The system will then generate the user's exercise plan based on this information.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user uses a device to enter personal data, including age, gender, weight, and fitness goals. The device sends this data to a server. The server processes the received data and converts it into the required format.

[0113] Step 2:

[0114] The server analyzes the received personal data and inputs it into a generating AI model. This model generates an exercise plan that takes the user's characteristics into account. The output is a plan that specifies the type, intensity, and frequency of exercise that has been individually optimized.

[0115] Step 3:

[0116] The server sends the generated motion plan to the terminal. The terminal presents this plan to the user. The user can view the details of the plan in real time via a visual display device.

[0117] Step 4:

[0118] When a user starts exercising, the device records their exercise status in real time. Sensors collect data such as heart rate, distance, and calories burned. The device then sends this data to a server.

[0119] Step 5:

[0120] The server analyzes the collected exercise data and uses a generative AI model to generate feedback for the user. The output includes advice on how to improve the user's exercise form and appropriate pacing.

[0121] Step 6:

[0122] The server sends feedback to the terminal, which is then displayed to the user via a visual display. The user can then review the feedback in real time and adjust their movements accordingly.

[0123] Step 7:

[0124] Once the user's workout is complete, the server analyzes the workout data and generates evaluation points based on their performance. These evaluation points are linked to the user's achievement of their workout goals.

[0125] Step 8:

[0126] The server calculates rewards based on evaluation points and notifies the user. Users use this to maintain motivation and challenge themselves to achieve their next fitness goals.

[0127] 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.

[0128] This invention is a fitness management system that incorporates a function to recognize the user's emotions, and aims to provide an individually optimized exercise plan, feedback on its execution, and support for improving motivation based on emotion recognition.

[0129] User settings and sentiment data acquisition

[0130] As part of the initial setup, users enter personal information into the device. This personal information includes their fitness level and goals. The device uses its camera and microphone to record the user's facial expressions, voice, and movements, and an emotion engine analyzes this data. Based on this analysis, the server determines the user's emotional state and stores it in a database.

[0131] Generating and adjusting exercise plans

[0132] The server uses a generative AI to generate a personalized exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted according to the user's emotional state to maintain their motivation. For example, if the user is determined to be in a negative emotional state, easier exercises and encouraging messages are provided.

[0133] Real-time exercise monitoring and feedback

[0134] When a user starts exercising, the device collects exercise data in real time and sends it to a server. The server analyzes the received data and provides feedback. This feedback includes advice on exercise form and motivational messages tailored to the user's emotional state. For example, it may send encouraging or praising messages to support the user's successful experience.

[0135] Customization of point systems and rewards

[0136] The server calculates and awards points based on the user's exercise performance and achievements. These points are customized based on the results of the emotion engine and provided as rewards. For example, if the user's emotions are positive, they may be awarded a special bonus for achieving challenging goals.

[0137] In this way, a system incorporating emotion recognition enables flexible fitness management and motivation enhancement tailored to each user's needs, making continuous and effective training possible.

[0138] The following describes the processing flow.

[0139] Step 1: The user enters personal information into the device and configures the settings to enable the emotion engine.

[0140] Step 2: The device collects user facial expressions, voice, and motion data in real time through the camera and microphone, and the emotion engine analyzes the data.

[0141] Step 3: The server receives the analyzed emotion data, determines the user's emotional state, and saves it to the database.

[0142] Step 4: The server uses AI to generate an optimal exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted to be gentle if the user is experiencing negative emotions and challenging if they are experiencing positive emotions.

[0143] Step 5: The device presents the generated exercise plan to the user, and the user begins exercising.

[0144] Step 6: The device records data during exercise (e.g., heart rate, number of repetitions) and continuously monitors emotional changes through the emotion engine.

[0145] Step 7: The server analyzes the collected exercise and emotional data and provides form advice and emotionally responsive feedback.

[0146] Step 8: The user receives real-time feedback through the device and adjusts their exercise based on it.

[0147] Step 9: The server calculates points based on the level of exercise achievement and emotional state, and sends them to the terminal.

[0148] Step 10: The device displays points to the user and notifies them of a reward customized based on their emotions. The user is motivated by the reward and continues training.

[0149] (Example 2)

[0150] 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 device 14 as the "terminal".

[0151] Traditional fitness management systems have difficulty responding to individual user emotional states, making it challenging to optimize exercise plans and maintain sustained motivation. Because exercise difficulty and feedback are not tailored to the user's emotions, user satisfaction may decrease.

[0152] 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.

[0153] In this invention, the server includes means for receiving characteristics from the user, means for analyzing the user's emotional state and adjusting the exercise plan, and means for assigning evaluations based on the degree of achievement and emotional state. This makes it possible to adjust the exercise plan in response to the user's emotions and maintain the user's motivation through an individually optimized feedback and reward system.

[0154] "Features" refer to information such as the user's personal information, fitness level, and emotional state, and serve as the basic data for the system to generate exercise plans and feedback.

[0155] An "exercise plan" is a training program created based on the user's characteristics and is adjusted according to individual needs and emotional state.

[0156] "Emotional state" refers to the user's current psychological state and is a parameter used for system feedback and adjustment of the exercise plan.

[0157] "Real time" is a term that refers to the time interval used to instantly record and analyze a user's exercise status.

[0158] "Responses" refer to feedback provided based on the user's exercise status and emotional state, with the aim of improving exercise form and increasing motivation.

[0159] "Evaluation" refers to points and rewards calculated based on a user's exercise performance and emotional state, and is used to increase motivation.

[0160] This invention is a fitness management system that recognizes the user's emotions and optimizes the fitness exercise plan according to that state. During initial setup, the user enters personal information into the terminal and provides the system with their goals and fitness level. The terminal uses its built-in camera and microphone to record the user's facial expressions, voice, and movements, and sends this data to an emotion engine to analyze the user's emotional state. The emotion engine uses image processing and voice analysis technology to determine the user's emotions. The results of the emotion engine's determination are sent to a server and recorded in a database.

[0161] The server uses a generative AI model to generate a personalized training program based on the received emotional state data and the user's personal information. The generated exercise plan is adjusted in terms of schedule and content according to the user's emotions. The generative AI model used in this process is input with pre-generated prompts. An example prompt is, "The user is currently experiencing negative emotions; please suggest an easy exercise set."

[0162] When a user begins exercising, the device monitors their exercise status in real time and continuously sends information to the server. The server analyzes this information and returns feedback to the device, such as advice on exercise form and encouraging messages. Based on the user's exercise performance and analyzed emotional state, the server calculates points and adjusts rewards. This reward system increases user motivation and encourages continued fitness activities.

[0163] In this way, a system is realized that provides a personalized fitness experience based on the user's characteristics and emotions, enabling it to maximize their performance.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users enter personal information into the fitness management system terminal. This information includes their fitness level and goals. Once this information is entered, the terminal creates a profile and prepares to send it to the server.

[0167] Step 2:

[0168] The device uses its built-in camera and microphone to record the user's facial expressions, voice, and movements. During this process, the camera recognizes the user's face, and the microphone analyzes their voice tone. This data is sent to an emotion engine, where it is analyzed as input data. The emotion engine processes this data to determine the user's emotional state. It then generates emotional state information as output and sends it to the server.

[0169] Step 3:

[0170] The server inputs the user's personal information and emotional state into a generating AI model to create a personalized exercise plan. Based on this input data, the generating AI model calculates the optimal schedule and exercise content for the user and outputs the plan. As a specific example, the prompt "The user is currently experiencing negative emotions; please suggest an easy exercise set" is input to the generating AI model. The server then sends the generated exercise plan to the terminal.

[0171] Step 4:

[0172] When a user begins exercising, the device uses attached sensors to collect exercise data in real time. The sensors record the user's heart rate, exercise speed, form, and other information. This information is sent to a server as input data and used to analyze the user's condition during exercise. The server performs the analysis and prepares to provide appropriate feedback and responses to the user.

[0173] Step 5:

[0174] The server generates feedback based on the analysis of the exercise and the user's emotional state. This feedback includes advice on exercise form and encouraging messages to boost motivation. The generated feedback is sent to the device and provided to the user in real time.

[0175] Step 6:

[0176] Once a user's exercise record is collected, the server calculates points based on their level of achievement and emotional state. These points are treated as an evaluation and sent to the reward system. The reward system adjusts the user's rewards based on this evaluation and provides this information to the user's device. Users can then view their earned points and rewards.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] While conventional fitness management systems have optimized exercise plans based on users' health status and lifestyles, they have lacked adjustments that take into account users' emotional states and provide insufficient motivational support. As a result, users sometimes lost interest in exercise, making it difficult to continue training. Furthermore, in brick-and-mortar fitness gyms, real-time emotional analysis and feedback to individual users have not been adequately implemented. This invention aims to solve these problems and provide a system that offers appropriate feedback and motivation enhancement tailored to each user's emotional state.

[0180] 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.

[0181] In this invention, the server includes means for generating an action plan based on the user's personal data, means including an emotion recognition engine for analyzing the user's emotional state, and means using a generation model that adjusts the exercise plan and generates motivational messages based on emotion recognition. This enables appropriate fitness management and effective motivation enhancement tailored to the user's individual emotional state, as well as real-time feedback and improved user experience in physical stores.

[0182] "Personal data" refers to information about a user's health status, lifestyle, and individual preferences, which is used to customize their fitness plan.

[0183] A "movement plan" is a plan that includes the schedule and content of exercises generated based on the user's personal data and emotional state, and is designed to help users engage in fitness efficiently.

[0184] An "emotion recognition engine" refers to a technology that analyzes and determines a user's emotional state at a given moment based on their facial expressions, voice, and behavior.

[0185] A "generative model" refers to an algorithm that generates appropriate feedback and motivational messages for users based on analyzed data, and generates responses in real time that are tailored to the user's emotional state.

[0186] A "motivational message" is a message provided to encourage continued exercise in accordance with the user's emotional state, and includes encouragement and instructions for fitness activities.

[0187] This invention involves a user inputting personal data using a device such as a smartphone to implement a fitness plan. The device incorporates an emotion recognition engine and a generative model, which analyze the user's emotional state in real time. For this purpose, the device's camera and microphone are used to collect and analyze data on the user's facial expressions and voice.

[0188] The server generates an optimized exercise plan based on collected emotional data. The generated exercise plan is tailored to the user's health condition and lifestyle, and is presented to the user. Furthermore, the server collects user movement data in real time during exercise and adjusts the plan in response to changes in emotional state.

[0189] The generative AI model generates appropriate feedback for the user based on the analysis results. For example, if the exercise is progressing well, a motivational message such as "Great pace! Keep it up!" will be sent. Conversely, if the AI ​​detects that the user's emotional state is negative, it can adjust the plan and provide encouraging messages such as, "Let's relax a bit. How about switching to a lighter workout today?"

[0190] As an example, it is envisioned that a user visiting a gym will continue their training while their emotions are recognized through a device. The exercise plan and motivational messages provided at this time will be dynamically generated according to the emotional state determined from the user's facial expressions and voice.

[0191] The following prompt statements are used as example inputs to the generative AI model.

[0192] "User facial expression data: User data, please generate a feedback message for this user."

[0193] "Generate an exercise plan to be provided when the emotional state is not positive."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] Users enter personal data using their devices. This personal data includes their health status and fitness goals. This data is sent to the server as basic information for generating an exercise plan.

[0197] Step 2:

[0198] The device uses a camera and microphone to record the user's facial expressions and voice in real time, and an emotion recognition engine analyzes this data. This analysis reveals the user's emotional state. This emotional data, along with personal data, is sent to a server.

[0199] Step 3:

[0200] The server uses a generative AI model to generate an individually optimized exercise plan based on the received personal and emotional data. This process analyzes the user's needs and current situation from the data to determine the optimal exercise program.

[0201] Step 4:

[0202] The generated exercise plan is presented to the user via the device. The user then begins exercising based on the proposed plan.

[0203] Step 5:

[0204] During exercise, the device continues to collect user activity data in real time. This includes exercise form, accuracy of movements, and changes in emotions. The collected data is sent to a server and treated as progress data for the exercise.

[0205] Step 6:

[0206] The server analyzes collected motion and emotion data and generates feedback using a generative AI model. This feedback includes instructions for improving movement and motivational messages. This allows users to understand their progress and receive specific actions to further improve.

[0207] Step 7:

[0208] The generated feedback is provided to the user in real time via the device. By receiving this feedback, users can improve the quality of their exercise and maintain their motivation.

[0209] Step 8:

[0210] After completing an exercise, the server awards points based on the user's achievement level. These points are customized based on the results of the emotion engine and offered to the user as a special reward plan.

[0211] Step 9:

[0212] Ultimately, users review the results of their fitness sessions and adjust their goals and plans for the next session. This creates a continuous cycle of fitness improvement.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention provides a system that generates individually optimized exercise plans based on the user's specific needs and supports continuous fitness activities.

[0230] User input and plan generation

[0231] The first step for the user is to enter personal information through their device. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The device then sends this information to a server. Based on the received information, the server uses a generative AI model to create a personalized exercise plan for the user. This plan adjusts the type, intensity, and frequency of exercise to suit the user's health condition and goals.

[0232] Training implementation and data collection

[0233] Once a training plan is delivered to the device, the user begins exercising according to it. During exercise, the device collects the user's exercise data. This data includes start and end times, type of exercise, distance, heart rate, and calories burned. The user either enters the information after completing each exercise session, or the device automatically records it.

[0234] Providing feedback and monitoring the situation

[0235] The server analyzes data in real time and provides feedback on the user's form and progress. This feedback includes advice on correcting form and what exercises to do next. For example, if the user's heart rate is high, it may notify them to slow down. In addition, the server tracks progress and measures the degree of achievement toward the goal. Points are awarded based on this achievement, and the user can check them.

[0236] Maintaining motivation and reward systems

[0237] To motivate users, a point system is implemented based on their achievements. Users can check their earned points and exchange them for rewards. These rewards are training-related products and perks, encouraging users to continue exercising. For example, a discount coupon for new training gear may be offered as a reward each time a goal is achieved.

[0238] In this way, the system helps users effectively achieve their fitness goals and provides a system for continuous health management.

[0239] The following describes the processing flow.

[0240] Step 1: The user uses their device to enter personal information such as age, gender, weight, height, fitness goals, and exercise experience.

[0241] Step 2: The device sends the entered personal information to the server, which then securely stores the data.

[0242] Step 3: Based on the stored data, the server uses a generating AI model to create a customized exercise plan tailored to the user's fitness level and goals.

[0243] Step 4: The device notifies the user of the generated exercise plan and displays the daily exercise content and schedule.

[0244] Step 5: The user follows the instructions on the device to begin training, and enters the start and end times of the exercise and calories burned into the device as needed.

[0245] Step 6: The device records data such as heart rate and type of exercise in real time during the user's workout and sends it to the server.

[0246] Step 7: The server analyzes the received exercise data and provides feedback to the user, such as suggestions for improving form or recommending the next exercise.

[0247] Step 8: The server evaluates the user's exercise performance, calculates and awards points based on their achievement level.

[0248] Step 9: The device displays calculated feedback, points, and motivational messages to the user, and provides advice for the next workout.

[0249] Step 10: Users continue training based on feedback and maintain motivation by exchanging points for rewards based on the goals they achieve.

[0250] (Example 1)

[0251] 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."

[0252] Traditional fitness systems struggle to generate optimal exercise plans tailored to each user's individual health condition and activity level, provide real-time feedback during exercise, and continuously improve user motivation. Therefore, supporting users in effectively achieving their fitness goals is a challenge.

[0253] 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.

[0254] In this invention, the server includes means for acquiring attribute information from the user, means for creating prompt statements and using a generation AI to generate an exercise plan based on the attribute information, and means for transmitting the generated exercise plan to the terminal via a communication network to present it to the user. This enables the provision of an optimal exercise plan tailored to the user's individual needs and real-time feedback.

[0255] A "user" is an individual who uses the system to achieve their fitness goals.

[0256] "Attribute information" refers to personal data such as the user's age, gender, weight, height, health status, and activity level.

[0257] An "exercise plan" is a guide to specific physical activities, including type, intensity, and frequency, generated based on the user's individual attribute information.

[0258] "Generative AI" is a model that utilizes artificial intelligence to analyze user attribute information and construct an appropriate exercise plan.

[0259] A "prompt message" is a set of instructions or information that a generating AI needs to construct the optimal movement plan.

[0260] A "communication network" is an infrastructure for digital data transfer used to send and receive information between terminals.

[0261] "Feedback" refers to real-time instructions and suggestions provided to users during exercise regarding their progress and how to improve their form.

[0262] "Rewards" refer to incentives such as points or discounts on training equipment that users earn by achieving their exercise goals.

[0263] Overall overview

[0264] This system is designed to support fitness activities by generating exercise plans tailored to the individual needs of each user. The server and terminal communicate via a network, and the system uses a generative AI model to generate prompt messages, providing the user with the optimal fitness plan.

[0265] Hardware and software to be used

[0266] The terminal is envisioned to be an information and communication device such as a wearable device or a smartphone. This allows users to operate it intuitively and input their own attribute information. The server operates in a cloud computing environment, has a generative AI model, and processes user information based on it.

[0267] Data processing and data calculation

[0268] The server constructs prompt messages for the generating AI model based on the received user attribute information. By inputting these prompt messages into the generating AI model, an individually optimized exercise plan is created. The plan is sent to the terminal, which continuously monitors the user's activity, provides feedback as needed, and evaluates the user's progress through data analysis.

[0269] Specific example

[0270] For example, suppose a 30-year-old female user aims to lose 5kg. She uses her device to input her age and past exercise habits. The server prompts the AI ​​model with a message such as, "30 years old, female, 5kg weight loss, wants to exercise 3 times a week." Based on this information, the AI ​​model creates an exercise plan, including running and yoga, and sends the plan to her device. She can then continue exercising according to this plan, receiving feedback while monitoring her progress.

[0271] In this way, by implementing the invention, users can receive a fitness plan tailored to their individual circumstances and obtain specific guidance toward achieving their health goals.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] Users use a device to input their personal information. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The entered information is collected as basic data to generate an optimal exercise plan and is sent from the device to the server.

[0275] Step 2:

[0276] The server receives user attribute information sent from the terminal. Based on the received information, it generates a prompt message. This prompt message is filtered into a format suitable for input into the generating AI model, and an example would be "30 years old, female, weight loss of 5 kg, desires to exercise 3 times a week." Data analysis and filtering techniques are used to generate the prompt message.

[0277] Step 3:

[0278] The server inputs prompt messages into the generating AI model. Based on the input prompt messages, the generating AI model generates an exercise plan optimized for the user. The generated exercise plan is a detailed activity plan including the type of exercise, intensity, and frequency, and is stored internally on the server in JSON format.

[0279] Step 4:

[0280] The server sends the generated exercise plan to the terminal via the communication network. The terminal visualizes the received exercise plan and displays it on the screen in a way that is easy for the user to understand. For example, icons and explanations are assigned to each type of exercise, and the weekly schedule is presented in a calendar format.

[0281] Step 5:

[0282] The user starts daily training according to the presented exercise plan. The terminal records the user's exercise data in real time and continuously collects indicators such as heart rate, calories burned, and exercise time. This data is managed as information necessary for achieving the exercise goal.

[0283] Step 6:

[0284] The exercise data collected by the terminal is periodically sent to the server. The server analyzes this data and generates feedback on the user's exercise form and progress. For example, when the user's heart rate exceeds the target range, a real-time notification such as "Continue exercising while adjusting your breathing" is sent.

[0285] Step 7:

[0286] Based on the analysis results, the server evaluates the user's degree of achievement and calculates points. These points are converted into rewards that strengthen the user's motivation. Depending on the degree of achievement and points, for example, they may be provided as discount coupons for training goods available in an online store. The user can confirm and use these rewards through the terminal.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] There is a problem that it is difficult for users to check in real time an exercise plan tailored to each person's health condition and fitness level and obtain appropriate feedback. There is also a problem that there is a lack of tools to effectively support improving the form during exercise and maintaining motivation.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means for receiving personal data from a user, means for generating an exercise plan based on the personal data, and means for displaying information in real time during exercise using a visual display device. This makes it possible to provide an exercise plan tailored to each user's health condition and fitness goals in real time, and to obtain appropriate feedback during exercise.

[0292] "Personal data" refers to information related to an individual, including the user's age, gender, weight, height, fitness goals, past exercise habits, and daily activity level.

[0293] An "exercise plan" is a plan of activities, including the type, intensity, and frequency of exercise, designed based on the user's individual health condition and fitness goals.

[0294] "Real-time monitoring" refers to a method of instantly observing and recording a user's exercise status and immediately analyzing the data.

[0295] "Feedback" refers to responses and advice provided based on the user's exercise performance, and may include improvements to exercise form.

[0296] "Evaluation" refers to indicators such as points or scores that are awarded based on the user's exercise achievement level.

[0297] "Rewards" refer to incentives provided in accordance with the performance achieved, and may include training-related goods or benefits.

[0298] A "visual display device" is a device that visually presents various information to the user during exercise, and includes smart glasses and similar display devices.

[0299] This system provides exercise plans based on the user's individual needs and supports continuous fitness activities. At the core of the system is a mechanism that uses a generative AI model to create a personalized exercise plan for each user and provides appropriate feedback in real time through a visual display.

[0300] The server processes personal data received from users via smart devices and generates an exercise plan using a generative AI model. This exercise plan is optimized to match the user's health status and goals.

[0301] The device displays an exercise plan to the user and utilizes sensors to monitor exercise status in real time. By using a visual display device (e.g., smart glasses), users can check information such as heart rate and pace in real time during exercise. Furthermore, feedback is displayed as needed if exercise form is inappropriate.

[0302] Each time a user completes a set amount of exercise, the server analyzes the data from the sensors and awards points based on their level of achievement. To motivate users, these evaluation points can be exchanged for training-related rewards.

[0303] For example, when a user is running, if the smart glasses detect that their heart rate has exceeded the target range, they may display an alert to slow down. Additionally, points are immediately added when the user achieves their set goals. This allows users to constantly monitor their progress and use that information to set their next goals.

[0304] An example of a prompt message would be: "20 years old, male, weight 70kg, desires cycling, please set training to 4 times a week. Please generate a fitness plan based on this information." The system will then generate the user's exercise plan based on this information.

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1:

[0307] The user uses the terminal to input personal data. The input content includes age, gender, weight, fitness goals, etc. The terminal transmits these data to the server. The server processes the received data and converts it into the required format.

[0308] Step 2:

[0309] The server analyzes the received personal data and inputs it into the generated AI model. This model generates an exercise plan considering the user's characteristics. As output, a plan regarding the type, intensity, and frequency of individually optimized exercises is obtained.

[0310] Step 3:

[0311] The server transmits the generated exercise plan to the terminal. The terminal presents this plan to the user. The user can view the details of the plan in real time via the visual display device.

[0312] Step 4:

[0313] When the user starts exercising, the terminal records the exercise situation in real time. The sensor collects data such as heart rate, distance, calorie consumption, etc. The terminal transmits these data to the server.

[0314] Step 5:

[0315] The server analyzes the collected exercise data and uses the generated AI model to generate feedback for the user. As output, advice on improving the user's exercise form and appropriate pace can be obtained.

[0316] Step 6:

[0317] The server sends feedback to the terminal, which is then displayed to the user via a visual display. The user can then review the feedback in real time and adjust their movements accordingly.

[0318] Step 7:

[0319] Once the user's workout is complete, the server analyzes the workout data and generates evaluation points based on their performance. These evaluation points are linked to the user's achievement of their workout goals.

[0320] Step 8:

[0321] The server calculates rewards based on evaluation points and notifies the user. Users use this to maintain motivation and challenge themselves to achieve their next fitness goals.

[0322] 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.

[0323] This invention is a fitness management system that incorporates a function to recognize the user's emotions, and aims to provide an individually optimized exercise plan, feedback on its execution, and support for improving motivation based on emotion recognition.

[0324] User settings and sentiment data acquisition

[0325] As part of the initial setup, users enter personal information into the device. This personal information includes their fitness level and goals. The device uses its camera and microphone to record the user's facial expressions, voice, and movements, and an emotion engine analyzes this data. Based on this analysis, the server determines the user's emotional state and stores it in a database.

[0326] Generating and adjusting exercise plans

[0327] The server uses a generative AI to generate a personalized exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted according to the user's emotional state to maintain their motivation. For example, if the user is determined to be in a negative emotional state, easier exercises and encouraging messages are provided.

[0328] Real-time exercise monitoring and feedback

[0329] When a user starts exercising, the device collects exercise data in real time and sends it to a server. The server analyzes the received data and provides feedback. This feedback includes advice on exercise form and motivational messages tailored to the user's emotional state. For example, it may send encouraging or praising messages to support the user's successful experience.

[0330] Customization of point systems and rewards

[0331] The server calculates and awards points based on the user's exercise performance and achievements. These points are customized based on the results of the emotion engine and provided as rewards. For example, if the user's emotions are positive, they may be awarded a special bonus for achieving challenging goals.

[0332] In this way, a system incorporating emotion recognition enables flexible fitness management and motivation enhancement tailored to each user's needs, making continuous and effective training possible.

[0333] The following describes the processing flow.

[0334] Step 1: The user enters personal information into the device and configures the settings to enable the emotion engine.

[0335] Step 2: The device collects user facial expressions, voice, and motion data in real time through the camera and microphone, and the emotion engine analyzes the data.

[0336] Step 3: The server receives the analyzed emotion data, determines the user's emotional state, and saves it to the database.

[0337] Step 4: The server uses AI to generate an optimal exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted to be gentle if the user is experiencing negative emotions and challenging if they are experiencing positive emotions.

[0338] Step 5: The device presents the generated exercise plan to the user, and the user begins exercising.

[0339] Step 6: The device records data during exercise (e.g., heart rate, number of repetitions) and continuously monitors emotional changes through the emotion engine.

[0340] Step 7: The server analyzes the collected exercise and emotional data and provides form advice and emotionally responsive feedback.

[0341] Step 8: The user receives real-time feedback through the device and adjusts their exercise based on it.

[0342] Step 9: The server calculates points based on the level of exercise achievement and emotional state, and sends them to the terminal.

[0343] Step 10: The device displays points to the user and notifies them of a reward customized based on their emotions. The user is motivated by the reward and continues training.

[0344] (Example 2)

[0345] 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".

[0346] Traditional fitness management systems have difficulty responding to individual user emotional states, making it challenging to optimize exercise plans and maintain sustained motivation. Because exercise difficulty and feedback are not tailored to the user's emotions, user satisfaction may decrease.

[0347] 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.

[0348] In this invention, the server includes means for receiving characteristics from the user, means for analyzing the user's emotional state and adjusting the exercise plan, and means for assigning evaluations based on the degree of achievement and emotional state. This makes it possible to adjust the exercise plan in response to the user's emotions and maintain the user's motivation through an individually optimized feedback and reward system.

[0349] "Features" refer to information such as the user's personal information, fitness level, and emotional state, and serve as the basic data for the system to generate exercise plans and feedback.

[0350] An "exercise plan" is a training program created based on the user's characteristics and is adjusted according to individual needs and emotional state.

[0351] "Emotional state" refers to the user's current psychological state and is a parameter used for system feedback and adjustment of the exercise plan.

[0352] "Real time" is a term that refers to the time interval used to instantly record and analyze a user's exercise status.

[0353] "Responses" refer to feedback provided based on the user's exercise status and emotional state, with the aim of improving exercise form and increasing motivation.

[0354] "Evaluation" refers to points and rewards calculated based on a user's exercise performance and emotional state, and is used to increase motivation.

[0355] This invention is a fitness management system that recognizes the user's emotions and optimizes the fitness exercise plan according to that state. During initial setup, the user enters personal information into the terminal and provides the system with their goals and fitness level. The terminal uses its built-in camera and microphone to record the user's facial expressions, voice, and movements, and sends this data to an emotion engine to analyze the user's emotional state. The emotion engine uses image processing and voice analysis technology to determine the user's emotions. The results of the emotion engine's determination are sent to a server and recorded in a database.

[0356] The server uses a generative AI model to generate a personalized training program based on the received emotional state data and the user's personal information. The generated exercise plan is adjusted in terms of schedule and content according to the user's emotions. The generative AI model used in this process is input with pre-generated prompts. An example prompt is, "The user is currently experiencing negative emotions; please suggest an easy exercise set."

[0357] When a user begins exercising, the device monitors their exercise status in real time and continuously sends information to the server. The server analyzes this information and returns feedback to the device, such as advice on exercise form and encouraging messages. Based on the user's exercise performance and analyzed emotional state, the server calculates points and adjusts rewards. This reward system increases user motivation and encourages continued fitness activities.

[0358] In this way, a system is realized that provides a personalized fitness experience based on the user's characteristics and emotions, enabling it to maximize their performance.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] Users enter personal information into the fitness management system terminal. This information includes their fitness level and goals. Once this information is entered, the terminal creates a profile and prepares to send it to the server.

[0362] Step 2:

[0363] The device uses its built-in camera and microphone to record the user's facial expressions, voice, and movements. During this process, the camera recognizes the user's face, and the microphone analyzes their voice tone. This data is sent to an emotion engine, where it is analyzed as input data. The emotion engine processes this data to determine the user's emotional state. It then generates emotional state information as output and sends it to the server.

[0364] Step 3:

[0365] The server inputs the user's personal information and emotional state into a generating AI model to create a personalized exercise plan. Based on this input data, the generating AI model calculates the optimal schedule and exercise content for the user and outputs the plan. As a specific example, the prompt "The user is currently experiencing negative emotions; please suggest an easy exercise set" is input to the generating AI model. The server then sends the generated exercise plan to the terminal.

[0366] Step 4:

[0367] When a user begins exercising, the device uses attached sensors to collect exercise data in real time. The sensors record the user's heart rate, exercise speed, form, and other information. This information is sent to a server as input data and used to analyze the user's condition during exercise. The server performs the analysis and prepares to provide appropriate feedback and responses to the user.

[0368] Step 5:

[0369] The server generates feedback based on the analysis of the exercise and the user's emotional state. This feedback includes advice on exercise form and encouraging messages to boost motivation. The generated feedback is sent to the device and provided to the user in real time.

[0370] Step 6:

[0371] Once a user's exercise record is collected, the server calculates points based on their level of achievement and emotional state. These points are treated as an evaluation and sent to the reward system. The reward system adjusts the user's rewards based on this evaluation and provides this information to the user's device. Users can then view their earned points and rewards.

[0372] (Application Example 2)

[0373] 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."

[0374] While conventional fitness management systems have optimized exercise plans based on users' health status and lifestyles, they have lacked adjustments that take into account users' emotional states and provide insufficient motivational support. As a result, users sometimes lost interest in exercise, making it difficult to continue training. Furthermore, in brick-and-mortar fitness gyms, real-time emotional analysis and feedback to individual users have not been adequately implemented. This invention aims to solve these problems and provide a system that offers appropriate feedback and motivation enhancement tailored to each user's emotional state.

[0375] 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.

[0376] In this invention, the server includes means for generating an action plan based on the user's personal data, means including an emotion recognition engine for analyzing the user's emotional state, and means using a generation model that adjusts the exercise plan and generates motivational messages based on emotion recognition. This enables appropriate fitness management and effective motivation enhancement tailored to the user's individual emotional state, as well as real-time feedback and improved user experience in physical stores.

[0377] "Personal data" refers to information about a user's health status, lifestyle, and individual preferences, which is used to customize their fitness plan.

[0378] A "movement plan" is a plan that includes the schedule and content of exercises generated based on the user's personal data and emotional state, and is designed to help users engage in fitness efficiently.

[0379] An "emotion recognition engine" refers to a technology that analyzes and determines a user's emotional state at a given moment based on their facial expressions, voice, and behavior.

[0380] A "generative model" refers to an algorithm that generates appropriate feedback and motivational messages for users based on analyzed data, and generates responses in real time that are tailored to the user's emotional state.

[0381] A "motivational message" is a message provided to encourage continued exercise in accordance with the user's emotional state, and includes encouragement and instructions for fitness activities.

[0382] This invention involves a user inputting personal data using a device such as a smartphone to implement a fitness plan. The device incorporates an emotion recognition engine and a generative model, which analyze the user's emotional state in real time. For this purpose, the device's camera and microphone are used to collect and analyze data on the user's facial expressions and voice.

[0383] The server generates an optimized exercise plan based on collected emotional data. The generated exercise plan is tailored to the user's health condition and lifestyle, and is presented to the user. Furthermore, the server collects user movement data in real time during exercise and adjusts the plan in response to changes in emotional state.

[0384] The generative AI model generates appropriate feedback for the user based on the analysis results. For example, if the exercise is progressing well, a motivational message such as "Great pace! Keep it up!" will be sent. Conversely, if the AI ​​detects that the user's emotional state is negative, it can adjust the plan and provide encouraging messages such as, "Let's relax a bit. How about switching to a lighter workout today?"

[0385] As an example, it is envisioned that a user visiting a gym will continue their training while their emotions are recognized through a device. The exercise plan and motivational messages provided at this time will be dynamically generated according to the emotional state determined from the user's facial expressions and voice.

[0386] The following prompt statements are used as example inputs to the generative AI model.

[0387] "User facial expression data: User data, please generate a feedback message for this user."

[0388] "Generate an exercise plan to be provided when the emotional state is not positive."

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] Users enter personal data using their devices. This personal data includes their health status and fitness goals. This data is sent to the server as basic information for generating an exercise plan.

[0392] Step 2:

[0393] The device uses a camera and microphone to record the user's facial expressions and voice in real time, and an emotion recognition engine analyzes this data. This analysis reveals the user's emotional state. This emotional data, along with personal data, is sent to a server.

[0394] Step 3:

[0395] The server uses a generative AI model to generate an individually optimized exercise plan based on the received personal and emotional data. This process analyzes the user's needs and current situation from the data to determine the optimal exercise program.

[0396] Step 4:

[0397] The generated exercise plan is presented to the user via the device. The user then begins exercising based on the proposed plan.

[0398] Step 5:

[0399] During exercise, the device continues to collect user activity data in real time. This includes exercise form, accuracy of movements, and changes in emotions. The collected data is sent to a server and treated as progress data for the exercise.

[0400] Step 6:

[0401] The server analyzes collected motion and emotion data and generates feedback using a generative AI model. This feedback includes instructions for improving movement and motivational messages. This allows users to understand their progress and receive specific actions to further improve.

[0402] Step 7:

[0403] The generated feedback is provided to the user in real time via the device. By receiving this feedback, users can improve the quality of their exercise and maintain their motivation.

[0404] Step 8:

[0405] After completing an exercise, the server awards points based on the user's achievement level. These points are customized based on the results of the emotion engine and offered to the user as a special reward plan.

[0406] Step 9:

[0407] Ultimately, users review the results of their fitness sessions and adjust their goals and plans for the next session. This creates a continuous cycle of fitness improvement.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention provides a system that generates individually optimized exercise plans based on the user's specific needs and supports continuous fitness activities.

[0425] User input and plan generation

[0426] The first step for the user is to enter personal information through their device. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The device then sends this information to a server. Based on the received information, the server uses a generative AI model to create a personalized exercise plan for the user. This plan adjusts the type, intensity, and frequency of exercise to suit the user's health condition and goals.

[0427] Training implementation and data collection

[0428] Once a training plan is delivered to the device, the user begins exercising according to it. During exercise, the device collects the user's exercise data. This data includes start and end times, type of exercise, distance, heart rate, and calories burned. The user either enters the information after completing each exercise session, or the device automatically records it.

[0429] Providing feedback and monitoring the situation

[0430] The server analyzes data in real time and provides feedback on the user's form and progress. This feedback includes advice on correcting form and what exercises to do next. For example, if the user's heart rate is high, it may notify them to slow down. In addition, the server tracks progress and measures the degree of achievement toward the goal. Points are awarded based on this achievement, and the user can check them.

[0431] Maintaining motivation and reward systems

[0432] To motivate users, a point system is implemented based on their achievements. Users can check their earned points and exchange them for rewards. These rewards are training-related products and perks, encouraging users to continue exercising. For example, a discount coupon for new training gear may be offered as a reward each time a goal is achieved.

[0433] In this way, the system helps users effectively achieve their fitness goals and provides a system for continuous health management.

[0434] The following describes the processing flow.

[0435] Step 1: The user uses their device to enter personal information such as age, gender, weight, height, fitness goals, and exercise experience.

[0436] Step 2: The device sends the entered personal information to the server, which then securely stores the data.

[0437] Step 3: Based on the stored data, the server uses a generating AI model to create a customized exercise plan tailored to the user's fitness level and goals.

[0438] Step 4: The device notifies the user of the generated exercise plan and displays the daily exercise content and schedule.

[0439] Step 5: The user follows the instructions on the device to begin training, and enters the start and end times of the exercise and calories burned into the device as needed.

[0440] Step 6: The device records data such as heart rate and type of exercise in real time during the user's workout and sends it to the server.

[0441] Step 7: The server analyzes the received exercise data and provides feedback to the user, such as suggestions for improving form or recommending the next exercise.

[0442] Step 8: The server evaluates the user's exercise performance, calculates and awards points based on their achievement level.

[0443] Step 9: The device displays calculated feedback, points, and motivational messages to the user, and provides advice for the next workout.

[0444] Step 10: Users continue training based on feedback and maintain motivation by exchanging points for rewards based on the goals they achieve.

[0445] (Example 1)

[0446] 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."

[0447] Traditional fitness systems struggle to generate optimal exercise plans tailored to each user's individual health condition and activity level, provide real-time feedback during exercise, and continuously improve user motivation. Therefore, supporting users in effectively achieving their fitness goals is a challenge.

[0448] 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.

[0449] In this invention, the server includes means for acquiring attribute information from the user, means for creating prompt statements and using a generation AI to generate an exercise plan based on the attribute information, and means for transmitting the generated exercise plan to the terminal via a communication network to present it to the user. This enables the provision of an optimal exercise plan tailored to the user's individual needs and real-time feedback.

[0450] A "user" is an individual who uses the system to achieve their fitness goals.

[0451] "Attribute information" refers to personal data such as the user's age, gender, weight, height, health status, and activity level.

[0452] An "exercise plan" is a guide to specific physical activities, including type, intensity, and frequency, generated based on the user's individual attribute information.

[0453] "Generative AI" is a model that utilizes artificial intelligence to analyze user attribute information and construct an appropriate exercise plan.

[0454] A "prompt message" is a set of instructions or information that a generating AI needs to construct the optimal movement plan.

[0455] A "communication network" is an infrastructure for digital data transfer used to send and receive information between terminals.

[0456] "Feedback" refers to real-time instructions and suggestions provided to users during exercise regarding their progress and how to improve their form.

[0457] "Rewards" refer to incentives such as points or discounts on training equipment that users earn by achieving their exercise goals.

[0458] Overall overview

[0459] This system is designed to support fitness activities by generating exercise plans tailored to the individual needs of each user. The server and terminal communicate via a network, and the system uses a generative AI model to generate prompt messages, providing the user with the optimal fitness plan.

[0460] Hardware and software to be used

[0461] The terminal is envisioned to be an information and communication device such as a wearable device or a smartphone. This allows users to operate it intuitively and input their own attribute information. The server operates in a cloud computing environment, has a generative AI model, and processes user information based on it.

[0462] Data processing and data calculation

[0463] The server constructs prompt messages for the generating AI model based on the received user attribute information. By inputting these prompt messages into the generating AI model, an individually optimized exercise plan is created. The plan is sent to the terminal, which continuously monitors the user's activity, provides feedback as needed, and evaluates the user's progress through data analysis.

[0464] Specific example

[0465] For example, suppose a 30-year-old female user aims to lose 5kg. She uses her device to input her age and past exercise habits. The server prompts the AI ​​model with a message such as, "30 years old, female, 5kg weight loss, wants to exercise 3 times a week." Based on this information, the AI ​​model creates an exercise plan, including running and yoga, and sends the plan to her device. She can then continue exercising according to this plan, receiving feedback while monitoring her progress.

[0466] In this way, by implementing the invention, users can receive a fitness plan tailored to their individual circumstances and obtain specific guidance toward achieving their health goals.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] Users use a device to input their personal information. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The entered information is collected as basic data to generate an optimal exercise plan and is sent from the device to the server.

[0470] Step 2:

[0471] The server receives user attribute information sent from the terminal. Based on the received information, it generates a prompt message. This prompt message is filtered into a format suitable for input into the generating AI model, and an example would be "30 years old, female, weight loss of 5 kg, desires to exercise 3 times a week." Data analysis and filtering techniques are used to generate the prompt message.

[0472] Step 3:

[0473] The server inputs prompt messages into the generating AI model. Based on the input prompt messages, the generating AI model generates an exercise plan optimized for the user. The generated exercise plan is a detailed activity plan including the type of exercise, intensity, and frequency, and is stored internally on the server in JSON format.

[0474] Step 4:

[0475] The server sends the generated exercise plan to the terminal via the communication network. The terminal visualizes the received exercise plan and displays it on the screen in a way that is easy for the user to understand. For example, icons and explanations are assigned to each type of exercise, and the weekly schedule is presented in a calendar format.

[0476] Step 5:

[0477] The user begins their daily training according to the provided exercise plan. The device records the user's exercise data in real time, continuously collecting metrics such as heart rate, calories burned, and exercise time. This data is managed as information necessary to achieve exercise goals.

[0478] Step 6:

[0479] Exercise data collected by the device is periodically sent to a server. The server analyzes this data and generates feedback about the user's exercise form and progress. For example, if the user's heart rate exceeds the target range, a real-time notification such as "Continue exercising while regulating your breathing" is sent.

[0480] Step 7:

[0481] The server evaluates the user's achievement level based on the analysis results and calculates points. These points are converted into rewards to strengthen the user's motivation. Based on the achievement level and points, rewards may be offered, for example, as discount coupons for training goods usable in an online shop. Users can view and use these rewards through their device.

[0482] (Application Example 1)

[0483] 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."

[0484] Users face challenges in accessing real-time exercise plans tailored to their individual health and fitness levels, and in receiving appropriate feedback. Furthermore, there is a lack of tools to effectively support form improvement and motivation maintenance during exercise.

[0485] 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.

[0486] In this invention, the server includes means for receiving personal data from a user, means for generating an exercise plan based on the personal data, and means for displaying information in real time during exercise using a visual display device. This makes it possible to provide an exercise plan tailored to each user's health condition and fitness goals in real time, and to obtain appropriate feedback during exercise.

[0487] "Personal data" refers to information related to an individual, including the user's age, gender, weight, height, fitness goals, past exercise habits, and daily activity level.

[0488] An "exercise plan" is a plan of activities, including the type, intensity, and frequency of exercise, designed based on the user's individual health condition and fitness goals.

[0489] "Real-time monitoring" refers to a method of instantly observing and recording a user's exercise status and immediately analyzing the data.

[0490] "Feedback" refers to responses and advice provided based on the user's exercise performance, and may include improvements to exercise form.

[0491] "Evaluation" refers to indicators such as points or scores that are awarded based on the user's exercise achievement level.

[0492] "Rewards" refer to incentives provided in accordance with the performance achieved, and may include training-related goods or benefits.

[0493] A "visual display device" is a device that visually presents various information to the user during exercise, and includes smart glasses and similar display devices.

[0494] This system provides exercise plans based on the user's individual needs and supports continuous fitness activities. At the core of the system is a mechanism that uses a generative AI model to create a personalized exercise plan for each user and provides appropriate feedback in real time through a visual display.

[0495] The server processes personal data received from users via smart devices and generates an exercise plan using a generative AI model. This exercise plan is optimized to match the user's health status and goals.

[0496] The device displays an exercise plan to the user and utilizes sensors to monitor exercise status in real time. By using a visual display device (e.g., smart glasses), users can check information such as heart rate and pace in real time during exercise. Furthermore, feedback is displayed as needed if exercise form is inappropriate.

[0497] Each time a user completes a set amount of exercise, the server analyzes the data from the sensors and awards points based on their level of achievement. To motivate users, these evaluation points can be exchanged for training-related rewards.

[0498] For example, when a user is running, if the smart glasses detect that their heart rate has exceeded the target range, they may display an alert to slow down. Additionally, points are immediately added when the user achieves their set goals. This allows users to constantly monitor their progress and use that information to set their next goals.

[0499] An example of a prompt message would be: "20 years old, male, weight 70kg, desires cycling, please set training to 4 times a week. Please generate a fitness plan based on this information." The system will then generate the user's exercise plan based on this information.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The user uses a device to enter personal data, including age, gender, weight, and fitness goals. The device sends this data to a server. The server processes the received data and converts it into the required format.

[0503] Step 2:

[0504] The server analyzes the received personal data and inputs it into a generating AI model. This model generates an exercise plan that takes the user's characteristics into account. The output is a plan that specifies the type, intensity, and frequency of exercise that has been individually optimized.

[0505] Step 3:

[0506] The server sends the generated motion plan to the terminal. The terminal presents this plan to the user. The user can view the details of the plan in real time via a visual display device.

[0507] Step 4:

[0508] When a user starts exercising, the device records their exercise status in real time. Sensors collect data such as heart rate, distance, and calories burned. The device then sends this data to a server.

[0509] Step 5:

[0510] The server analyzes the collected exercise data and uses a generative AI model to generate feedback for the user. The output includes advice on how to improve the user's exercise form and appropriate pacing.

[0511] Step 6:

[0512] The server sends feedback to the terminal, which is then displayed to the user via a visual display. The user can then review the feedback in real time and adjust their movements accordingly.

[0513] Step 7:

[0514] Once the user's workout is complete, the server analyzes the workout data and generates evaluation points based on their performance. These evaluation points are linked to the user's achievement of their workout goals.

[0515] Step 8:

[0516] The server calculates rewards based on evaluation points and notifies the user. Users use this to maintain motivation and challenge themselves to achieve their next fitness goals.

[0517] 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.

[0518] This invention is a fitness management system that incorporates a function to recognize the user's emotions, and aims to provide an individually optimized exercise plan, feedback on its execution, and support for improving motivation based on emotion recognition.

[0519] User settings and sentiment data acquisition

[0520] As part of the initial setup, users enter personal information into the device. This personal information includes their fitness level and goals. The device uses its camera and microphone to record the user's facial expressions, voice, and movements, and an emotion engine analyzes this data. Based on this analysis, the server determines the user's emotional state and stores it in a database.

[0521] Generating and adjusting exercise plans

[0522] The server uses a generative AI to generate a personalized exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted according to the user's emotional state to maintain their motivation. For example, if the user is determined to be in a negative emotional state, easier exercises and encouraging messages are provided.

[0523] Real-time exercise monitoring and feedback

[0524] When a user starts exercising, the device collects exercise data in real time and sends it to a server. The server analyzes the received data and provides feedback. This feedback includes advice on exercise form and motivational messages tailored to the user's emotional state. For example, it may send encouraging or praising messages to support the user's successful experience.

[0525] Customization of point systems and rewards

[0526] The server calculates and awards points based on the user's exercise performance and achievements. These points are customized based on the results of the emotion engine and provided as rewards. For example, if the user's emotions are positive, they may be awarded a special bonus for achieving challenging goals.

[0527] In this way, a system incorporating emotion recognition enables flexible fitness management and motivation enhancement tailored to each user's needs, making continuous and effective training possible.

[0528] The following describes the processing flow.

[0529] Step 1: The user enters personal information into the device and configures the settings to enable the emotion engine.

[0530] Step 2: The device collects user facial expressions, voice, and motion data in real time through the camera and microphone, and the emotion engine analyzes the data.

[0531] Step 3: The server receives the analyzed emotion data, determines the user's emotional state, and saves it to the database.

[0532] Step 4: The server uses AI to generate an optimal exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted to be gentle if the user is experiencing negative emotions and challenging if they are experiencing positive emotions.

[0533] Step 5: The device presents the generated exercise plan to the user, and the user begins exercising.

[0534] Step 6: The device records data during exercise (e.g., heart rate, number of repetitions) and continuously monitors emotional changes through the emotion engine.

[0535] Step 7: The server analyzes the collected exercise and emotional data and provides form advice and emotionally responsive feedback.

[0536] Step 8: The user receives real-time feedback through the device and adjusts their exercise based on it.

[0537] Step 9: The server calculates points based on the level of exercise achievement and emotional state, and sends them to the terminal.

[0538] Step 10: The device displays points to the user and notifies them of a reward customized based on their emotions. The user is motivated by the reward and continues training.

[0539] (Example 2)

[0540] 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."

[0541] Traditional fitness management systems have difficulty responding to individual user emotional states, making it challenging to optimize exercise plans and maintain sustained motivation. Because exercise difficulty and feedback are not tailored to the user's emotions, user satisfaction may decrease.

[0542] 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.

[0543] In this invention, the server includes means for receiving characteristics from the user, means for analyzing the user's emotional state and adjusting the exercise plan, and means for assigning evaluations based on the degree of achievement and emotional state. This makes it possible to adjust the exercise plan in response to the user's emotions and maintain the user's motivation through an individually optimized feedback and reward system.

[0544] "Features" refer to information such as the user's personal information, fitness level, and emotional state, and serve as the basic data for the system to generate exercise plans and feedback.

[0545] An "exercise plan" is a training program created based on the user's characteristics and is adjusted according to individual needs and emotional state.

[0546] "Emotional state" refers to the user's current psychological state and is a parameter used for system feedback and adjustment of the exercise plan.

[0547] "Real time" is a term that refers to the time interval used to instantly record and analyze a user's exercise status.

[0548] "Responses" refer to feedback provided based on the user's exercise status and emotional state, with the aim of improving exercise form and increasing motivation.

[0549] "Evaluation" refers to points and rewards calculated based on a user's exercise performance and emotional state, and is used to increase motivation.

[0550] This invention is a fitness management system that recognizes the user's emotions and optimizes the fitness exercise plan according to that state. During initial setup, the user enters personal information into the terminal and provides the system with their goals and fitness level. The terminal uses its built-in camera and microphone to record the user's facial expressions, voice, and movements, and sends this data to an emotion engine to analyze the user's emotional state. The emotion engine uses image processing and voice analysis technology to determine the user's emotions. The results of the emotion engine's determination are sent to a server and recorded in a database.

[0551] The server uses a generative AI model to generate a personalized training program based on the received emotional state data and the user's personal information. The generated exercise plan is adjusted in terms of schedule and content according to the user's emotions. The generative AI model used in this process is input with pre-generated prompts. An example prompt is, "The user is currently experiencing negative emotions; please suggest an easy exercise set."

[0552] When a user begins exercising, the device monitors their exercise status in real time and continuously sends information to the server. The server analyzes this information and returns feedback to the device, such as advice on exercise form and encouraging messages. Based on the user's exercise performance and analyzed emotional state, the server calculates points and adjusts rewards. This reward system increases user motivation and encourages continued fitness activities.

[0553] In this way, a system is realized that provides a personalized fitness experience based on the user's characteristics and emotions, enabling it to maximize their performance.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] Users enter personal information into the fitness management system terminal. This information includes their fitness level and goals. Once this information is entered, the terminal creates a profile and prepares to send it to the server.

[0557] Step 2:

[0558] The device uses its built-in camera and microphone to record the user's facial expressions, voice, and movements. During this process, the camera recognizes the user's face, and the microphone analyzes their voice tone. This data is sent to an emotion engine, where it is analyzed as input data. The emotion engine processes this data to determine the user's emotional state. It then generates emotional state information as output and sends it to the server.

[0559] Step 3:

[0560] The server inputs the user's personal information and emotional state into a generating AI model to create a personalized exercise plan. Based on this input data, the generating AI model calculates the optimal schedule and exercise content for the user and outputs the plan. As a specific example, the prompt "The user is currently experiencing negative emotions; please suggest an easy exercise set" is input to the generating AI model. The server then sends the generated exercise plan to the terminal.

[0561] Step 4:

[0562] When a user begins exercising, the device uses attached sensors to collect exercise data in real time. The sensors record the user's heart rate, exercise speed, form, and other information. This information is sent to a server as input data and used to analyze the user's condition during exercise. The server performs the analysis and prepares to provide appropriate feedback and responses to the user.

[0563] Step 5:

[0564] The server generates feedback based on the analysis of the exercise and the user's emotional state. This feedback includes advice on exercise form and encouraging messages to boost motivation. The generated feedback is sent to the device and provided to the user in real time.

[0565] Step 6:

[0566] Once a user's exercise record is collected, the server calculates points based on their level of achievement and emotional state. These points are treated as an evaluation and sent to the reward system. The reward system adjusts the user's rewards based on this evaluation and provides this information to the user's device. Users can then view their earned points and rewards.

[0567] (Application Example 2)

[0568] 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."

[0569] While conventional fitness management systems have optimized exercise plans based on users' health status and lifestyles, they have lacked adjustments that take into account users' emotional states and provide insufficient motivational support. As a result, users sometimes lost interest in exercise, making it difficult to continue training. Furthermore, in brick-and-mortar fitness gyms, real-time emotional analysis and feedback to individual users have not been adequately implemented. This invention aims to solve these problems and provide a system that offers appropriate feedback and motivation enhancement tailored to each user's emotional state.

[0570] 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.

[0571] In this invention, the server includes means for generating an action plan based on the user's personal data, means including an emotion recognition engine for analyzing the user's emotional state, and means using a generation model that adjusts the exercise plan and generates motivational messages based on emotion recognition. This enables appropriate fitness management and effective motivation enhancement tailored to the user's individual emotional state, as well as real-time feedback and improved user experience in physical stores.

[0572] "Personal data" refers to information about a user's health status, lifestyle, and individual preferences, which is used to customize their fitness plan.

[0573] A "movement plan" is a plan that includes the schedule and content of exercises generated based on the user's personal data and emotional state, and is designed to help users engage in fitness efficiently.

[0574] An "emotion recognition engine" refers to a technology that analyzes and determines a user's emotional state at a given moment based on their facial expressions, voice, and behavior.

[0575] A "generative model" refers to an algorithm that generates appropriate feedback and motivational messages for users based on analyzed data, and generates responses in real time that are tailored to the user's emotional state.

[0576] A "motivational message" is a message provided to encourage continued exercise in accordance with the user's emotional state, and includes encouragement and instructions for fitness activities.

[0577] This invention involves a user inputting personal data using a device such as a smartphone to implement a fitness plan. The device incorporates an emotion recognition engine and a generative model, which analyze the user's emotional state in real time. For this purpose, the device's camera and microphone are used to collect and analyze data on the user's facial expressions and voice.

[0578] The server generates an optimized exercise plan based on collected emotional data. The generated exercise plan is tailored to the user's health condition and lifestyle, and is presented to the user. Furthermore, the server collects user movement data in real time during exercise and adjusts the plan in response to changes in emotional state.

[0579] The generative AI model generates appropriate feedback for the user based on the analysis results. For example, if the exercise is progressing well, a motivational message such as "Great pace! Keep it up!" will be sent. Conversely, if the AI ​​detects that the user's emotional state is negative, it can adjust the plan and provide encouraging messages such as, "Let's relax a bit. How about switching to a lighter workout today?"

[0580] As an example, it is envisioned that a user visiting a gym will continue their training while their emotions are recognized through a device. The exercise plan and motivational messages provided at this time will be dynamically generated according to the emotional state determined from the user's facial expressions and voice.

[0581] The following prompt statements are used as example inputs to the generative AI model.

[0582] "User facial expression data: User data, please generate a feedback message for this user."

[0583] "Generate an exercise plan to be provided when the emotional state is not positive."

[0584] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0585] Step 1:

[0586] Users enter personal data using their devices. This personal data includes their health status and fitness goals. This data is sent to the server as basic information for generating an exercise plan.

[0587] Step 2:

[0588] The device uses a camera and microphone to record the user's facial expressions and voice in real time, and an emotion recognition engine analyzes this data. This analysis reveals the user's emotional state. This emotional data, along with personal data, is sent to a server.

[0589] Step 3:

[0590] The server uses a generative AI model to generate an individually optimized exercise plan based on the received personal and emotional data. This process analyzes the user's needs and current situation from the data to determine the optimal exercise program.

[0591] Step 4:

[0592] The generated exercise plan is presented to the user via the device. The user then begins exercising based on the proposed plan.

[0593] Step 5:

[0594] During exercise, the device continues to collect user activity data in real time. This includes exercise form, accuracy of movements, and changes in emotions. The collected data is sent to a server and treated as progress data for the exercise.

[0595] Step 6:

[0596] The server analyzes collected motion and emotion data and generates feedback using a generative AI model. This feedback includes instructions for improving movement and motivational messages. This allows users to understand their progress and receive specific actions to further improve.

[0597] Step 7:

[0598] The generated feedback is provided to the user in real time via the device. By receiving this feedback, users can improve the quality of their exercise and maintain their motivation.

[0599] Step 8:

[0600] After completing an exercise, the server awards points based on the user's achievement level. These points are customized based on the results of the emotion engine and offered to the user as a special reward plan.

[0601] Step 9:

[0602] Ultimately, users review the results of their fitness sessions and adjust their goals and plans for the next session. This creates a continuous cycle of fitness improvement.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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 in 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] This invention provides a system that generates individually optimized exercise plans based on the user's specific needs and supports continuous fitness activities.

[0621] User input and plan generation

[0622] The first step for the user is to enter personal information through their device. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The device then sends this information to a server. Based on the received information, the server uses a generative AI model to create a personalized exercise plan for the user. This plan adjusts the type, intensity, and frequency of exercise to suit the user's health condition and goals.

[0623] Training implementation and data collection

[0624] Once a training plan is delivered to the device, the user begins exercising according to it. During exercise, the device collects the user's exercise data. This data includes start and end times, type of exercise, distance, heart rate, and calories burned. The user either enters the information after completing each exercise session, or the device automatically records it.

[0625] Providing feedback and monitoring the situation

[0626] The server analyzes data in real time and provides feedback on the user's form and progress. This feedback includes advice on correcting form and what exercises to do next. For example, if the user's heart rate is high, it may notify them to slow down. In addition, the server tracks progress and measures the degree of achievement toward the goal. Points are awarded based on this achievement, and the user can check them.

[0627] Maintaining motivation and reward systems

[0628] To motivate users, a point system is implemented based on their achievements. Users can check their earned points and exchange them for rewards. These rewards are training-related products and perks, encouraging users to continue exercising. For example, a discount coupon for new training gear may be offered as a reward each time a goal is achieved.

[0629] In this way, the system helps users effectively achieve their fitness goals and provides a system for continuous health management.

[0630] The following describes the processing flow.

[0631] Step 1: The user uses their device to enter personal information such as age, gender, weight, height, fitness goals, and exercise experience.

[0632] Step 2: The device sends the entered personal information to the server, which then securely stores the data.

[0633] Step 3: Based on the stored data, the server uses a generating AI model to create a customized exercise plan tailored to the user's fitness level and goals.

[0634] Step 4: The device notifies the user of the generated exercise plan and displays the daily exercise content and schedule.

[0635] Step 5: The user follows the instructions on the device to begin training, and enters the start and end times of the exercise and calories burned into the device as needed.

[0636] Step 6: The device records data such as heart rate and type of exercise in real time during the user's workout and sends it to the server.

[0637] Step 7: The server analyzes the received exercise data and provides feedback to the user, such as suggestions for improving form or recommending the next exercise.

[0638] Step 8: The server evaluates the user's exercise performance, calculates and awards points based on their achievement level.

[0639] Step 9: The device displays calculated feedback, points, and motivational messages to the user, and provides advice for the next workout.

[0640] Step 10: Users continue training based on feedback and maintain motivation by exchanging points for rewards based on the goals they achieve.

[0641] (Example 1)

[0642] 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".

[0643] Traditional fitness systems struggle to generate optimal exercise plans tailored to each user's individual health condition and activity level, provide real-time feedback during exercise, and continuously improve user motivation. Therefore, supporting users in effectively achieving their fitness goals is a challenge.

[0644] 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.

[0645] In this invention, the server includes means for acquiring attribute information from the user, means for creating prompt statements and using a generation AI to generate an exercise plan based on the attribute information, and means for transmitting the generated exercise plan to the terminal via a communication network to present it to the user. This enables the provision of an optimal exercise plan tailored to the user's individual needs and real-time feedback.

[0646] A "user" is an individual who uses the system to achieve their fitness goals.

[0647] "Attribute information" refers to personal data such as the user's age, gender, weight, height, health status, and activity level.

[0648] An "exercise plan" is a guide to specific physical activities, including type, intensity, and frequency, generated based on the user's individual attribute information.

[0649] "Generative AI" is a model that utilizes artificial intelligence to analyze user attribute information and construct an appropriate exercise plan.

[0650] A "prompt message" is a set of instructions or information that a generating AI needs to construct the optimal movement plan.

[0651] A "communication network" is an infrastructure for digital data transfer used to send and receive information between terminals.

[0652] "Feedback" refers to real-time instructions and suggestions provided to users during exercise regarding their progress and how to improve their form.

[0653] "Rewards" refer to incentives such as points or discounts on training equipment that users earn by achieving their exercise goals.

[0654] Overall overview

[0655] This system is designed to support fitness activities by generating exercise plans tailored to the individual needs of each user. The server and terminal communicate via a network, and the system uses a generative AI model to generate prompt messages, providing the user with the optimal fitness plan.

[0656] Hardware and software to be used

[0657] The terminal is envisioned to be an information and communication device such as a wearable device or a smartphone. This allows users to operate it intuitively and input their own attribute information. The server operates in a cloud computing environment, has a generative AI model, and processes user information based on it.

[0658] Data processing and data calculation

[0659] The server constructs prompt messages for the generating AI model based on the received user attribute information. By inputting these prompt messages into the generating AI model, an individually optimized exercise plan is created. The plan is sent to the terminal, which continuously monitors the user's activity, provides feedback as needed, and evaluates the user's progress through data analysis.

[0660] Specific example

[0661] For example, suppose a 30-year-old female user aims to lose 5kg. She uses her device to input her age and past exercise habits. The server prompts the AI ​​model with a message such as, "30 years old, female, 5kg weight loss, wants to exercise 3 times a week." Based on this information, the AI ​​model creates an exercise plan, including running and yoga, and sends the plan to her device. She can then continue exercising according to this plan, receiving feedback while monitoring her progress.

[0662] In this way, by implementing the invention, users can receive a fitness plan tailored to their individual circumstances and obtain specific guidance toward achieving their health goals.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] Users use a device to input their personal information. This information includes age, gender, weight, height, fitness goals, past exercise habits, and daily activity level. The entered information is collected as basic data to generate an optimal exercise plan and is sent from the device to the server.

[0666] Step 2:

[0667] The server receives user attribute information sent from the terminal. Based on the received information, it generates a prompt message. This prompt message is filtered into a format suitable for input into the generating AI model, and an example would be "30 years old, female, weight loss of 5 kg, desires to exercise 3 times a week." Data analysis and filtering techniques are used to generate the prompt message.

[0668] Step 3:

[0669] The server inputs prompt messages into the generating AI model. Based on the input prompt messages, the generating AI model generates an exercise plan optimized for the user. The generated exercise plan is a detailed activity plan including the type of exercise, intensity, and frequency, and is stored internally on the server in JSON format.

[0670] Step 4:

[0671] The server sends the generated exercise plan to the terminal via the communication network. The terminal visualizes the received exercise plan and displays it on the screen in a way that is easy for the user to understand. For example, icons and explanations are assigned to each type of exercise, and the weekly schedule is presented in a calendar format.

[0672] Step 5:

[0673] The user begins their daily training according to the provided exercise plan. The device records the user's exercise data in real time, continuously collecting metrics such as heart rate, calories burned, and exercise time. This data is managed as information necessary to achieve exercise goals.

[0674] Step 6:

[0675] Exercise data collected by the device is periodically sent to a server. The server analyzes this data and generates feedback about the user's exercise form and progress. For example, if the user's heart rate exceeds the target range, a real-time notification such as "Continue exercising while regulating your breathing" is sent.

[0676] Step 7:

[0677] The server evaluates the user's achievement level based on the analysis results and calculates points. These points are converted into rewards to strengthen the user's motivation. Based on the achievement level and points, rewards may be offered, for example, as discount coupons for training goods usable in an online shop. Users can view and use these rewards through their device.

[0678] (Application Example 1)

[0679] 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".

[0680] Users face challenges in accessing real-time exercise plans tailored to their individual health and fitness levels, and in receiving appropriate feedback. Furthermore, there is a lack of tools to effectively support form improvement and motivation maintenance during exercise.

[0681] 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.

[0682] In this invention, the server includes means for receiving personal data from a user, means for generating an exercise plan based on the personal data, and means for displaying information in real time during exercise using a visual display device. This makes it possible to provide an exercise plan tailored to each user's health condition and fitness goals in real time, and to obtain appropriate feedback during exercise.

[0683] "Personal data" refers to information related to an individual, including the user's age, gender, weight, height, fitness goals, past exercise habits, and daily activity level.

[0684] An "exercise plan" is a plan of activities, including the type, intensity, and frequency of exercise, designed based on the user's individual health condition and fitness goals.

[0685] "Real-time monitoring" refers to a method of instantly observing and recording a user's exercise status and immediately analyzing the data.

[0686] "Feedback" refers to responses and advice provided based on the user's exercise performance, and may include improvements to exercise form.

[0687] "Evaluation" refers to indicators such as points or scores that are awarded based on the user's exercise achievement level.

[0688] "Rewards" refer to incentives provided in accordance with the performance achieved, and may include training-related goods or benefits.

[0689] A "visual display device" is a device that visually presents various information to the user during exercise, and includes smart glasses and similar display devices.

[0690] This system provides exercise plans based on the user's individual needs and supports continuous fitness activities. At the core of the system is a mechanism that uses a generative AI model to create a personalized exercise plan for each user and provides appropriate feedback in real time through a visual display.

[0691] The server processes personal data received from users via smart devices and generates an exercise plan using a generative AI model. This exercise plan is optimized to match the user's health status and goals.

[0692] The device displays an exercise plan to the user and utilizes sensors to monitor exercise status in real time. By using a visual display device (e.g., smart glasses), users can check information such as heart rate and pace in real time during exercise. Furthermore, feedback is displayed as needed if exercise form is inappropriate.

[0693] Each time a user completes a set amount of exercise, the server analyzes the data from the sensors and awards points based on their level of achievement. To motivate users, these evaluation points can be exchanged for training-related rewards.

[0694] For example, when a user is running, if the smart glasses detect that their heart rate has exceeded the target range, they may display an alert to slow down. Additionally, points are immediately added when the user achieves their set goals. This allows users to constantly monitor their progress and use that information to set their next goals.

[0695] An example of a prompt message would be: "20 years old, male, weight 70kg, desires cycling, please set training to 4 times a week. Please generate a fitness plan based on this information." The system will then generate the user's exercise plan based on this information.

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The user uses a device to enter personal data, including age, gender, weight, and fitness goals. The device sends this data to a server. The server processes the received data and converts it into the required format.

[0699] Step 2:

[0700] The server analyzes the received personal data and inputs it into a generating AI model. This model generates an exercise plan that takes the user's characteristics into account. The output is a plan that specifies the type, intensity, and frequency of exercise that has been individually optimized.

[0701] Step 3:

[0702] The server sends the generated motion plan to the terminal. The terminal presents this plan to the user. The user can view the details of the plan in real time via a visual display device.

[0703] Step 4:

[0704] When a user starts exercising, the device records their exercise status in real time. Sensors collect data such as heart rate, distance, and calories burned. The device then sends this data to a server.

[0705] Step 5:

[0706] The server analyzes the collected exercise data and uses a generative AI model to generate feedback for the user. The output includes advice on how to improve the user's exercise form and appropriate pacing.

[0707] Step 6:

[0708] The server sends feedback to the terminal, which is then displayed to the user via a visual display. The user can then review the feedback in real time and adjust their movements accordingly.

[0709] Step 7:

[0710] Once the user's workout is complete, the server analyzes the workout data and generates evaluation points based on their performance. These evaluation points are linked to the user's achievement of their workout goals.

[0711] Step 8:

[0712] The server calculates rewards based on evaluation points and notifies the user. Users use this to maintain motivation and challenge themselves to achieve their next fitness goals.

[0713] 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.

[0714] This invention is a fitness management system that incorporates a function to recognize the user's emotions, and aims to provide an individually optimized exercise plan, feedback on its execution, and support for improving motivation based on emotion recognition.

[0715] User settings and sentiment data acquisition

[0716] As part of the initial setup, users enter personal information into the device. This personal information includes their fitness level and goals. The device uses its camera and microphone to record the user's facial expressions, voice, and movements, and an emotion engine analyzes this data. Based on this analysis, the server determines the user's emotional state and stores it in a database.

[0717] Generating and adjusting exercise plans

[0718] The server uses a generative AI to generate a personalized exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted according to the user's emotional state to maintain their motivation. For example, if the user is determined to be in a negative emotional state, easier exercises and encouraging messages are provided.

[0719] Real-time exercise monitoring and feedback

[0720] When a user starts exercising, the device collects exercise data in real time and sends it to a server. The server analyzes the received data and provides feedback. This feedback includes advice on exercise form and motivational messages tailored to the user's emotional state. For example, it may send encouraging or praising messages to support the user's successful experience.

[0721] Customization of point systems and rewards

[0722] The server calculates and awards points based on the user's exercise performance and achievements. These points are customized based on the results of the emotion engine and provided as rewards. For example, if the user's emotions are positive, they may be awarded a special bonus for achieving challenging goals.

[0723] In this way, a system incorporating emotion recognition enables flexible fitness management and motivation enhancement tailored to each user's needs, making continuous and effective training possible.

[0724] The following describes the processing flow.

[0725] Step 1: The user enters personal information into the device and configures the settings to enable the emotion engine.

[0726] Step 2: The device collects user facial expressions, voice, and motion data in real time through the camera and microphone, and the emotion engine analyzes the data.

[0727] Step 3: The server receives the analyzed emotion data, determines the user's emotional state, and saves it to the database.

[0728] Step 4: The server uses AI to generate an optimal exercise plan based on the user's personal information and emotional state. The exercise plan is adjusted to be gentle if the user is experiencing negative emotions and challenging if they are experiencing positive emotions.

[0729] Step 5: The device presents the generated exercise plan to the user, and the user begins exercising.

[0730] Step 6: The device records data during exercise (e.g., heart rate, number of repetitions) and continuously monitors emotional changes through the emotion engine.

[0731] Step 7: The server analyzes the collected exercise and emotional data and provides form advice and emotionally responsive feedback.

[0732] Step 8: The user receives real-time feedback through the device and adjusts their exercise based on it.

[0733] Step 9: The server calculates points based on the level of exercise achievement and emotional state, and sends them to the terminal.

[0734] Step 10: The device displays points to the user and notifies them of a reward customized based on their emotions. The user is motivated by the reward and continues training.

[0735] (Example 2)

[0736] 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".

[0737] Traditional fitness management systems have difficulty responding to individual user emotional states, making it challenging to optimize exercise plans and maintain sustained motivation. Because exercise difficulty and feedback are not tailored to the user's emotions, user satisfaction may decrease.

[0738] 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.

[0739] In this invention, the server includes means for receiving characteristics from the user, means for analyzing the user's emotional state and adjusting the exercise plan, and means for assigning evaluations based on the degree of achievement and emotional state. This makes it possible to adjust the exercise plan in response to the user's emotions and maintain the user's motivation through an individually optimized feedback and reward system.

[0740] "Features" refer to information such as the user's personal information, fitness level, and emotional state, and serve as the basic data for the system to generate exercise plans and feedback.

[0741] An "exercise plan" is a training program created based on the user's characteristics and is adjusted according to individual needs and emotional state.

[0742] "Emotional state" refers to the user's current psychological state and is a parameter used for system feedback and adjustment of the exercise plan.

[0743] "Real time" is a term that refers to the time interval used to instantly record and analyze a user's exercise status.

[0744] "Responses" refer to feedback provided based on the user's exercise status and emotional state, with the aim of improving exercise form and increasing motivation.

[0745] "Evaluation" refers to points and rewards calculated based on a user's exercise performance and emotional state, and is used to increase motivation.

[0746] This invention is a fitness management system that recognizes the user's emotions and optimizes the fitness exercise plan according to that state. During initial setup, the user enters personal information into the terminal and provides the system with their goals and fitness level. The terminal uses its built-in camera and microphone to record the user's facial expressions, voice, and movements, and sends this data to an emotion engine to analyze the user's emotional state. The emotion engine uses image processing and voice analysis technology to determine the user's emotions. The results of the emotion engine's determination are sent to a server and recorded in a database.

[0747] The server uses a generative AI model to generate a personalized training program based on the received emotional state data and the user's personal information. The generated exercise plan is adjusted in terms of schedule and content according to the user's emotions. The generative AI model used in this process is input with pre-generated prompts. An example prompt is, "The user is currently experiencing negative emotions; please suggest an easy exercise set."

[0748] When a user begins exercising, the device monitors their exercise status in real time and continuously sends information to the server. The server analyzes this information and returns feedback to the device, such as advice on exercise form and encouraging messages. Based on the user's exercise performance and analyzed emotional state, the server calculates points and adjusts rewards. This reward system increases user motivation and encourages continued fitness activities.

[0749] In this way, a system is realized that provides a personalized fitness experience based on the user's characteristics and emotions, enabling it to maximize their performance.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] Users enter personal information into the fitness management system terminal. This information includes their fitness level and goals. Once this information is entered, the terminal creates a profile and prepares to send it to the server.

[0753] Step 2:

[0754] The device uses its built-in camera and microphone to record the user's facial expressions, voice, and movements. During this process, the camera recognizes the user's face, and the microphone analyzes their voice tone. This data is sent to an emotion engine, where it is analyzed as input data. The emotion engine processes this data to determine the user's emotional state. It then generates emotional state information as output and sends it to the server.

[0755] Step 3:

[0756] The server inputs the user's personal information and emotional state into a generating AI model to create a personalized exercise plan. Based on this input data, the generating AI model calculates the optimal schedule and exercise content for the user and outputs the plan. As a specific example, the prompt "The user is currently experiencing negative emotions; please suggest an easy exercise set" is input to the generating AI model. The server then sends the generated exercise plan to the terminal.

[0757] Step 4:

[0758] When a user begins exercising, the device uses attached sensors to collect exercise data in real time. The sensors record the user's heart rate, exercise speed, form, and other information. This information is sent to a server as input data and used to analyze the user's condition during exercise. The server performs the analysis and prepares to provide appropriate feedback and responses to the user.

[0759] Step 5:

[0760] The server generates feedback based on the analysis of the exercise and the user's emotional state. This feedback includes advice on exercise form and encouraging messages to boost motivation. The generated feedback is sent to the device and provided to the user in real time.

[0761] Step 6:

[0762] Once a user's exercise record is collected, the server calculates points based on their level of achievement and emotional state. These points are treated as an evaluation and sent to the reward system. The reward system adjusts the user's rewards based on this evaluation and provides this information to the user's device. Users can then view their earned points and rewards.

[0763] (Application Example 2)

[0764] 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".

[0765] While conventional fitness management systems have optimized exercise plans based on users' health status and lifestyles, they have lacked adjustments that take into account users' emotional states and provide insufficient motivational support. As a result, users sometimes lost interest in exercise, making it difficult to continue training. Furthermore, in brick-and-mortar fitness gyms, real-time emotional analysis and feedback to individual users have not been adequately implemented. This invention aims to solve these problems and provide a system that offers appropriate feedback and motivation enhancement tailored to each user's emotional state.

[0766] 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.

[0767] In this invention, the server includes means for generating an action plan based on the user's personal data, means including an emotion recognition engine for analyzing the user's emotional state, and means using a generation model that adjusts the exercise plan and generates motivational messages based on emotion recognition. This enables appropriate fitness management and effective motivation enhancement tailored to the user's individual emotional state, as well as real-time feedback and improved user experience in physical stores.

[0768] "Personal data" refers to information about a user's health status, lifestyle, and individual preferences, which is used to customize their fitness plan.

[0769] A "movement plan" is a plan that includes the schedule and content of exercises generated based on the user's personal data and emotional state, and is designed to help users engage in fitness efficiently.

[0770] An "emotion recognition engine" refers to a technology that analyzes and determines a user's emotional state at a given moment based on their facial expressions, voice, and behavior.

[0771] A "generative model" refers to an algorithm that generates appropriate feedback and motivational messages for users based on analyzed data, and generates responses in real time that are tailored to the user's emotional state.

[0772] A "motivational message" is a message provided to encourage continued exercise in accordance with the user's emotional state, and includes encouragement and instructions for fitness activities.

[0773] This invention involves a user inputting personal data using a device such as a smartphone to implement a fitness plan. The device incorporates an emotion recognition engine and a generative model, which analyze the user's emotional state in real time. For this purpose, the device's camera and microphone are used to collect and analyze data on the user's facial expressions and voice.

[0774] The server generates an optimized exercise plan based on collected emotional data. The generated exercise plan is tailored to the user's health condition and lifestyle, and is presented to the user. Furthermore, the server collects user movement data in real time during exercise and adjusts the plan in response to changes in emotional state.

[0775] The generative AI model generates appropriate feedback for the user based on the analysis results. For example, if the exercise is progressing well, a motivational message such as "Great pace! Keep it up!" will be sent. Conversely, if the AI ​​detects that the user's emotional state is negative, it can adjust the plan and provide encouraging messages such as, "Let's relax a bit. How about switching to a lighter workout today?"

[0776] As an example, it is envisioned that a user visiting a gym will continue their training while their emotions are recognized through a device. The exercise plan and motivational messages provided at this time will be dynamically generated according to the emotional state determined from the user's facial expressions and voice.

[0777] The following prompt statements are used as example inputs to the generative AI model.

[0778] "User facial expression data: User data, please generate a feedback message for this user."

[0779] "Generate an exercise plan to be provided when the emotional state is not positive."

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] Users enter personal data using their devices. This personal data includes their health status and fitness goals. This data is sent to the server as basic information for generating an exercise plan.

[0783] Step 2:

[0784] The device uses a camera and microphone to record the user's facial expressions and voice in real time, and an emotion recognition engine analyzes this data. This analysis reveals the user's emotional state. This emotional data, along with personal data, is sent to a server.

[0785] Step 3:

[0786] The server uses a generative AI model to generate an individually optimized exercise plan based on the received personal and emotional data. This process analyzes the user's needs and current situation from the data to determine the optimal exercise program.

[0787] Step 4:

[0788] The generated exercise plan is presented to the user via the device. The user then begins exercising based on the proposed plan.

[0789] Step 5:

[0790] During exercise, the device continues to collect user activity data in real time. This includes exercise form, accuracy of movements, and changes in emotions. The collected data is sent to a server and treated as progress data for the exercise.

[0791] Step 6:

[0792] The server analyzes collected motion and emotion data and generates feedback using a generative AI model. This feedback includes instructions for improving movement and motivational messages. This allows users to understand their progress and receive specific actions to further improve.

[0793] Step 7:

[0794] The generated feedback is provided to the user in real time via the device. By receiving this feedback, users can improve the quality of their exercise and maintain their motivation.

[0795] Step 8:

[0796] After completing an exercise, the server awards points based on the user's achievement level. These points are customized based on the results of the emotion engine and offered to the user as a special reward plan.

[0797] Step 9:

[0798] Ultimately, users review the results of their fitness sessions and adjust their goals and plans for the next session. This creates a continuous cycle of fitness improvement.

[0799] 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.

[0800] 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.

[0801] In the above embodiment, an example was given in which the 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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."

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] Means of receiving personal information from users,

[0823] Means for generating an exercise plan based on the aforementioned personal information,

[0824] A means for presenting the aforementioned exercise plan to the user,

[0825] A means of monitoring the user's exercise status in real time,

[0826] A means for providing feedback to the user based on the aforementioned exercise conditions,

[0827] A means of awarding points based on the degree of achievement,

[0828] A means for calculating a reward based on the aforementioned points,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the exercise plan is customized based on the user's fitness level and lifestyle.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the feedback instructs the user to improve their exercise form.

[0834] "Example 1"

[0835] (Claim 1)

[0836] A means of obtaining attribute information from users,

[0837] A means of creating a prompt statement and using a generation AI to generate a motor plan based on the aforementioned attribute information,

[0838] A means for transmitting the generated motor plan to a terminal via a communication network in order to present it to the user,

[0839] A means of monitoring the user's physical activity status in real time and collecting that data,

[0840] A means of providing feedback to the user based on the collected data and encouraging improvements in exercise pace and form,

[0841] A means of providing numerically defined rewards based on the degree of achievement,

[0842] A means of providing motivation to the user based on the aforementioned reward,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the exercise plan is individually optimized based on the user's health condition and activity pattern.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the feedback includes specific instructions for optimizing the user's athletic performance.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] Means of receiving personal data from users,

[0851] Means for generating an exercise plan based on the aforementioned personal data,

[0852] Means for displaying the exercise plan to the user,

[0853] A means of monitoring the user's exercise environment in real time,

[0854] Means for providing a response to the user based on the aforementioned motion environment,

[0855] A means of assigning evaluations based on the degree of achievement,

[0856] A means for calculating compensation based on the aforementioned evaluation,

[0857] A means of displaying information in real time during movement using a visual display device,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the exercise plan is individually optimized based on the user's health level and lifestyle.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the response instructs the user to improve their motor movements and is displayed via a visual display device.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] A means of receiving features from the user,

[0866] Means for generating a motion plan based on the aforementioned features,

[0867] A means for analyzing the user's emotional state and adjusting the exercise plan,

[0868] A means for presenting the aforementioned exercise plan to the user,

[0869] A means of monitoring the user's exercise status in real time,

[0870] Means for providing a response to the user based on the aforementioned physical condition and emotional state,

[0871] A means of assigning evaluations based on achievement level and emotional state,

[0872] A means for calculating compensation based on the aforementioned evaluation,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, wherein the exercise plan is individualized based on the user's training level and lifestyle.

[0876] (Claim 3)

[0877] The system according to claim 1, wherein the response instructs the user to improve their exercise posture.

[0878] "Application example 2 when combining with an emotional engine"

[0879] (Claim 1)

[0880] Means of receiving personal data from users,

[0881] Means for generating an action plan based on the aforementioned personal data,

[0882] Means for presenting the aforementioned operation plan to the user,

[0883] A means of monitoring the user's activity in real time,

[0884] A means for providing feedback to the user based on the aforementioned operating status,

[0885] A means of awarding points based on the degree of achievement,

[0886] A means for calculating a reward based on the aforementioned points,

[0887] A means including an emotion recognition engine that analyzes the user's emotional state,

[0888] Means for using a generative model that adjusts the exercise plan and generates motivational messages based on the aforementioned emotion recognition,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, wherein the aforementioned action plan is adjusted based on the user's health condition and lifestyle and is dynamically modified in order to enhance motivation in accordance with the results of emotion recognition analysis.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the feedback instructs the user to improve their behavioral form and provides a message to support their motivation based on the results of sentiment analysis. [Explanation of Symbols]

[0894] 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. Means of receiving personal information from users, Means for generating an exercise plan based on the aforementioned personal information, A means for presenting the aforementioned exercise plan to the user, A means of monitoring the user's exercise status in real time, A means for providing feedback to the user based on the aforementioned exercise conditions, A means of awarding points based on the degree of achievement, A means for calculating a reward based on the aforementioned points, A system that includes this.

2. The system according to claim 1, wherein the exercise plan is customized based on the user's fitness level and lifestyle.

3. The system according to claim 1, wherein the feedback instructs the user to improve their exercise form.

Images