system

Abstract

Provide a system. [Solution] means for generating a personalized exercise and nutrition plan based on the user's fitness level, goals, and preferences; means for providing the generated exercise and nutrition plans to the user; a means for collecting data using sensors or cameras to monitor the user's movements; means for analyzing the collected data and providing feedback to the user to adjust their exercise and nutrition plans as needed; and a means for periodically assessing the user's progress and updating the plan to achieve long-term goals.

Description

[Technical Field]

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

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

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

[0004] Traditional fitness programs and nutrition plans are often delivered in a one-size-fits-all format, making it difficult to customize them to fit individual users' characteristics and goals. Effective training is also difficult due to a lack of real-time fitness monitoring and ongoing feedback. Furthermore, there is a lack of a system to accurately assess a user's progress and adjust the plan accordingly, making it difficult for users to maintain motivation and achieve their goals. [Means for solving the problem]

[0005] The present invention provides a means for generating individually designed exercise and nutrition plans based on a user's fitness level, goals, and preferences. It also includes a means for providing the generated exercise and nutrition plans to the user, thereby providing a customized plan tailored to the user's unique needs. It also includes a means for collecting data using sensors or cameras to monitor the user's exercise, thereby enabling real-time fitness monitoring. It also includes a means for analyzing the collected data, providing feedback to the user, and adjusting the exercise and nutrition plans as needed to support effective training. It also includes a means for regularly evaluating the user's progress and updating the plans to support long-term goal achievement, thereby promoting ongoing motivation and goal achievement.

[0006] A "user" is a person who utilizes the system to receive fitness and nutrition plans.

[0007] "Fitness level" is an index that indicates the user's exercise experience and physical condition, and is classified into categories such as beginner, intermediate, and advanced.

[0008] A "goal" is a specific outcome that a user wants to achieve through fitness activities, such as losing weight, building muscle, or staying in shape.

[0009] "Preferences" refer to the type of exercise and eating style that a user prefers and are used to customize exercise and nutrition plans.

[0010] An "exercise plan" is specific exercise instructions or schedule created based on a user's fitness level, goals, and preferences.

[0011] A "nutrition plan" is a set of meal guidelines and recipes designed to support a user's fitness goals.

[0012] A "sensor" is a device that detects a user's physical movements or physiological state and collects data, and includes, for example, a heart rate monitor or an acceleration sensor.

[0013] A "camera" is a device that captures a user's exercise form and movements and provides images for analysis in real time.

[0014] "Collecting data" means obtaining information about the user's movements and physiological state using sensors and cameras.

[0015] "Analysis" refers to the process of using collected data to assess a user's fitness and exercise form to provide feedback and adjust their plan.

[0016] "Feedback" refers to advice and instructions regarding exercise and nutrition provided to users based on the analysis results.

[0017] "Adjusting" means changing or updating your exercise and nutrition plans in light of the data and feedback collected.

[0018] "Progress" is a measure of how far a user has come towards their fitness goals.

[0019] "Updating" means redesigning the contents of the exercise plan and nutrition plan based on the user's progress and new information. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention is a system that provides individually designed exercise and nutrition plans based on a user's fitness level, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. This section describes the specific program processing of the system in natural language.

[0042] Overall system overview

[0043] 1. Initial Setup

[0044] User: Opens the application and begins the initial setup. The user answers questions about their fitness level, goals, and preferences and enters the required data.

[0045] Terminal: Collects this information and sends it to the server.

[0046] Server: Creates a user profile based on the received data and stores it in a database.

[0047] 2. Generate exercise and nutrition plans

[0048] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans.

[0049] Server: Sends the generated plan to the user's device.

[0050] Device: Displaying exercise and nutrition plans to the user.

[0051] Specific examples

[0052] 1. Example of initial settings

[0053] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[0054] Terminal: Collects questions and answers as data, formats the user's responses, and sends the data to the server.

[0055] Server: Based on the received information, create a new user entry in the database and register the fitness level "Beginner," goal "Weight loss," and preference "Aerobic exercise."

[0056] 2. Examples of creating exercise and nutrition plans

[0057] Server: Runs AI algorithms based on the user's profile information to generate a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[0058] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[0059] On your device: Display plan details on a dashboard and allow users to track their daily progress.

[0060] Fitness Monitoring and Feedback

[0061] 1. User: Starts exercising according to a specified exercise plan. For example, starts walking.

[0062] Device: Collects user exercise data (e.g., steps, heart rate) in real time using built-in cameras and sensors (e.g., heart rate monitors on smartwatches).

[0063] Terminal: Sends collected data to the server.

[0064] 2. Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[0065] Server: Generates feedback as needed, for example, creating a message like "You're walking too slowly, please walk faster."

[0066] Device: Notify the user of the feedback.

[0067] 3. Progress evaluation and plan adjustment

[0068] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[0069] Device: Sends self-assessment data to the server.

[0070] Server: Integrates subjective assessment and exercise data to comprehensively evaluate the user's progress. Updates the next week's exercise and nutrition plans as needed.

[0071] Server: Sends the updated plan to the user's device.

[0072] Device: Display next week's plan on the user's dashboard.

[0073] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] Initial Setup

[0077] User: Opens the application and taps the start button for initial setup.

[0078] Device: Prompts the user with questions about their fitness level, goals, and preferences, and asks them to enter their answers. For example, they might enter "Fitness level is beginner," "Goal is weight loss," and "Preferences are aerobic exercise."

[0079] Terminal: Formats the entered data and sends it to the server.

[0080] Server: Creates a user profile based on the received data and stores it in a database.

[0081] Step 2:

[0082] Generate exercise and nutrition plans

[0083] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[0084] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[0085] On the device: Display an exercise and nutrition plan to the user, for example, a balanced diet plan of 2000 kcal and 30 minutes of walking three times a week.

[0086] Step 3:

[0087] Fitness Monitoring

[0088] User: Starts exercising according to the specified exercise plan. For example, starts walking.

[0089] Device: Uses built-in cameras and sensors to collect real-time exercise data from users, such as capturing their form with a camera and measuring their heart rate with a heart rate monitor.

[0090] Terminal: Sends collected data to the server.

[0091] Step 4:

[0092] Feedback and Adjustments

[0093] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[0094] Server: Generates feedback on areas that need adjustment or improvement. For example, create a message like "Your pace is too slow, please walk a little faster."

[0095] Server: Sends feedback to the user's device.

[0096] Device: Notify the user of the feedback.

[0097] Step 5:

[0098] Progress evaluation and plan adjustment

[0099] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[0100] Terminal: Sends the entered self-evaluation data to the server.

[0101] Server: Integrates subjective evaluations and exercise data to comprehensively evaluate the user's progress, such as improving form or increasing exercise intensity.

[0102] Server: Adjust / update next week's exercise and nutrition plan as needed, for example, increase walking time to 40 minutes.

[0103] Server: Sends the updated plan to the user's device.

[0104] Device: Display next week's plan on the user's dashboard.

[0105] Through these steps, the system can effectively support users' fitness activities, encouraging them to maintain continuous motivation and achieve their goals.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] To meet today's individual fitness needs, many users are seeking individually designed exercise and nutrition plans. However, existing systems lack sufficient customization based on a user's physiological information, goals, and preferences, and lack continuous feedback and plan adjustments to achieve long-term goals. This makes it difficult for users to effectively achieve their goals, and providing a sustainable fitness plan is a challenge.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes means for generating a personalized exercise plan and nutrition plan based on the user's physiological information, goals, and preferences, means for providing the generated exercise plan and nutrition plan to the user, means for collecting data using a sensor or imaging device to monitor the user's exercise, means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and nutrition plan as needed, means for adjusting the exercise plan and nutrition plan using a generative AI model, and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals, thereby enabling customization of the exercise and nutrition plan to meet the user's individual needs, continuous monitoring and feedback, and adjustment of the plan according to progress.

[0111] "Physiological information" is data related to the user's health condition and physical characteristics, and examples include height, weight, heart rate, blood pressure, etc.

[0112] A "goal" is a goal that a user wishes to achieve through a fitness activity, examples of which include weight loss, muscle gain, and endurance improvement.

[0113] "Preferences" are the user's preferred types of fitness and eating style, examples of which include cardio, strength training, plant-based eating, etc.

[0114] An "exercise plan" is a specific exercise schedule or menu provided to the user, and examples include 30 minutes of walking three times a week and strength training twice a week.

[0115] A "nutritional plan" is a specific dietary instruction or meal plan provided to a user, and examples include a calorie-restricted diet, a protein-enriched diet, etc.

[0116] A "sensor" is a device that collects a user's exercise data in real time, and examples include smartwatches and fitness trackers.

[0117] "Photography device" refers to a camera or video device used to monitor the user's posture and exercise form.

[0118] "Means for analyzing data" refers to algorithms or software used to analyze collected data and assess the user's progress or condition.

[0119] "Feedback" refers to suggestions for improvement or advice provided to users based on the analysis results, such as "Please speed up your pace" or "Please add this exercise next time."

[0120] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate and adjust a user's plan.

[0121] A "human interface" is an interface through which a user interacts with a system, and examples include smartphone applications and web browsers.

[0122] A "database" is an information storage system for managing user profiles and history data stored on a server.

[0123] "Long-term progress" refers to the results or changes in a user's fitness activities over a period of time.

[0124] The above definitions allow a clear understanding of each element in the present invention.

[0125] The present invention provides a system that provides individually designed exercise and nutrition plans based on a user's physiological information, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. The operation of the system is described in detail below.

[0126] Initial Setup

[0127] 1. User: Launches the application on their smartphone or computer and begins the first-time setup. The user answers questions about their fitness level, goals, and preferences and enters them into the application.

[0128] 2. Terminal: Collects data entered by the user and sends it to the server.

[0129] 3. Server: Creates a user profile based on the received data and stores it in a database, registering the user's fitness level, goals, preferences, etc.

[0130] Generate exercise and nutrition plans

[0131] 1. Server: Runs AI algorithms based on the stored user profile information to generate customized exercise and nutrition plans. The AI ​​models used can be deep learning or machine learning algorithms.

[0132] 2. Server: Sends the generated plan to the user's device.

[0133] 3. Terminal: The received exercise plan and nutrition plan are displayed on the user interface and provided to the user.

[0134] Fitness Monitoring and Feedback

[0135] 1. User: Starts exercising according to a specified exercise plan, for example, walking.

[0136] 2. Device: Utilizing built-in sensors in smartwatches and smartphones, users' exercise data (e.g., steps, heart rate) is collected in real time. Examples of sensors include heart rate monitors and pedometers.

[0137] 3. Terminal: Sends the collected exercise data to the server.

[0138] 4. Server: Analyzes the received data and evaluates the user's progress and exercise form using statistical methods and machine learning techniques.

[0139] 5. Server: Generates feedback as needed, for example, "Your heart rate is dropping. Please pick up the pace a bit."

[0140] 6. Terminal: Providing feedback to the user interface.

[0141] Progress evaluation and plan adjustment

[0142] 1. User: Enter your thoughts and self-evaluation of your exercise regularly. For example, you could do a self-evaluation every weekend.

[0143] 2. Terminal: Sends self-assessment data to the server.

[0144] 3. Server: Integrates subjective and objective exercise data to comprehensively evaluate the user's progress and update the next week's exercise and nutrition plans as needed.

[0145] 4. Server: Sends the updated plan to the user's device.

[0146] 5. Terminal: Display next week's plan on the user interface.

[0147] In a concrete example of this system, if a user selects "beginner level," "weight loss," and "aerobic exercise," the server generates an exercise plan that includes 30 minutes of walking three times a week and a nutrition plan that includes a calorie-restricted diet.The system also uses a smartwatch to monitor heart rate and step count, providing real-time feedback to adjust the pace as needed.

[0148] Prompt Sentence Examples

[0149] "What are your fitness goals? (e.g., weight loss, strength gain, endurance improvement)"

[0150] "What type of exercise do you prefer? (e.g., cardio, strength training, yoga)"

[0151] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] Step 1:

[0154] User: Launches the application and begins the initial setup. The user answers questions and inputs their fitness level, goals, and preferences. Examples of inputs include "Beginner," "Weight loss," and "Prefers cardio."

[0155] Input: Fitness level, goals, and preferences

[0156] Output: Initial setting data

[0157] Step 2:

[0158] Terminal: Collects data entered by the user and formats it, for example, into JSON format data, and then sends it to the server.

[0159] Input: Initialization data

[0160] Output: Formatted data

[0161] Step 3:

[0162] Server: Receives the formatted data and stores it in a database. Creates a new user profile with fitness level "Beginner", goal "Weight loss", and preference "Aerobic exercise".

[0163] Input: Formatted data

[0164] Output: User profile stored in the database

[0165] Step 4:

[0166] Server: Runs AI algorithms based on the user profile. Specifically, it uses deep learning algorithms to generate exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to the user's needs.

[0167] Input: Database user profile

[0168] Output: Customized exercise and nutrition plans

[0169] Step 5:

[0170] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[0171] Enter: a customized exercise and nutrition plan.

[0172] Output: The plan sent to the user's device

[0173] Step 6:

[0174] Device: Displays the received exercise and nutrition plans on a user interface. A screen appears providing the user with specific exercise and dietary instructions.

[0175] Input: The plan sent to the user's device

[0176] Output: Exercise and nutrition plans displayed in a user interface

[0177] Step 7:

[0178] User: Follows the specified exercise plan and starts exercising. For example, starts walking.

[0179] Input: Exercise plan displayed in the user interface

[0180] Output: The exercise performed

[0181] Step 8:

[0182] Device: Using built-in sensors in smartwatches and smartphones (e.g., heart rate monitors, pedometers), we collect data on your exercise in real time.

[0183] Input: Exercise to be performed

[0184] Output: Collected exercise data

[0185] Step 9:

[0186] Device: Sends collected exercise data to the server.

[0187] Input: Collected exercise data

[0188] Output: Exercise data sent to the server

[0189] Step 10:

[0190] Server: Analyzes the received exercise data. It uses statistical methods and machine learning algorithms to evaluate the user's progress and exercise form. For example, it checks whether the heart rate is within the appropriate range.

[0191] Input: Exercise data sent to the server

[0192] Output: Analysis results

[0193] Step 11:

[0194] Server: Generates feedback based on the analysis results, such as a message like "Your heart rate is dropping. Please pick up the pace a bit."

[0195] Input: Analysis results

[0196] Output: Feedback message

[0197] Step 12:

[0198] Server: Sends feedback messages to the user's device and notifies them through the user interface.

[0199] Input: Feedback message

[0200] Output: Feedback message sent to the user's device

[0201] Step 13:

[0202] User: Periodically (e.g., at the end of the week) enter a self-evaluation, which includes thoughts on exercise and achievement.

[0203] Input: Feedback message

[0204] Output: Self-assessment

[0205] Step 14:

[0206] Terminal: Collects self-assessment data and sends it to the server.

[0207] Input: Self-assessment data

[0208] Output: Self-assessment data sent to the server

[0209] Step 15:

[0210] Server: Combines subjective assessments and objective exercise data to provide a comprehensive assessment of the user's progress, and determines whether the user's exercise and nutrition plans for the next week need to be updated.

[0211] Input: Subjective assessment data and exercise data

[0212] Output: Exercise and nutrition plans that need updating

[0213] Step 16:

[0214] Server: Sends the updated plan to the user's device.

[0215] Input: Exercise and nutrition plans that need updating

[0216] Output: Renewal plan sent to user's device

[0217] Step 17:

[0218] Terminal: Display the updated plan in the user interface and provide the user with instructions for the next week.

[0219] Input: The renewal plan sent to the user's device

[0220] Output: Update plan displayed in the user interface

[0221] (Application example 1)

[0222] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0223] While existing fitness systems offer personalized exercise and nutritional plans, they often lack the ability to analyze users' movements in real time and provide interactive feedback. Furthermore, they lack the means to effectively enhance the user experience through specific wearable displays, making it difficult to achieve long-term fitness goals.

[0224] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0225] In this invention, the server includes means for analyzing the user's movements in real time and providing interactive feedback using the wearable display, means for formatting the collected data and transmitting it to the server, and means for periodically evaluating the user's progress and updating the plan for long-term goal achievement, thereby enabling continuous adjustment of the plan for long-term goal achievement while monitoring the user's movements in real time and providing appropriate feedback.

[0226] "User's fitness level" is an index that indicates the user's current physical ability and health condition.

[0227] "Goal" refers to the outcome or goal that a user wishes to achieve through their fitness activity.

[0228] "Preferences" refers to the types of exercise and eating habits that a user prefers.

[0229] An "exercise plan" is a specific exercise schedule designed based on a user's fitness level, goals, and preferences.

[0230] A "nutrition plan" is a meal plan designed to suit a user's fitness goals and health status.

[0231] "Sensor or camera" refers to a device used to monitor a user's activity and collect data.

[0232] "Feedback" is advice or guidance provided to users based on collected data.

[0233] A "wearable display device" is a display device that is worn by the user, such as a head-mounted display.

[0234] "Interactive feedback" is feedback that is provided immediately in response to a user's real-time actions.

[0235] "Means for formatting collected data and sending it to the server" refers to the process for converting data collected from users into an appropriate format and sending it to the server.

[0236] "Means to regularly evaluate your progress and update your plan to meet your long-term goals" refers to the process of regularly evaluating your fitness progress and adjusting and updating your exercise and nutrition plans as needed.

[0237] The "server" is a central management system that processes and stores data, generates feedback, updates exercise and nutrition plans, etc.

[0238] The present invention is a system that provides users with individually designed exercise and nutrition plans based on their fitness level, goals, and preferences, allowing them to enjoy a customized fitness experience at home and efficiently reach their goals based on ongoing fitness monitoring and feedback.

[0239] First, the user puts on a head-mounted display (HMD) and performs the initial setup. The HMD is equipped with an interface, and the user answers questions about their fitness level, goals, and preferences through voice and visual input, inputting the necessary data. The device collects this information and sends it to a server. The server creates a user profile based on the received data and stores it in a database.

[0240] The server then runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan, which is then sent to the user's HMD, where it is displayed to the user. The user can then begin their fitness activities according to the displayed exercise and nutrition plan.

[0241] During fitness activities, the HMD's built-in cameras and sensors collect real-time data on the user's movements, heart rate, and other data. The device then sends this data to a server, which analyzes it. Based on the results of this analysis, the device evaluates whether the user's movements are appropriate, whether their heart rate is within the target zone, and other factors, and generates appropriate feedback. The generated feedback is displayed on the user's HMD.

[0242] For example, while a user is walking, the HMD monitors the user's posture, number of steps, and heart rate, and if the user's stride is small, it provides real-time feedback such as "Take longer steps." In this way, the user can exercise efficiently with proper form.

[0243] Furthermore, the server periodically evaluates the user's progress and updates the exercise and nutrition plan to achieve long-term goals. The server also collects the user's self-evaluation data (subjective feedback), and combines this data with the exercise data to adjust the next plan. This process helps the user to continuously progress toward their goals.

[0244] Below are some example prompts to input to a generative AI model:

[0245] "Please provide example code to build an algorithm that captures video of a user walking at a heart rate of 72 and then evaluates their form and provides feedback."

[0246] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] The user puts on a head-mounted display (HMD) and launches the application. On the initial setup screen, the user answers questions about their fitness level, goals, and preferences and enters the required data. This input data is sent to the device, formatted, and transferred to the server. The input data includes the user's fitness level, goals, and preferences. The server receives this data, creates a user profile, and stores it in a database.

[0250] Step 2:

[0251] The server runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan. The server then performs data calculations based on input data (fitness level, goals, and preferences) to create the optimal plan. The generated plan is sent from the server to the user's HMD and displayed to the user, allowing the user to review the exercise and nutrition plan.

[0252] Step 3:

[0253] The user begins fitness activities based on the displayed exercise and nutrition plans. The HMD's built-in cameras and sensors collect data on the user's movements and heart rate in real time. This collected data includes the user's posture, number of steps, and heart rate. The device then transmits the collected data to a server.

[0254] Step 4:

[0255] The server analyzes the received data. Here, the server processes the data and evaluates whether the user's movement and heart rate are within the target zone. Based on this evaluation, the server generates appropriate feedback. The generated feedback is sent from the server to the user's HMD and displayed to the user. For example, if the user's stride is small, the server will display feedback such as "Please take longer steps."

[0256] Step 5:

[0257] The server periodically evaluates the user's progress. The user inputs their thoughts and self-evaluation of their exercise, which is then sent to the server. The server then combines the self-evaluation data with the exercise data to evaluate long-term progress. Based on this progress evaluation, the server adjusts the user's exercise and nutrition plans and updates the next plan. The updated plan is sent to the user's HMD and displayed.

[0258] Step 6:

[0259] This process is repeated until the user achieves their goal. Through regularly updated plans and feedback, users can efficiently progress towards their long-term fitness goals.

[0260] Through these steps, the present invention can effectively support users' fitness activities, providing real-time feedback and continuous adjustments toward achieving long-term goals. For example, a possible input prompt for a generative AI model could be: "Please provide sample code to build an algorithm that captures video of a user walking at a heart rate of 72 and evaluates their form and provides feedback."

[0261] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0262] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions, enabling more personalized and effective support for the user's fitness experience. This section describes the specific program processing of the system in natural language.

[0263] Overall system overview

[0264] 1. Initial Setup

[0265] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[0266] Terminal: Collects this information and sends it to the server.

[0267] Server: Creates a user profile based on the received data and stores it in a database.

[0268] 2. Generate exercise and nutrition plans

[0269] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[0270] Server: Sends the generated plan to the user's device.

[0271] Device: Displaying exercise and nutrition plans to the user.

[0272] 3. Emotional awareness and regulation

[0273] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0274] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[0275] Specific examples

[0276] 1. Example of initial settings

[0277] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[0278] Terminal: Collects response data and sends it to the server.

[0279] Server: Creates a new user profile based on the data and saves it in the database. The user's fitness level is "Beginner," their goal is "Weight loss," and their preference is "Aerobic exercise."

[0280] 2. Examples of creating exercise and nutrition plans

[0281] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[0282] Server: Sends the generated plan to the user's device.

[0283] Device: View plan details on your dashboard.

[0284] 3. Examples of Emotion Recognition and Regulation

[0285] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[0286] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[0287] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[0288] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[0289] Fitness Monitoring and Feedback

[0290] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[0291] Device: Collects exercise data using built-in cameras and sensors and sends it to a server. Examples include steps taken, heart rate, and form checks.

[0292] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[0293] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[0294] 2. Progress evaluation and plan adjustment

[0295] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[0296] Device: Sends self-assessment data to the server.

[0297] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[0298] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[0299] Device: Display next week's plan on the user's dashboard.

[0300] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[0301] The processing flow will be explained below.

[0302] Step 1:

[0303] Initial Setup

[0304] User: Launches the application and follows the on-screen prompts to begin initial setup. Answers questions about fitness level, goals, and preferences. For example, enter "Fitness level is beginner," "Goal is weight loss," and "Preference is cardio."

[0305] Terminal: Collects input data, formats it and sends it to the server.

[0306] Server: Creates a user profile based on the received data and stores it in a database.

[0307] Step 2:

[0308] Generate exercise and nutrition plans

[0309] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans, such as designing a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[0310] Server: Sends the generated plan to the user's device.

[0311] On device: Display plan details to the user, for example, daily exercise tasks and meal plans on the app dashboard.

[0312] Step 3:

[0313] Emotional awareness and regulation

[0314] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, and send them to the emotion engine in real time.

[0315] Server: Uses an emotion engine to analyze the user's emotions from the received data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is tired or stressed.

[0316] Server: Adjusts exercise and nutrition plans accordingly based on perceived emotions, for example suggesting yoga instead of walking if the user is feeling stressed.

[0317] Server: Sends the adjusted plan to the device and notifies the user.

[0318] Step 4:

[0319] Fitness Monitoring

[0320] User: Start exercising according to the specified exercise plan and follow the instructions on the device, for example, start walking.

[0321] Device: Uses built-in sensors and cameras to collect exercise data (e.g., steps, heart rate, form) in real time.

[0322] Terminal: Sends collected data to the server.

[0323] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking form accuracy and heart rate range.

[0324] Server: Generates and sends feedback to the device as needed, such as advice like "walk faster" or "check your form."

[0325] Step 5:

[0326] Progress evaluation and plan adjustment

[0327] User: Periodically (e.g., on weekends) enter their impressions and self-evaluation of their exercise. For example, they may write comments such as "exercise is getting easier" or "I feel less tired."

[0328] Terminal: Sends the entered self-evaluation data to the server.

[0329] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to comprehensively evaluate the user's progress, for example, by reviewing the intensity and frequency of exercise and adjusting the plan as needed.

[0330] Server: Updates the next week's exercise and nutrition plans and sends them to the device.

[0331] Device: Display the updated plan on the user's dashboard and guide them through the next steps.

[0332] Through these steps, the system can effectively support users' fitness activities, provide customized exercise and nutrition plans that take users' emotions into account, and provide real-time monitoring, feedback, and progress evaluation.

[0333] Example 2

[0334] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0335] Conventional fitness systems struggle to provide exercise and meal plans that fully consider a user's individual fitness level, goals, and preferences. Furthermore, they focus solely on the user's exercise, failing to adequately provide feedback or adjust plans that reflect the user's emotional state or subjective evaluation. This makes it difficult to maintain user motivation and effectively monitor progress.

[0336] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0337] In this invention, the server includes: means for generating a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and meal plan to the user; means for collecting data using an optical sensor or a voice input device to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and meal plan as needed; means for analyzing the user's emotions and adjusting the exercise plan and meal plan based on the collected data; and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals. This allows the server to provide an exercise plan and nutrition plan tailored to the user's individual needs and adjust the plan based on real-time feedback and emotional state, thereby maintaining the user's motivation and enabling more effective progress management.

[0338] "User's fitness level" is an index that indicates the user's current athletic ability and experience level, and is classified as beginner, intermediate, advanced, etc.

[0339] "Goal" refers to a specific fitness or health objective that a user wishes to achieve, such as losing weight, gaining strength, or improving endurance.

[0340] "Preferences" refers to the type of exercise and diet preferred by the user, including likes and dislikes of aerobic exercise and preferences for specific foods.

[0341] "Exercise plan" refers to a customized exercise schedule and content based on a user's fitness level, goals, and preferences.

[0342] "Meal Plan" refers to a meal schedule or composition designed to meet a user's fitness goals.

[0343] "Optical sensor" refers to a device that uses light to detect physical actions or conditions, including cameras.

[0344] "Audio input device" refers to hardware for collecting audio data, including a microphone.

[0345] "Feedback" refers to performance-based information provided to a user, such as progress and form evaluation.

[0346] An "emotion recognition engine" refers to software or hardware that analyzes a user's facial expressions, voice, etc. to recognize their emotional state.

[0347] "Progress assessment" refers to the process of analyzing a user's exercise data and self-assessment to assess their progress toward their fitness goals.

[0348] "Plan update" refers to the act of reviewing and adjusting the exercise and meal plans based on the user's progress and emotional state.

[0349] The present invention is a system that provides a user with a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences. The system includes a means for monitoring the user's exercise, a means for analyzing collected data and providing feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[0350] Overall system overview

[0351] Initial Setup

[0352] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences. For example, enter "Beginner level," "Weight loss," or "Prefers cardio."

[0353] Device: This information is collected and sent to the server using the submitProfileData() method, and the data is sent to the server in JSON format.

[0354] Server: Create a user profile based on the received data and save it in the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[0355] Generate exercise and nutrition plans

[0356] Server: Runs AI algorithms (e.g., TENSORFLOW®) based on the user's profile information to generate customized exercise and meal plans.

[0357] Server: Sends the generated plan to the user's device using structured data (e.g., JSON).

[0358] On the device: Display the exercise and meal plans to the user. Example: Use the displayPlans() method to reflect the received data in UI components.

[0359] Emotional awareness and regulation

[0360] Device: Use the built-in camera (e.g., Logitech C920) or microphone (e.g., Samson Go Mic) to capture the user's facial expressions and voice, and analyze them with the emotion engine (e.g., Affectiva SDK). Example: Use the captureEmotionData() method.

[0361] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[0362] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[0363] Fitness Monitoring and Feedback

[0364] Collecting user exercise data

[0365] User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[0366] Device: Collect exercise data using the built-in camera or sensor (e.g., Apple Watch sensor) and send it to the server. Example: Use the collectExerciseData() method to send the collected data to the server in real time.

[0367] Server: Analyzes data and evaluates user progress and form. For example, ensures form is correct and heart rate is within an appropriate range. Analyzes data using SQL queries and a secure data processing pipeline.

[0368] Providing feedback and adjusting plans

[0369] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[0370] Terminal: Show feedback to the user and move on to the next step.

[0371] Progress assessment and plan updates

[0372] Entering user self-assessment

[0373] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[0374] Device: Sends self-assessment data to the server.

[0375] Progress assessment and plan updates

[0376] Server: Integrates subjective assessments, exercise data, and data from an emotion recognition engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[0377] Server: Updates next week's exercise and meal plans and sends them to your device.

[0378] On the device: Display next week's plan on the user's dashboard and notify the user.

[0379] This system can provide personalized support for users' fitness activities, allowing them to more effectively monitor their progress, and can also adjust the system to take their emotional state into account to increase their motivation and help them achieve their goals.

[0380] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0381] Step 1: Enter user data

[0382] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[0383] Input: Fitness level (e.g., "Beginner"), goal (e.g., "Weight loss"), preference (e.g., "Cardio").

[0384] Output: User's answers to the questions.

[0385] Specific behavior: The data entered by the user is temporarily stored within the application and prepared for sending to the server.

[0386] Step 2: Send data to the server

[0387] Terminal: Collects input user data, converts it into JSON format, and sends it to the server.

[0388] Input: User response data.

[0389] Output: Data in JSON format.

[0390] Specific operation: Calls the submitProfileData() method and sends a POST request to the server's API endpoint with the user's input data.

[0391] Step 3: Create a profile

[0392] Server: Analyzes the received data, creates a user profile, and stores the data in a database.

[0393] Input: User data in JSON format.

[0394] Output: User profile, database entry.

[0395] What it does: Parses the received data using the receiveUserData() method, executes a SQL query, and saves it to the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[0396] Step 4: Create an exercise and meal plan

[0397] Server: Runs AI algorithms (e.g., TensorFlow) based on the user's profile to generate customized exercise and meal plans.

[0398] Input: User profile data.

[0399] Output: A customized exercise and meal plan.

[0400] Specific behavior: Using the TensorFlow model, execute the model.predict(user_profile_data) method and reprocess the generated plan to save it in the database.

[0401] Step 5: Submit your plan data

[0402] Server: Converts the generated plan data into JSON format and sends it to the user's device.

[0403] Enter: a customized exercise and meal plan.

[0404] Output: Plan data in JSON format.

[0405] Specific operation: The generated plan is converted into JSON and sent to the user's device via an HTTP request.

[0406] Step 6: View your plan

[0407] Terminal: Parses the received plan data and displays it to the user.

[0408] Input: Plan data in JSON format.

[0409] Output: The exercise and meal plans displayed to the user.

[0410] Specific behavior: Calls the displayPlans() method and displays the received data as UI components.

[0411] Step 7: Capturing Emotional Data

[0412] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[0413] Input: User's facial expressions and voice.

[0414] Output: Emotion data.

[0415] What it does: Use the captureEmotionData() method to collect image and audio data and pass it to an emotion recognition engine in real time (e.g., Affectiva SDK).

[0416] Step 8: Emotional Data Analysis and Feedback

[0417] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine.

[0418] Input: Emotion data.

[0419] Output: Tailored exercise and meal plans.

[0420] Specific operation: Receives the analysis results of the emotion engine, uses them to dynamically adjust exercise and meal plans, and sends them back to the device.

[0421] Step 9: Collect exercise data

[0422] User: Starts exercising according to the specified exercise plan.

[0423] Device: Collects exercise data using the built-in camera and sensors and sends it to a server.

[0424] Input: User exercise data (e.g. steps, heart rate, form check).

[0425] Output: The raw data sent to the server.

[0426] Specific operation: Use the collectExerciseData() method to send collected data to the server in real time.

[0427] Step 10: Analyze data and provide feedback

[0428] Server: Analyzes the received data and evaluates the user's progress and exercise form.

[0429] Input: Raw data (e.g. steps, heart rate, forms).

[0430] Output: The feedback message.

[0431] What it does: Evaluates data using SQL queries and data processing algorithms, and generates and sends feedback to the device, such as "pace yourself a little faster" or "add some stretching next time."

[0432] Step 11: Enter your self-assessment

[0433] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[0434] Input: Subjective evaluation data.

[0435] Output: Self-assessment data sent to the server.

[0436] Specific behavior: The self-evaluation form is filled out within the application and the data is sent to the server using the submitSelfEvaluation() method.

[0437] Step 12: Evaluate your progress and update your plan

[0438] Server: Integrates subjective assessment, motion data, and data from the emotion engine to evaluate the user's progress.

[0439] Input: Subjective evaluation data, motion data, and emotion data.

[0440] Output: Updated exercise and meal plans.

[0441] Specific operation: Executes a comprehensive evaluation algorithm using multiple data sources to generate a new plan.

[0442] Step 13: Notification and display of new plans

[0443] Server: Updates the next week's exercise and meal plans and sends them to the device.

[0444] Enter: your updated exercise and meal plans.

[0445] Output: Plan data in JSON format.

[0446] Specific operation: Convert the updated plan into JSON format and send it to the user's device.

[0447] Device: Display next week's plan on the dashboard and notify the user.

[0448] Specific behavior: Calls the displayUpdatedPlans() method to display the new plans in the user's UI.

[0449] (Application example 2)

[0450] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0451] Conventional fitness systems have limited means of providing personalized exercise and nutrition plans, and are unable to adjust plans to take into account the user's emotions and stress levels. They also struggle to accurately assess a user's progress and appropriately update plans to achieve long-term goals. As a result, users struggle to maintain their motivation and achieve their goals.

[0452] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0453] In this invention, the server includes: means for generating an exercise plan and nutrition plan individually designed based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and nutrition plan to the user; means for collecting data using a sensor or camera to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, recognizing the user's emotions, and adjusting the exercise plan and nutrition plan as needed; means for analyzing the user's emotions using an emotion recognition engine and suggesting appropriate relaxation exercises and meals; and means for periodically evaluating the user's progress and updating the plan to help the user achieve their long-term goals. This makes it possible to provide an individual exercise plan and nutrition plan that takes the user's emotions into consideration.

[0454] "User's fitness level" is an index that indicates the user's level of exercise experience and physical strength.

[0455] A "goal" is a specific purpose or result that a user wants to achieve through fitness activities.

[0456] "Preferences" are personal preferences such as the type, content, and format of exercise and nutrition preferred by the user.

[0457] An "exercise plan" is a plan of exercise content and schedule designed to suit the user.

[0458] A "nutrition plan" is a meal plan and schedule that is appropriate for a user's health and fitness goals.

[0459] The "generating means" refers to the technology or algorithm that creates the exercise and nutrition plans based on the user's input data.

[0460] The "means for providing" refers to a technology or interface for displaying or notifying the user of the generated exercise plan and nutrition plan.

[0461] "Sensors or cameras for monitoring" are measuring devices or photographing devices for collecting information on the user's physical activity in real time.

[0462] "Means for analyzing collected data and providing feedback" refers to technologies and systems that analyze exercise data collected by sensors and cameras and inform users of areas for improvement and evaluation results.

[0463] "Emotion recognition and regulation" refers to technologies and algorithms that detect a user's emotional state and use that information to appropriately modify their exercise and nutrition plans.

[0464] An "emotion recognition engine" is software or AI technology that analyzes emotions from a user's facial expressions, voice, etc.

[0465] "Means for assessing progress and updating the plan" refers to technology or algorithms that periodically evaluate a user's exercise performance and redesign an optimal plan to meet their fitness goals.

[0466] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing the collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[0467] Overall system overview

[0468] 1. Initial Setup

[0469] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[0470] Terminal: Collects this information and sends it to the server.

[0471] Server: Creates a user profile based on the received data and stores it in a database.

[0472] 2. Generate exercise and nutrition plans

[0473] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans, using AI models such as TensorFlow.

[0474] Server: Sends the generated plan to the user's device.

[0475] Device: Displaying exercise and nutrition plans to the user.

[0476] 3. Emotional awareness and regulation

[0477] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, such as Microsoft® Azure® Face API.

[0478] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[0479] Specific examples

[0480] 1. Example of initial settings

[0481] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[0482] Terminal: Collects response data and sends it to the server.

[0483] Server: Create user profiles based on the data and store them in a database, for example, using Amazon RDS.

[0484] 2. Examples of creating exercise and nutrition plans

[0485] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[0486] Server: Sends the generated plan to the user's device.

[0487] Device: View plan details on your dashboard.

[0488] 3. Examples of Emotion Recognition and Regulation

[0489] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[0490] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[0491] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[0492] Fitness Monitoring and Feedback

[0493] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[0494] Device: Collects exercise data using built-in cameras and sensors and sends it to a server, such as steps, heart rate, and form checks.

[0495] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[0496] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[0497] 2. Progress evaluation and plan adjustment

[0498] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[0499] Device: Sends self-assessment data to the server.

[0500] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[0501] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[0502] Device: Display next week's plan on the user's dashboard.

[0503] Prompt Sentence Examples

[0504] Example prompt for a generative AI model:

[0505] "Generate a beginner's fitness plan. My goal is weight loss and I prefer cardio."

[0506] "If the user is feeling stressed, suggest a relaxing exercise."

[0507] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[0508] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0509] Step 1:

[0510] Initial Setup

[0511] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[0512] Input: User's fitness level, goals, and preferences.

[0513] Output: Constructed user profile data.

[0514] Step 2:

[0515] Collection and transmission of user data

[0516] Terminal: Collects user response information and sends it to the cloud server.

[0517] Input: User input data.

[0518] Output: Data sent from the device to the server.

[0519] Step 3:

[0520] Creating a User Profile

[0521] Server: Generates a user profile based on the received data and stores it in a database.

[0522] Input: User response data received from the device.

[0523] Output: User profile data.

[0524] Step 4:

[0525] Generate exercise and nutrition plans

[0526] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[0527] Input: User profile data.

[0528] Output: A customized exercise and nutrition plan.

[0529] Step 5:

[0530] Plan Delivery

[0531] Server: Sends the generated plan to the user's device.

[0532] Input: Customized plan data.

[0533] Output: Send the plan to the device.

[0534] Step 6:

[0535] View Plans

[0536] Device: Displays the received exercise and nutrition plans to the user.

[0537] Input: Plan data sent by the server.

[0538] Output: Plan information displayed to the user.

[0539] Step 7:

[0540] Movement and Emotion Monitoring

[0541] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[0542] Input: Real-time user movement and emotion data.

[0543] Output: Camera and microphone capture data.

[0544] Step 8:

[0545] Emotional Data Analysis

[0546] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[0547] Input: Camera and microphone capture data.

[0548] Output: Analyzed emotion data.

[0549] Step 9:

[0550] Emotionally-driven plan adjustments

[0551] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[0552] Input: Analyzed emotion data.

[0553] Output: Tailored exercise and nutrition plans.

[0554] Step 10:

[0555] Providing Feedback

[0556] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[0557] Input: User's motion and emotion data.

[0558] Output: The generated feedback information.

[0559] Step 11:

[0560] View Feedback

[0561] Terminal: Display feedback to the user.

[0562] Input: Feedback data sent by the server.

[0563] Output: Feedback information that is displayed to the user.

[0564] Step 12:

[0565] Collecting self-assessments

[0566] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[0567] Input: User self-assessment data.

[0568] Output: Collected self-assessment data.

[0569] Step 13:

[0570] Submitting and integrating self-assessments

[0571] Device: Sends self-evaluation data to the server, which then combines the subjective evaluation, movement data, and data from the emotion engine to evaluate the user's progress.

[0572] Input: User self-evaluation data, movement data, and emotion data.

[0573] Output: Consolidated progress assessment data.

[0574] Step 14:

[0575] Update your plan based on progress

[0576] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[0577] Input: Consolidated progress assessment data.

[0578] Output: Updated exercise and nutrition plans.

[0579] Step 15:

[0580] View renewal plans

[0581] Device: Display next week's plan on the user's dashboard.

[0582] Input: The renewal plan sent by the server.

[0583] Output: Renewal plan information displayed to the user.

[0584] In this way, the system can provide detailed support for users' fitness activities and provide them with customized exercise and nutrition plans that take their emotions into account.

[0585] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0586] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0588] [Second embodiment]

[0589] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0590] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0591] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0593] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0595] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0596] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0597] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0598] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0600] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0601] The present invention is a system that provides individually designed exercise and nutrition plans based on a user's fitness level, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. This section describes the specific program processing of the system in natural language.

[0602] Overall system overview

[0603] 1. Initial Setup

[0604] User: Opens the application and begins the initial setup. The user answers questions about their fitness level, goals, and preferences and enters the required data.

[0605] Terminal: Collects this information and sends it to the server.

[0606] Server: Creates a user profile based on the received data and stores it in a database.

[0607] 2. Generate exercise and nutrition plans

[0608] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans.

[0609] Server: Sends the generated plan to the user's device.

[0610] Device: Displaying exercise and nutrition plans to the user.

[0611] Specific examples

[0612] 1. Example of initial settings

[0613] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[0614] Terminal: Collects questions and answers as data, formats the user's responses, and sends the data to the server.

[0615] Server: Based on the received information, create a new user entry in the database and register the fitness level "Beginner," goal "Weight loss," and preference "Aerobic exercise."

[0616] 2. Examples of creating exercise and nutrition plans

[0617] Server: Runs AI algorithms based on the user's profile information to generate a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[0618] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[0619] On your device: Display plan details on a dashboard and allow users to track their daily progress.

[0620] Fitness Monitoring and Feedback

[0621] 1. User: Starts exercising according to a specified exercise plan. For example, starts walking.

[0622] Device: Collects user exercise data (e.g., steps, heart rate) in real time using built-in cameras and sensors (e.g., heart rate monitors on smartwatches).

[0623] Terminal: Sends collected data to the server.

[0624] 2. Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[0625] Server: Generates feedback as needed, for example, creating a message like "You're walking too slowly, please walk faster."

[0626] Device: Notify the user of the feedback.

[0627] 3. Progress evaluation and plan adjustment

[0628] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[0629] Device: Sends self-assessment data to the server.

[0630] Server: Integrates subjective assessment and exercise data to comprehensively evaluate the user's progress. Updates the next week's exercise and nutrition plans as needed.

[0631] Server: Sends the updated plan to the user's device.

[0632] Device: Display next week's plan on the user's dashboard.

[0633] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[0634] The processing flow will be explained below.

[0635] Step 1:

[0636] Initial Setup

[0637] User: Opens the application and taps the start button for initial setup.

[0638] Device: Prompts the user with questions about their fitness level, goals, and preferences, and asks them to enter their answers. For example, they might enter "Fitness level is beginner," "Goal is weight loss," and "Preferences are aerobic exercise."

[0639] Terminal: Formats the entered data and sends it to the server.

[0640] Server: Creates a user profile based on the received data and stores it in a database.

[0641] Step 2:

[0642] Generate exercise and nutrition plans

[0643] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[0644] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[0645] On the device: Display an exercise and nutrition plan to the user, for example, a balanced diet plan of 2000 kcal and 30 minutes of walking three times a week.

[0646] Step 3:

[0647] Fitness Monitoring

[0648] User: Starts exercising according to the specified exercise plan. For example, starts walking.

[0649] Device: Uses built-in cameras and sensors to collect real-time exercise data from users, such as capturing their form with a camera and measuring their heart rate with a heart rate monitor.

[0650] Terminal: Sends collected data to the server.

[0651] Step 4:

[0652] Feedback and Adjustments

[0653] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[0654] Server: Generates feedback on areas that need adjustment or improvement. For example, create a message like "Your pace is too slow, please walk a little faster."

[0655] Server: Sends feedback to the user's device.

[0656] Device: Notify the user of the feedback.

[0657] Step 5:

[0658] Progress evaluation and plan adjustment

[0659] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[0660] Terminal: Sends the entered self-evaluation data to the server.

[0661] Server: Integrates subjective evaluations and exercise data to comprehensively evaluate the user's progress, such as improving form or increasing exercise intensity.

[0662] Server: Adjust / update next week's exercise and nutrition plan as needed, for example, increase walking time to 40 minutes.

[0663] Server: Sends the updated plan to the user's device.

[0664] Device: Display next week's plan on the user's dashboard.

[0665] Through these steps, the system can effectively support users' fitness activities, encouraging them to maintain continuous motivation and achieve their goals.

[0666] Example 1

[0667] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] To meet today's individual fitness needs, many users are seeking individually designed exercise and nutrition plans. However, existing systems lack sufficient customization based on a user's physiological information, goals, and preferences, and lack continuous feedback and plan adjustments to achieve long-term goals. This makes it difficult for users to effectively achieve their goals, and providing a sustainable fitness plan is a challenge.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0670] In this invention, the server includes means for generating a personalized exercise plan and nutrition plan based on the user's physiological information, goals, and preferences, means for providing the generated exercise plan and nutrition plan to the user, means for collecting data using a sensor or imaging device to monitor the user's exercise, means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and nutrition plan as needed, means for adjusting the exercise plan and nutrition plan using a generative AI model, and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals, thereby enabling customization of the exercise and nutrition plan to meet the user's individual needs, continuous monitoring and feedback, and adjustment of the plan according to progress.

[0671] "Physiological information" is data related to the user's health condition and physical characteristics, and examples include height, weight, heart rate, blood pressure, etc.

[0672] A "goal" is a goal that a user wishes to achieve through a fitness activity, examples of which include weight loss, muscle gain, and endurance improvement.

[0673] "Preferences" are the user's preferred types of fitness and eating style, examples of which include cardio, strength training, plant-based eating, etc.

[0674] An "exercise plan" is a specific exercise schedule or menu provided to the user, and examples include 30 minutes of walking three times a week and strength training twice a week.

[0675] A "nutritional plan" is a specific dietary instruction or meal plan provided to a user, and examples include a calorie-restricted diet, a protein-enriched diet, etc.

[0676] A "sensor" is a device that collects a user's exercise data in real time, and examples include smartwatches and fitness trackers.

[0677] "Photography device" refers to a camera or video device used to monitor the user's posture and exercise form.

[0678] "Means for analyzing data" refers to algorithms or software used to analyze collected data and assess the user's progress or condition.

[0679] "Feedback" refers to suggestions for improvement or advice provided to users based on the analysis results, such as "Please speed up your pace" or "Please add this exercise next time."

[0680] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate and adjust a user's plan.

[0681] A "human interface" is an interface through which a user interacts with a system, and examples include smartphone applications and web browsers.

[0682] A "database" is an information storage system for managing user profiles and history data stored on a server.

[0683] "Long-term progress" refers to the results or changes in a user's fitness activities over a period of time.

[0684] The above definitions allow a clear understanding of each element in the present invention.

[0685] The present invention provides a system that provides individually designed exercise and nutrition plans based on a user's physiological information, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. The operation of the system is described in detail below.

[0686] Initial Setup

[0687] 1. User: Launches the application on their smartphone or computer and begins the first-time setup. The user answers questions about their fitness level, goals, and preferences and enters them into the application.

[0688] 2. Terminal: Collects data entered by the user and sends it to the server.

[0689] 3. Server: Creates a user profile based on the received data and stores it in a database, registering the user's fitness level, goals, preferences, etc.

[0690] Generate exercise and nutrition plans

[0691] 1. Server: Runs AI algorithms based on the stored user profile information to generate customized exercise and nutrition plans. The AI ​​models used can be deep learning or machine learning algorithms.

[0692] 2. Server: Sends the generated plan to the user's device.

[0693] 3. Terminal: The received exercise plan and nutrition plan are displayed on the user interface and provided to the user.

[0694] Fitness Monitoring and Feedback

[0695] 1. User: Starts exercising according to a specified exercise plan, for example, walking.

[0696] 2. Device: Utilizing built-in sensors in smartwatches and smartphones, users' exercise data (e.g., steps, heart rate) is collected in real time. Examples of sensors include heart rate monitors and pedometers.

[0697] 3. Terminal: Sends the collected exercise data to the server.

[0698] 4. Server: Analyzes the received data and evaluates the user's progress and exercise form using statistical methods and machine learning techniques.

[0699] 5. Server: Generates feedback as needed, for example, "Your heart rate is dropping. Please pick up the pace a bit."

[0700] 6. Terminal: Providing feedback to the user interface.

[0701] Progress evaluation and plan adjustment

[0702] 1. User: Enter your thoughts and self-evaluation of your exercise regularly. For example, you could do a self-evaluation every weekend.

[0703] 2. Terminal: Sends self-assessment data to the server.

[0704] 3. Server: Integrates subjective and objective exercise data to comprehensively evaluate the user's progress and update the next week's exercise and nutrition plans as needed.

[0705] 4. Server: Sends the updated plan to the user's device.

[0706] 5. Terminal: Display next week's plan on the user interface.

[0707] In a concrete example of this system, if a user selects "beginner level," "weight loss," and "aerobic exercise," the server generates an exercise plan that includes 30 minutes of walking three times a week and a nutrition plan that includes a calorie-restricted diet.The system also uses a smartwatch to monitor heart rate and step count, providing real-time feedback to adjust the pace as needed.

[0708] Prompt Sentence Examples

[0709] "What are your fitness goals? (e.g., weight loss, strength gain, endurance improvement)"

[0710] "What type of exercise do you prefer? (e.g., cardio, strength training, yoga)"

[0711] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[0712] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0713] Step 1:

[0714] User: Launches the application and begins the initial setup. The user answers questions and inputs their fitness level, goals, and preferences. Examples of inputs include "Beginner," "Weight loss," and "Prefers cardio."

[0715] Input: Fitness level, goals, and preferences

[0716] Output: Initial setting data

[0717] Step 2:

[0718] Terminal: Collects data entered by the user and formats it, for example, into JSON format data, and then sends it to the server.

[0719] Input: Initialization data

[0720] Output: Formatted data

[0721] Step 3:

[0722] Server: Receives the formatted data and stores it in a database. Creates a new user profile with fitness level "Beginner", goal "Weight loss", and preference "Aerobic exercise".

[0723] Input: Formatted data

[0724] Output: User profile stored in the database

[0725] Step 4:

[0726] Server: Runs AI algorithms based on the user profile. Specifically, it uses deep learning algorithms to generate exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to the user's needs.

[0727] Input: Database user profile

[0728] Output: Customized exercise and nutrition plans

[0729] Step 5:

[0730] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[0731] Enter: a customized exercise and nutrition plan.

[0732] Output: The plan sent to the user's device

[0733] Step 6:

[0734] Device: Displays the received exercise and nutrition plans on a user interface. A screen appears providing the user with specific exercise and dietary instructions.

[0735] Input: The plan sent to the user's device

[0736] Output: Exercise and nutrition plans displayed in a user interface

[0737] Step 7:

[0738] User: Follows the specified exercise plan and starts exercising. For example, starts walking.

[0739] Input: Exercise plan displayed in the user interface

[0740] Output: The exercise performed

[0741] Step 8:

[0742] Device: Using built-in sensors in smartwatches and smartphones (e.g., heart rate monitors, pedometers), we collect data on your exercise in real time.

[0743] Input: Exercise to be performed

[0744] Output: Collected exercise data

[0745] Step 9:

[0746] Device: Sends collected exercise data to the server.

[0747] Input: Collected exercise data

[0748] Output: Exercise data sent to the server

[0749] Step 10:

[0750] Server: Analyzes the received exercise data. It uses statistical methods and machine learning algorithms to evaluate the user's progress and exercise form. For example, it checks whether the heart rate is within the appropriate range.

[0751] Input: Exercise data sent to the server

[0752] Output: Analysis results

[0753] Step 11:

[0754] Server: Generates feedback based on the analysis results, such as a message like "Your heart rate is dropping. Please pick up the pace a bit."

[0755] Input: Analysis results

[0756] Output: Feedback message

[0757] Step 12:

[0758] Server: Sends feedback messages to the user's device and notifies them through the user interface.

[0759] Input: Feedback message

[0760] Output: Feedback message sent to the user's device

[0761] Step 13:

[0762] User: Periodically (e.g., at the end of the week) enter a self-evaluation, which includes thoughts on exercise and achievement.

[0763] Input: Feedback message

[0764] Output: Self-assessment

[0765] Step 14:

[0766] Terminal: Collects self-assessment data and sends it to the server.

[0767] Input: Self-assessment data

[0768] Output: Self-assessment data sent to the server

[0769] Step 15:

[0770] Server: Combines subjective assessments and objective exercise data to provide a comprehensive assessment of the user's progress, and determines whether the user's exercise and nutrition plans for the next week need to be updated.

[0771] Input: Subjective assessment data and exercise data

[0772] Output: Exercise and nutrition plans that need updating

[0773] Step 16:

[0774] Server: Sends the updated plan to the user's device.

[0775] Input: Exercise and nutrition plans that need updating

[0776] Output: Renewal plan sent to user's device

[0777] Step 17:

[0778] Terminal: Display the updated plan in the user interface and provide the user with instructions for the next week.

[0779] Input: The renewal plan sent to the user's device

[0780] Output: Update plan displayed in the user interface

[0781] (Application example 1)

[0782] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0783] While existing fitness systems offer personalized exercise and nutritional plans, they often lack the ability to analyze users' movements in real time and provide interactive feedback. Furthermore, they lack the means to effectively enhance the user experience through specific wearable displays, making it difficult to achieve long-term fitness goals.

[0784] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0785] In this invention, the server includes means for analyzing the user's movements in real time and providing interactive feedback using the wearable display, means for formatting the collected data and transmitting it to the server, and means for periodically evaluating the user's progress and updating the plan for long-term goal achievement, thereby enabling continuous adjustment of the plan for long-term goal achievement while monitoring the user's movements in real time and providing appropriate feedback.

[0786] "User's fitness level" is an index that indicates the user's current physical ability and health condition.

[0787] "Goal" refers to the outcome or goal that a user wishes to achieve through their fitness activity.

[0788] "Preferences" refers to the types of exercise and eating habits that a user prefers.

[0789] An "exercise plan" is a specific exercise schedule designed based on a user's fitness level, goals, and preferences.

[0790] A "nutrition plan" is a meal plan designed to suit a user's fitness goals and health status.

[0791] "Sensor or camera" refers to a device used to monitor a user's activity and collect data.

[0792] "Feedback" is advice or guidance provided to users based on collected data.

[0793] A "wearable display device" is a display device that is worn by the user, such as a head-mounted display.

[0794] "Interactive feedback" is feedback that is provided immediately in response to a user's real-time actions.

[0795] "Means for formatting collected data and sending it to the server" refers to the process for converting data collected from users into an appropriate format and sending it to the server.

[0796] "Means to regularly evaluate your progress and update your plan to meet your long-term goals" refers to the process of regularly evaluating your fitness progress and adjusting and updating your exercise and nutrition plans as needed.

[0797] The "server" is a central management system that processes and stores data, generates feedback, updates exercise and nutrition plans, etc.

[0798] The present invention is a system that provides users with individually designed exercise and nutrition plans based on their fitness level, goals, and preferences, allowing them to enjoy a customized fitness experience at home and efficiently reach their goals based on ongoing fitness monitoring and feedback.

[0799] First, the user puts on a head-mounted display (HMD) and performs the initial setup. The HMD is equipped with an interface, and the user answers questions about their fitness level, goals, and preferences through voice and visual input, inputting the necessary data. The device collects this information and sends it to a server. The server creates a user profile based on the received data and stores it in a database.

[0800] The server then runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan, which is then sent to the user's HMD, where it is displayed to the user. The user can then begin their fitness activities according to the displayed exercise and nutrition plan.

[0801] During fitness activities, the HMD's built-in cameras and sensors collect real-time data on the user's movements, heart rate, and other data. The device then sends this data to a server, which analyzes it. Based on the results of this analysis, the device evaluates whether the user's movements are appropriate, whether their heart rate is within the target zone, and other factors, and generates appropriate feedback. The generated feedback is displayed on the user's HMD.

[0802] For example, while a user is walking, the HMD monitors the user's posture, number of steps, and heart rate, and if the user's stride is small, it provides real-time feedback such as "Take longer steps." In this way, the user can exercise efficiently with proper form.

[0803] Furthermore, the server periodically evaluates the user's progress and updates the exercise and nutrition plan to achieve long-term goals. The server also collects the user's self-evaluation data (subjective feedback), and combines this data with the exercise data to adjust the next plan. This process helps the user to continuously progress toward their goals.

[0804] Below are some example prompts to input to a generative AI model:

[0805] "Please provide example code to build an algorithm that captures video of a user walking at a heart rate of 72 and then evaluates their form and provides feedback."

[0806] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[0807] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0808] Step 1:

[0809] The user puts on a head-mounted display (HMD) and launches the application. On the initial setup screen, the user answers questions about their fitness level, goals, and preferences and enters the required data. This input data is sent to the device, formatted, and transferred to the server. The input data includes the user's fitness level, goals, and preferences. The server receives this data, creates a user profile, and stores it in a database.

[0810] Step 2:

[0811] The server runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan. The server then performs data calculations based on input data (fitness level, goals, and preferences) to create the optimal plan. The generated plan is sent from the server to the user's HMD and displayed to the user, allowing the user to review the exercise and nutrition plan.

[0812] Step 3:

[0813] The user begins fitness activities based on the displayed exercise and nutrition plans. The HMD's built-in cameras and sensors collect data on the user's movements and heart rate in real time. This collected data includes the user's posture, number of steps, and heart rate. The device then transmits the collected data to a server.

[0814] Step 4:

[0815] The server analyzes the received data. Here, the server processes the data and evaluates whether the user's movement and heart rate are within the target zone. Based on this evaluation, the server generates appropriate feedback. The generated feedback is sent from the server to the user's HMD and displayed to the user. For example, if the user's stride is small, the server will display feedback such as "Please take longer steps."

[0816] Step 5:

[0817] The server periodically evaluates the user's progress. The user inputs their thoughts and self-evaluation of their exercise, which is then sent to the server. The server then combines the self-evaluation data with the exercise data to evaluate long-term progress. Based on this progress evaluation, the server adjusts the user's exercise and nutrition plans and updates the next plan. The updated plan is sent to the user's HMD and displayed.

[0818] Step 6:

[0819] This process is repeated until the user achieves their goal. Through regularly updated plans and feedback, users can efficiently progress towards their long-term fitness goals.

[0820] Through these steps, the present invention can effectively support users' fitness activities, providing real-time feedback and continuous adjustments toward achieving long-term goals. For example, a possible input prompt for a generative AI model could be: "Please provide sample code to build an algorithm that captures video of a user walking at a heart rate of 72 and evaluates their form and provides feedback."

[0821] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0822] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions, enabling more personalized and effective support for the user's fitness experience. This section describes the specific program processing of the system in natural language.

[0823] Overall system overview

[0824] 1. Initial Setup

[0825] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[0826] Terminal: Collects this information and sends it to the server.

[0827] Server: Creates a user profile based on the received data and stores it in a database.

[0828] 2. Generate exercise and nutrition plans

[0829] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[0830] Server: Sends the generated plan to the user's device.

[0831] Device: Displaying exercise and nutrition plans to the user.

[0832] 3. Emotional awareness and regulation

[0833] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0834] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[0835] Specific examples

[0836] 1. Example of initial settings

[0837] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[0838] Terminal: Collects response data and sends it to the server.

[0839] Server: Creates a new user profile based on the data and saves it in the database. The user's fitness level is "Beginner," their goal is "Weight loss," and their preference is "Aerobic exercise."

[0840] 2. Examples of creating exercise and nutrition plans

[0841] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[0842] Server: Sends the generated plan to the user's device.

[0843] Device: View plan details on your dashboard.

[0844] 3. Examples of Emotion Recognition and Regulation

[0845] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[0846] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[0847] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[0848] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[0849] Fitness Monitoring and Feedback

[0850] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[0851] Device: Collects exercise data using built-in cameras and sensors and sends it to a server. Examples include steps taken, heart rate, and form checks.

[0852] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[0853] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[0854] 2. Progress evaluation and plan adjustment

[0855] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[0856] Device: Sends self-assessment data to the server.

[0857] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[0858] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[0859] Device: Display next week's plan on the user's dashboard.

[0860] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[0861] The processing flow will be explained below.

[0862] Step 1:

[0863] Initial Setup

[0864] User: Launches the application and follows the on-screen prompts to begin initial setup. Answers questions about fitness level, goals, and preferences. For example, enter "Fitness level is beginner," "Goal is weight loss," and "Preference is cardio."

[0865] Terminal: Collects input data, formats it and sends it to the server.

[0866] Server: Creates a user profile based on the received data and stores it in a database.

[0867] Step 2:

[0868] Generate exercise and nutrition plans

[0869] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans, such as designing a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[0870] Server: Sends the generated plan to the user's device.

[0871] On device: Display plan details to the user, for example, daily exercise tasks and meal plans on the app dashboard.

[0872] Step 3:

[0873] Emotional awareness and regulation

[0874] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, and send them to the emotion engine in real time.

[0875] Server: Uses an emotion engine to analyze the user's emotions from the received data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is tired or stressed.

[0876] Server: Adjusts exercise and nutrition plans accordingly based on perceived emotions, for example suggesting yoga instead of walking if the user is feeling stressed.

[0877] Server: Sends the adjusted plan to the device and notifies the user.

[0878] Step 4:

[0879] Fitness Monitoring

[0880] User: Start exercising according to the specified exercise plan and follow the instructions on the device, for example, start walking.

[0881] Device: Uses built-in sensors and cameras to collect exercise data (e.g., steps, heart rate, form) in real time.

[0882] Terminal: Sends collected data to the server.

[0883] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking form accuracy and heart rate range.

[0884] Server: Generates and sends feedback to the device as needed, such as advice like "walk faster" or "check your form."

[0885] Step 5:

[0886] Progress evaluation and plan adjustment

[0887] User: Periodically (e.g., on weekends) enter their impressions and self-evaluation of their exercise. For example, they may write comments such as "exercise is getting easier" or "I feel less tired."

[0888] Terminal: Sends the entered self-evaluation data to the server.

[0889] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to comprehensively evaluate the user's progress, for example, by reviewing the intensity and frequency of exercise and adjusting the plan as needed.

[0890] Server: Updates the next week's exercise and nutrition plans and sends them to the device.

[0891] Device: Display the updated plan on the user's dashboard and guide them through the next steps.

[0892] Through these steps, the system can effectively support users' fitness activities, provide customized exercise and nutrition plans that take users' emotions into account, and provide real-time monitoring, feedback, and progress evaluation.

[0893] Example 2

[0894] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0895] Conventional fitness systems struggle to provide exercise and meal plans that fully consider a user's individual fitness level, goals, and preferences. Furthermore, they focus solely on the user's exercise, failing to adequately provide feedback or adjust plans that reflect the user's emotional state or subjective evaluation. This makes it difficult to maintain user motivation and effectively monitor progress.

[0896] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0897] In this invention, the server includes: means for generating a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and meal plan to the user; means for collecting data using an optical sensor or a voice input device to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and meal plan as needed; means for analyzing the user's emotions and adjusting the exercise plan and meal plan based on the collected data; and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals. This allows the server to provide an exercise plan and nutrition plan tailored to the user's individual needs and adjust the plan based on real-time feedback and emotional state, thereby maintaining the user's motivation and enabling more effective progress management.

[0898] "User's fitness level" is an index that indicates the user's current athletic ability and experience level, and is classified as beginner, intermediate, advanced, etc.

[0899] "Goal" refers to a specific fitness or health objective that a user wishes to achieve, such as losing weight, gaining strength, or improving endurance.

[0900] "Preferences" refers to the type of exercise and diet preferred by the user, including likes and dislikes of aerobic exercise and preferences for specific foods.

[0901] "Exercise plan" refers to a customized exercise schedule and content based on a user's fitness level, goals, and preferences.

[0902] "Meal Plan" refers to a meal schedule or composition designed to meet a user's fitness goals.

[0903] "Optical sensor" refers to a device that uses light to detect physical actions or conditions, including cameras.

[0904] "Audio input device" refers to hardware for collecting audio data, including a microphone.

[0905] "Feedback" refers to performance-based information provided to a user, such as progress and form evaluation.

[0906] An "emotion recognition engine" refers to software or hardware that analyzes a user's facial expressions, voice, etc. to recognize their emotional state.

[0907] "Progress assessment" refers to the process of analyzing a user's exercise data and self-assessment to assess their progress toward their fitness goals.

[0908] "Plan update" refers to the act of reviewing and adjusting the exercise and meal plans based on the user's progress and emotional state.

[0909] The present invention is a system that provides a user with a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences. The system includes a means for monitoring the user's exercise, a means for analyzing collected data and providing feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[0910] Overall system overview

[0911] Initial Setup

[0912] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences. For example, enter "Beginner level," "Weight loss," or "Prefers cardio."

[0913] Device: This information is collected and sent to the server using the submitProfileData() method, and the data is sent to the server in JSON format.

[0914] Server: Create a user profile based on the received data and save it in the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[0915] Generate exercise and nutrition plans

[0916] Server: Runs AI algorithms (e.g., TensorFlow) based on the user's profile information to generate customized exercise and meal plans.

[0917] Server: Sends the generated plan to the user's device using structured data (e.g., JSON).

[0918] On the device: Display the exercise and meal plans to the user. Example: Use the displayPlans() method to reflect the received data in UI components.

[0919] Emotional awareness and regulation

[0920] Device: Use the built-in camera (e.g., Logitech C920) or microphone (e.g., Samson Go Mic) to capture the user's facial expressions and voice, and analyze them with the emotion engine (e.g., Affectiva SDK). Example: Use the captureEmotionData() method.

[0921] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[0922] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[0923] Fitness Monitoring and Feedback

[0924] Collecting user exercise data

[0925] User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[0926] Device: Collect exercise data using the built-in camera or sensor (e.g., Apple Watch sensor) and send it to the server. Example: Use the collectExerciseData() method to send the collected data to the server in real time.

[0927] Server: Analyzes data and evaluates user progress and form. For example, ensures form is correct and heart rate is within an appropriate range. Analyzes data using SQL queries and a secure data processing pipeline.

[0928] Providing feedback and adjusting plans

[0929] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[0930] Terminal: Show feedback to the user and move on to the next step.

[0931] Progress assessment and plan updates

[0932] Entering user self-assessment

[0933] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[0934] Device: Sends self-assessment data to the server.

[0935] Progress assessment and plan updates

[0936] Server: Integrates subjective assessments, exercise data, and data from an emotion recognition engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[0937] Server: Updates next week's exercise and meal plans and sends them to your device.

[0938] On the device: Display next week's plan on the user's dashboard and notify the user.

[0939] This system can provide personalized support for users' fitness activities, allowing them to more effectively monitor their progress, and can also adjust the system to take their emotional state into account to increase their motivation and help them achieve their goals.

[0940] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0941] Step 1: Enter user data

[0942] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[0943] Input: Fitness level (e.g., "Beginner"), goal (e.g., "Weight loss"), preference (e.g., "Cardio").

[0944] Output: User's answers to the questions.

[0945] Specific behavior: The data entered by the user is temporarily stored within the application and prepared for sending to the server.

[0946] Step 2: Send data to the server

[0947] Terminal: Collects input user data, converts it into JSON format, and sends it to the server.

[0948] Input: User response data.

[0949] Output: Data in JSON format.

[0950] Specific operation: Calls the submitProfileData() method and sends a POST request to the server's API endpoint with the user's input data.

[0951] Step 3: Create a profile

[0952] Server: Analyzes the received data, creates a user profile, and stores the data in a database.

[0953] Input: User data in JSON format.

[0954] Output: User profile, database entry.

[0955] What it does: Parses the received data using the receiveUserData() method, executes a SQL query, and saves it to the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[0956] Step 4: Create an exercise and meal plan

[0957] Server: Runs AI algorithms (e.g., TensorFlow) based on the user's profile to generate customized exercise and meal plans.

[0958] Input: User profile data.

[0959] Output: A customized exercise and meal plan.

[0960] Specific behavior: Using the TensorFlow model, execute the model.predict(user_profile_data) method and reprocess the generated plan to save it in the database.

[0961] Step 5: Submit your plan data

[0962] Server: Converts the generated plan data into JSON format and sends it to the user's device.

[0963] Enter: a customized exercise and meal plan.

[0964] Output: Plan data in JSON format.

[0965] Specific operation: The generated plan is converted into JSON and sent to the user's device via an HTTP request.

[0966] Step 6: View your plan

[0967] Terminal: Parses the received plan data and displays it to the user.

[0968] Input: Plan data in JSON format.

[0969] Output: The exercise and meal plans displayed to the user.

[0970] Specific behavior: Calls the displayPlans() method and displays the received data as UI components.

[0971] Step 7: Capturing Emotional Data

[0972] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[0973] Input: User's facial expressions and voice.

[0974] Output: Emotion data.

[0975] What it does: Use the captureEmotionData() method to collect image and audio data and pass it to an emotion recognition engine in real time (e.g., Affectiva SDK).

[0976] Step 8: Emotional Data Analysis and Feedback

[0977] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine.

[0978] Input: Emotion data.

[0979] Output: Tailored exercise and meal plans.

[0980] Specific operation: Receives the analysis results of the emotion engine, uses them to dynamically adjust exercise and meal plans, and sends them back to the device.

[0981] Step 9: Collect exercise data

[0982] User: Starts exercising according to the specified exercise plan.

[0983] Device: Collects exercise data using the built-in camera and sensors and sends it to a server.

[0984] Input: User exercise data (e.g. steps, heart rate, form check).

[0985] Output: The raw data sent to the server.

[0986] Specific operation: Use the collectExerciseData() method to send collected data to the server in real time.

[0987] Step 10: Analyze data and provide feedback

[0988] Server: Analyzes the received data and evaluates the user's progress and exercise form.

[0989] Input: Raw data (e.g. steps, heart rate, forms).

[0990] Output: The feedback message.

[0991] What it does: Evaluates data using SQL queries and data processing algorithms, and generates and sends feedback to the device, such as "pace yourself a little faster" or "add some stretching next time."

[0992] Step 11: Enter your self-assessment

[0993] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[0994] Input: Subjective evaluation data.

[0995] Output: Self-assessment data sent to the server.

[0996] Specific behavior: The self-evaluation form is filled out within the application and the data is sent to the server using the submitSelfEvaluation() method.

[0997] Step 12: Evaluate your progress and update your plan

[0998] Server: Integrates subjective assessment, motion data, and data from the emotion engine to evaluate the user's progress.

[0999] Input: Subjective evaluation data, motion data, and emotion data.

[1000] Output: Updated exercise and meal plans.

[1001] Specific operation: Executes a comprehensive evaluation algorithm using multiple data sources to generate a new plan.

[1002] Step 13: Notification and display of new plans

[1003] Server: Updates the next week's exercise and meal plans and sends them to the device.

[1004] Enter: your updated exercise and meal plans.

[1005] Output: Plan data in JSON format.

[1006] Specific operation: Convert the updated plan into JSON format and send it to the user's device.

[1007] Device: Display next week's plan on the dashboard and notify the user.

[1008] Specific behavior: Calls the displayUpdatedPlans() method to display the new plans in the user's UI.

[1009] (Application example 2)

[1010] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1011] Conventional fitness systems have limited means of providing personalized exercise and nutrition plans, and are unable to adjust plans to take into account the user's emotions and stress levels. They also struggle to accurately assess a user's progress and appropriately update plans to achieve long-term goals. As a result, users struggle to maintain their motivation and achieve their goals.

[1012] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1013] In this invention, the server includes: means for generating an exercise plan and nutrition plan individually designed based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and nutrition plan to the user; means for collecting data using a sensor or camera to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, recognizing the user's emotions, and adjusting the exercise plan and nutrition plan as needed; means for analyzing the user's emotions using an emotion recognition engine and suggesting appropriate relaxation exercises and meals; and means for periodically evaluating the user's progress and updating the plan to help the user achieve their long-term goals. This makes it possible to provide an individual exercise plan and nutrition plan that takes the user's emotions into consideration.

[1014] "User's fitness level" is an index that indicates the user's level of exercise experience and physical strength.

[1015] A "goal" is a specific purpose or result that a user wants to achieve through fitness activities.

[1016] "Preferences" are personal preferences such as the type, content, and format of exercise and nutrition preferred by the user.

[1017] An "exercise plan" is a plan of exercise content and schedule designed to suit the user.

[1018] A "nutrition plan" is a meal plan and schedule that is appropriate for a user's health and fitness goals.

[1019] The "generating means" refers to the technology or algorithm that creates the exercise and nutrition plans based on the user's input data.

[1020] The "means for providing" refers to a technology or interface for displaying or notifying the user of the generated exercise plan and nutrition plan.

[1021] "Sensors or cameras for monitoring" are measuring devices or photographing devices for collecting information on the user's physical activity in real time.

[1022] "Means for analyzing collected data and providing feedback" refers to technologies and systems that analyze exercise data collected by sensors and cameras and inform users of areas for improvement and evaluation results.

[1023] "Emotion recognition and regulation" refers to technologies and algorithms that detect a user's emotional state and use that information to appropriately modify their exercise and nutrition plans.

[1024] An "emotion recognition engine" is software or AI technology that analyzes emotions from a user's facial expressions, voice, etc.

[1025] "Means for assessing progress and updating the plan" refers to technology or algorithms that periodically evaluate a user's exercise performance and redesign an optimal plan to meet their fitness goals.

[1026] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing the collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[1027] Overall system overview

[1028] 1. Initial Setup

[1029] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[1030] Terminal: Collects this information and sends it to the server.

[1031] Server: Creates a user profile based on the received data and stores it in a database.

[1032] 2. Generate exercise and nutrition plans

[1033] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans, using AI models such as TensorFlow.

[1034] Server: Sends the generated plan to the user's device.

[1035] Device: Displaying exercise and nutrition plans to the user.

[1036] 3. Emotional awareness and regulation

[1037] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, such as the Microsoft Azure Face API.

[1038] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[1039] Specific examples

[1040] 1. Example of initial settings

[1041] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[1042] Terminal: Collects response data and sends it to the server.

[1043] Server: Create user profiles based on the data and store them in a database, for example, using Amazon RDS.

[1044] 2. Examples of creating exercise and nutrition plans

[1045] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[1046] Server: Sends the generated plan to the user's device.

[1047] Device: View plan details on your dashboard.

[1048] 3. Examples of Emotion Recognition and Regulation

[1049] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[1050] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[1051] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[1052] Fitness Monitoring and Feedback

[1053] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[1054] Device: Collects exercise data using built-in cameras and sensors and sends it to a server, such as steps, heart rate, and form checks.

[1055] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[1056] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[1057] 2. Progress evaluation and plan adjustment

[1058] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[1059] Device: Sends self-assessment data to the server.

[1060] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[1061] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[1062] Device: Display next week's plan on the user's dashboard.

[1063] Prompt Sentence Examples

[1064] Example prompt for a generative AI model:

[1065] "Generate a beginner's fitness plan. My goal is weight loss and I prefer cardio."

[1066] "If the user is feeling stressed, suggest a relaxing exercise."

[1067] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[1068] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1069] Step 1:

[1070] Initial Setup

[1071] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[1072] Input: User's fitness level, goals, and preferences.

[1073] Output: Constructed user profile data.

[1074] Step 2:

[1075] Collection and transmission of user data

[1076] Terminal: Collects user response information and sends it to the cloud server.

[1077] Input: User input data.

[1078] Output: Data sent from the device to the server.

[1079] Step 3:

[1080] Creating a User Profile

[1081] Server: Generates a user profile based on the received data and stores it in a database.

[1082] Input: User response data received from the device.

[1083] Output: User profile data.

[1084] Step 4:

[1085] Generate exercise and nutrition plans

[1086] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[1087] Input: User profile data.

[1088] Output: A customized exercise and nutrition plan.

[1089] Step 5:

[1090] Plan Delivery

[1091] Server: Sends the generated plan to the user's device.

[1092] Input: Customized plan data.

[1093] Output: Send the plan to the device.

[1094] Step 6:

[1095] View Plans

[1096] Device: Displays the received exercise and nutrition plans to the user.

[1097] Input: Plan data sent by the server.

[1098] Output: Plan information displayed to the user.

[1099] Step 7:

[1100] Movement and Emotion Monitoring

[1101] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[1102] Input: Real-time user movement and emotion data.

[1103] Output: Camera and microphone capture data.

[1104] Step 8:

[1105] Emotional Data Analysis

[1106] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[1107] Input: Camera and microphone capture data.

[1108] Output: Analyzed emotion data.

[1109] Step 9:

[1110] Emotionally-driven plan adjustments

[1111] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[1112] Input: Analyzed emotion data.

[1113] Output: Tailored exercise and nutrition plans.

[1114] Step 10:

[1115] Providing Feedback

[1116] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[1117] Input: User's motion and emotion data.

[1118] Output: The generated feedback information.

[1119] Step 11:

[1120] View Feedback

[1121] Terminal: Display feedback to the user.

[1122] Input: Feedback data sent by the server.

[1123] Output: Feedback information that is displayed to the user.

[1124] Step 12:

[1125] Collecting self-assessments

[1126] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[1127] Input: User self-assessment data.

[1128] Output: Collected self-assessment data.

[1129] Step 13:

[1130] Submitting and integrating self-assessments

[1131] Device: Sends self-evaluation data to the server, which then combines the subjective evaluation, movement data, and data from the emotion engine to evaluate the user's progress.

[1132] Input: User self-evaluation data, movement data, and emotion data.

[1133] Output: Consolidated progress assessment data.

[1134] Step 14:

[1135] Update your plan based on progress

[1136] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[1137] Input: Consolidated progress assessment data.

[1138] Output: Updated exercise and nutrition plans.

[1139] Step 15:

[1140] View renewal plans

[1141] Device: Display next week's plan on the user's dashboard.

[1142] Input: The renewal plan sent by the server.

[1143] Output: Renewal plan information displayed to the user.

[1144] In this way, the system can provide detailed support for users' fitness activities and provide them with customized exercise and nutrition plans that take their emotions into account.

[1145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1147] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1148] [Third embodiment]

[1149] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1153] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1157] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1159] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1160] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1161] The present invention is a system that provides individually designed exercise and nutrition plans based on a user's fitness level, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. This section describes the specific program processing of the system in natural language.

[1162] Overall system overview

[1163] 1. Initial Setup

[1164] User: Opens the application and begins the initial setup. The user answers questions about their fitness level, goals, and preferences and enters the required data.

[1165] Terminal: Collects this information and sends it to the server.

[1166] Server: Creates a user profile based on the received data and stores it in a database.

[1167] 2. Generate exercise and nutrition plans

[1168] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans.

[1169] Server: Sends the generated plan to the user's device.

[1170] Device: Displaying exercise and nutrition plans to the user.

[1171] Specific examples

[1172] 1. Example of initial settings

[1173] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[1174] Terminal: Collects questions and answers as data, formats the user's responses, and sends the data to the server.

[1175] Server: Based on the received information, create a new user entry in the database and register the fitness level "Beginner," goal "Weight loss," and preference "Aerobic exercise."

[1176] 2. Examples of creating exercise and nutrition plans

[1177] Server: Runs AI algorithms based on the user's profile information to generate a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[1178] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[1179] On your device: Display plan details on a dashboard and allow users to track their daily progress.

[1180] Fitness Monitoring and Feedback

[1181] 1. User: Starts exercising according to a specified exercise plan. For example, starts walking.

[1182] Device: Collects user exercise data (e.g., steps, heart rate) in real time using built-in cameras and sensors (e.g., heart rate monitors on smartwatches).

[1183] Terminal: Sends collected data to the server.

[1184] 2. Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[1185] Server: Generates feedback as needed, for example, creating a message like "You're walking too slowly, please walk faster."

[1186] Device: Notify the user of the feedback.

[1187] 3. Progress evaluation and plan adjustment

[1188] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[1189] Device: Sends self-assessment data to the server.

[1190] Server: Integrates subjective assessment and exercise data to comprehensively evaluate the user's progress. Updates the next week's exercise and nutrition plans as needed.

[1191] Server: Sends the updated plan to the user's device.

[1192] Device: Display next week's plan on the user's dashboard.

[1193] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[1194] The processing flow will be explained below.

[1195] Step 1:

[1196] Initial Setup

[1197] User: Opens the application and taps the start button for initial setup.

[1198] Device: Prompts the user with questions about their fitness level, goals, and preferences, and asks them to enter their answers. For example, they might enter "Fitness level is beginner," "Goal is weight loss," and "Preferences are aerobic exercise."

[1199] Terminal: Formats the entered data and sends it to the server.

[1200] Server: Creates a user profile based on the received data and stores it in a database.

[1201] Step 2:

[1202] Generate exercise and nutrition plans

[1203] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[1204] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[1205] On the device: Display an exercise and nutrition plan to the user, for example, a balanced diet plan of 2000 kcal and 30 minutes of walking three times a week.

[1206] Step 3:

[1207] Fitness Monitoring

[1208] User: Starts exercising according to the specified exercise plan. For example, starts walking.

[1209] Device: Uses built-in cameras and sensors to collect real-time exercise data from users, such as capturing their form with a camera and measuring their heart rate with a heart rate monitor.

[1210] Terminal: Sends collected data to the server.

[1211] Step 4:

[1212] Feedback and Adjustments

[1213] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[1214] Server: Generates feedback on areas that need adjustment or improvement. For example, create a message like "Your pace is too slow, please walk a little faster."

[1215] Server: Sends feedback to the user's device.

[1216] Device: Notify the user of the feedback.

[1217] Step 5:

[1218] Progress evaluation and plan adjustment

[1219] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[1220] Terminal: Sends the entered self-evaluation data to the server.

[1221] Server: Integrates subjective evaluations and exercise data to comprehensively evaluate the user's progress, such as improving form or increasing exercise intensity.

[1222] Server: Adjust / update next week's exercise and nutrition plan as needed, for example, increase walking time to 40 minutes.

[1223] Server: Sends the updated plan to the user's device.

[1224] Device: Display next week's plan on the user's dashboard.

[1225] Through these steps, the system can effectively support users' fitness activities, encouraging them to maintain continuous motivation and achieve their goals.

[1226] Example 1

[1227] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1228] To meet today's individual fitness needs, many users are seeking individually designed exercise and nutrition plans. However, existing systems lack sufficient customization based on a user's physiological information, goals, and preferences, and lack continuous feedback and plan adjustments to achieve long-term goals. This makes it difficult for users to effectively achieve their goals, and providing a sustainable fitness plan is a challenge.

[1229] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1230] In this invention, the server includes means for generating a personalized exercise plan and nutrition plan based on the user's physiological information, goals, and preferences, means for providing the generated exercise plan and nutrition plan to the user, means for collecting data using a sensor or imaging device to monitor the user's exercise, means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and nutrition plan as needed, means for adjusting the exercise plan and nutrition plan using a generative AI model, and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals, thereby enabling customization of the exercise and nutrition plan to meet the user's individual needs, continuous monitoring and feedback, and adjustment of the plan according to progress.

[1231] "Physiological information" is data related to the user's health condition and physical characteristics, and examples include height, weight, heart rate, blood pressure, etc.

[1232] A "goal" is a goal that a user wishes to achieve through a fitness activity, examples of which include weight loss, muscle gain, and endurance improvement.

[1233] "Preferences" are the user's preferred types of fitness and eating style, examples of which include cardio, strength training, plant-based eating, etc.

[1234] An "exercise plan" is a specific exercise schedule or menu provided to the user, and examples include 30 minutes of walking three times a week and strength training twice a week.

[1235] A "nutritional plan" is a specific dietary instruction or meal plan provided to a user, and examples include a calorie-restricted diet, a protein-enriched diet, etc.

[1236] A "sensor" is a device that collects a user's exercise data in real time, and examples include smartwatches and fitness trackers.

[1237] "Photography device" refers to a camera or video device used to monitor the user's posture and exercise form.

[1238] "Means for analyzing data" refers to algorithms or software used to analyze collected data and assess the user's progress or condition.

[1239] "Feedback" refers to suggestions for improvement or advice provided to users based on the analysis results, such as "Please speed up your pace" or "Please add this exercise next time."

[1240] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate and adjust a user's plan.

[1241] A "human interface" is an interface through which a user interacts with a system, and examples include smartphone applications and web browsers.

[1242] A "database" is an information storage system for managing user profiles and history data stored on a server.

[1243] "Long-term progress" refers to the results or changes in a user's fitness activities over a period of time.

[1244] The above definitions allow a clear understanding of each element in the present invention.

[1245] The present invention provides a system that provides individually designed exercise and nutrition plans based on a user's physiological information, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. The operation of the system is described in detail below.

[1246] Initial Setup

[1247] 1. User: Launches the application on their smartphone or computer and begins the first-time setup. The user answers questions about their fitness level, goals, and preferences and enters them into the application.

[1248] 2. Terminal: Collects data entered by the user and sends it to the server.

[1249] 3. Server: Creates a user profile based on the received data and stores it in a database, registering the user's fitness level, goals, preferences, etc.

[1250] Generate exercise and nutrition plans

[1251] 1. Server: Runs AI algorithms based on the stored user profile information to generate customized exercise and nutrition plans. The AI ​​models used can be deep learning or machine learning algorithms.

[1252] 2. Server: Sends the generated plan to the user's device.

[1253] 3. Terminal: The received exercise plan and nutrition plan are displayed on the user interface and provided to the user.

[1254] Fitness Monitoring and Feedback

[1255] 1. User: Starts exercising according to a specified exercise plan, for example, walking.

[1256] 2. Device: Utilizing built-in sensors in smartwatches and smartphones, users' exercise data (e.g., steps, heart rate) is collected in real time. Examples of sensors include heart rate monitors and pedometers.

[1257] 3. Terminal: Sends the collected exercise data to the server.

[1258] 4. Server: Analyzes the received data and evaluates the user's progress and exercise form using statistical methods and machine learning techniques.

[1259] 5. Server: Generates feedback as needed, for example, "Your heart rate is dropping. Please pick up the pace a bit."

[1260] 6. Terminal: Providing feedback to the user interface.

[1261] Progress evaluation and plan adjustment

[1262] 1. User: Enter your thoughts and self-evaluation of your exercise regularly. For example, you could do a self-evaluation every weekend.

[1263] 2. Terminal: Sends self-assessment data to the server.

[1264] 3. Server: Integrates subjective and objective exercise data to comprehensively evaluate the user's progress and update the next week's exercise and nutrition plans as needed.

[1265] 4. Server: Sends the updated plan to the user's device.

[1266] 5. Terminal: Display next week's plan on the user interface.

[1267] In a concrete example of this system, if a user selects "beginner level," "weight loss," and "aerobic exercise," the server generates an exercise plan that includes 30 minutes of walking three times a week and a nutrition plan that includes a calorie-restricted diet.The system also uses a smartwatch to monitor heart rate and step count, providing real-time feedback to adjust the pace as needed.

[1268] Prompt Sentence Examples

[1269] "What are your fitness goals? (e.g., weight loss, strength gain, endurance improvement)"

[1270] "What type of exercise do you prefer? (e.g., cardio, strength training, yoga)"

[1271] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[1272] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1273] Step 1:

[1274] User: Launches the application and begins the initial setup. The user answers questions and inputs their fitness level, goals, and preferences. Examples of inputs include "Beginner," "Weight loss," and "Prefers cardio."

[1275] Input: Fitness level, goals, and preferences

[1276] Output: Initial setting data

[1277] Step 2:

[1278] Terminal: Collects data entered by the user and formats it, for example, into JSON format data, and then sends it to the server.

[1279] Input: Initialization data

[1280] Output: Formatted data

[1281] Step 3:

[1282] Server: Receives the formatted data and stores it in a database. Creates a new user profile with fitness level "Beginner", goal "Weight loss", and preference "Aerobic exercise".

[1283] Input: Formatted data

[1284] Output: User profile stored in the database

[1285] Step 4:

[1286] Server: Runs AI algorithms based on the user profile. Specifically, it uses deep learning algorithms to generate exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to the user's needs.

[1287] Input: Database user profile

[1288] Output: Customized exercise and nutrition plans

[1289] Step 5:

[1290] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[1291] Enter: a customized exercise and nutrition plan.

[1292] Output: The plan sent to the user's device

[1293] Step 6:

[1294] Device: Displays the received exercise and nutrition plans on a user interface. A screen appears providing the user with specific exercise and dietary instructions.

[1295] Input: The plan sent to the user's device

[1296] Output: Exercise and nutrition plans displayed in a user interface

[1297] Step 7:

[1298] User: Follows the specified exercise plan and starts exercising. For example, starts walking.

[1299] Input: Exercise plan displayed in the user interface

[1300] Output: The exercise performed

[1301] Step 8:

[1302] Device: Using built-in sensors in smartwatches and smartphones (e.g., heart rate monitors, pedometers), we collect data on your exercise in real time.

[1303] Input: Exercise to be performed

[1304] Output: Collected exercise data

[1305] Step 9:

[1306] Device: Sends collected exercise data to the server.

[1307] Input: Collected exercise data

[1308] Output: Exercise data sent to the server

[1309] Step 10:

[1310] Server: Analyzes the received exercise data. It uses statistical methods and machine learning algorithms to evaluate the user's progress and exercise form. For example, it checks whether the heart rate is within the appropriate range.

[1311] Input: Exercise data sent to the server

[1312] Output: Analysis results

[1313] Step 11:

[1314] Server: Generates feedback based on the analysis results, such as a message like "Your heart rate is dropping. Please pick up the pace a bit."

[1315] Input: Analysis results

[1316] Output: Feedback message

[1317] Step 12:

[1318] Server: Sends feedback messages to the user's device and notifies them through the user interface.

[1319] Input: Feedback message

[1320] Output: Feedback message sent to the user's device

[1321] Step 13:

[1322] User: Periodically (e.g., at the end of the week) enter a self-evaluation, which includes thoughts on exercise and achievement.

[1323] Input: Feedback message

[1324] Output: Self-assessment

[1325] Step 14:

[1326] Terminal: Collects self-assessment data and sends it to the server.

[1327] Input: Self-assessment data

[1328] Output: Self-assessment data sent to the server

[1329] Step 15:

[1330] Server: Combines subjective assessments and objective exercise data to provide a comprehensive assessment of the user's progress, and determines whether the user's exercise and nutrition plans for the next week need to be updated.

[1331] Input: Subjective assessment data and exercise data

[1332] Output: Exercise and nutrition plans that need updating

[1333] Step 16:

[1334] Server: Sends the updated plan to the user's device.

[1335] Input: Exercise and nutrition plans that need updating

[1336] Output: Renewal plan sent to user's device

[1337] Step 17:

[1338] Terminal: Display the updated plan in the user interface and provide the user with instructions for the next week.

[1339] Input: The renewal plan sent to the user's device

[1340] Output: Update plan displayed in the user interface

[1341] (Application example 1)

[1342] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1343] While existing fitness systems offer personalized exercise and nutritional plans, they often lack the ability to analyze users' movements in real time and provide interactive feedback. Furthermore, they lack the means to effectively enhance the user experience through specific wearable displays, making it difficult to achieve long-term fitness goals.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1345] In this invention, the server includes means for analyzing the user's movements in real time and providing interactive feedback using the wearable display, means for formatting the collected data and transmitting it to the server, and means for periodically evaluating the user's progress and updating the plan for long-term goal achievement, thereby enabling continuous adjustment of the plan for long-term goal achievement while monitoring the user's movements in real time and providing appropriate feedback.

[1346] "User's fitness level" is an index that indicates the user's current physical ability and health condition.

[1347] "Goal" refers to the outcome or goal that a user wishes to achieve through their fitness activity.

[1348] "Preferences" refers to the types of exercise and eating habits that a user prefers.

[1349] An "exercise plan" is a specific exercise schedule designed based on a user's fitness level, goals, and preferences.

[1350] A "nutrition plan" is a meal plan designed to suit a user's fitness goals and health status.

[1351] "Sensor or camera" refers to a device used to monitor a user's activity and collect data.

[1352] "Feedback" is advice or guidance provided to users based on collected data.

[1353] A "wearable display device" is a display device that is worn by the user, such as a head-mounted display.

[1354] "Interactive feedback" is feedback that is provided immediately in response to a user's real-time actions.

[1355] "Means for formatting collected data and sending it to the server" refers to the process for converting data collected from users into an appropriate format and sending it to the server.

[1356] "Means to regularly evaluate your progress and update your plan to meet your long-term goals" refers to the process of regularly evaluating your fitness progress and adjusting and updating your exercise and nutrition plans as needed.

[1357] The "server" is a central management system that processes and stores data, generates feedback, updates exercise and nutrition plans, etc.

[1358] The present invention is a system that provides users with individually designed exercise and nutrition plans based on their fitness level, goals, and preferences, allowing them to enjoy a customized fitness experience at home and efficiently reach their goals based on ongoing fitness monitoring and feedback.

[1359] First, the user puts on a head-mounted display (HMD) and performs the initial setup. The HMD is equipped with an interface, and the user answers questions about their fitness level, goals, and preferences through voice and visual input, inputting the necessary data. The device collects this information and sends it to a server. The server creates a user profile based on the received data and stores it in a database.

[1360] The server then runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan, which is then sent to the user's HMD, where it is displayed to the user. The user can then begin their fitness activities according to the displayed exercise and nutrition plan.

[1361] During fitness activities, the HMD's built-in cameras and sensors collect real-time data on the user's movements, heart rate, and other data. The device then sends this data to a server, which analyzes it. Based on the results of this analysis, the device evaluates whether the user's movements are appropriate, whether their heart rate is within the target zone, and other factors, and generates appropriate feedback. The generated feedback is displayed on the user's HMD.

[1362] For example, while a user is walking, the HMD monitors the user's posture, number of steps, and heart rate, and if the user's stride is small, it provides real-time feedback such as "Take longer steps." In this way, the user can exercise efficiently with proper form.

[1363] Furthermore, the server periodically evaluates the user's progress and updates the exercise and nutrition plan to achieve long-term goals. The server also collects the user's self-evaluation data (subjective feedback), and combines this data with the exercise data to adjust the next plan. This process helps the user to continuously progress toward their goals.

[1364] Below are some example prompts to input to a generative AI model:

[1365] "Please provide example code to build an algorithm that captures video of a user walking at a heart rate of 72 and then evaluates their form and provides feedback."

[1366] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[1367] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1368] Step 1:

[1369] The user puts on a head-mounted display (HMD) and launches the application. On the initial setup screen, the user answers questions about their fitness level, goals, and preferences and enters the required data. This input data is sent to the device, formatted, and transferred to the server. The input data includes the user's fitness level, goals, and preferences. The server receives this data, creates a user profile, and stores it in a database.

[1370] Step 2:

[1371] The server runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan. The server then performs data calculations based on input data (fitness level, goals, and preferences) to create the optimal plan. The generated plan is sent from the server to the user's HMD and displayed to the user, allowing the user to review the exercise and nutrition plan.

[1372] Step 3:

[1373] The user begins fitness activities based on the displayed exercise and nutrition plans. The HMD's built-in cameras and sensors collect data on the user's movements and heart rate in real time. This collected data includes the user's posture, number of steps, and heart rate. The device then transmits the collected data to a server.

[1374] Step 4:

[1375] The server analyzes the received data. Here, the server processes the data and evaluates whether the user's movement and heart rate are within the target zone. Based on this evaluation, the server generates appropriate feedback. The generated feedback is sent from the server to the user's HMD and displayed to the user. For example, if the user's stride is small, the server will display feedback such as "Please take longer steps."

[1376] Step 5:

[1377] The server periodically evaluates the user's progress. The user inputs their thoughts and self-evaluation of their exercise, which is then sent to the server. The server then combines the self-evaluation data with the exercise data to evaluate long-term progress. Based on this progress evaluation, the server adjusts the user's exercise and nutrition plans and updates the next plan. The updated plan is sent to the user's HMD and displayed.

[1378] Step 6:

[1379] This process is repeated until the user achieves their goal. Through regularly updated plans and feedback, users can efficiently progress towards their long-term fitness goals.

[1380] Through these steps, the present invention can effectively support users' fitness activities, providing real-time feedback and continuous adjustments toward achieving long-term goals. For example, a possible input prompt for a generative AI model could be: "Please provide sample code to build an algorithm that captures video of a user walking at a heart rate of 72 and evaluates their form and provides feedback."

[1381] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1382] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions, enabling more personalized and effective support for the user's fitness experience. This section describes the specific program processing of the system in natural language.

[1383] Overall system overview

[1384] 1. Initial Setup

[1385] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[1386] Terminal: Collects this information and sends it to the server.

[1387] Server: Creates a user profile based on the received data and stores it in a database.

[1388] 2. Generate exercise and nutrition plans

[1389] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[1390] Server: Sends the generated plan to the user's device.

[1391] Device: Displaying exercise and nutrition plans to the user.

[1392] 3. Emotional awareness and regulation

[1393] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1394] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[1395] Specific examples

[1396] 1. Example of initial settings

[1397] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[1398] Terminal: Collects response data and sends it to the server.

[1399] Server: Creates a new user profile based on the data and saves it in the database. The user's fitness level is "Beginner," their goal is "Weight loss," and their preference is "Aerobic exercise."

[1400] 2. Examples of creating exercise and nutrition plans

[1401] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[1402] Server: Sends the generated plan to the user's device.

[1403] Device: View plan details on your dashboard.

[1404] 3. Examples of Emotion Recognition and Regulation

[1405] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[1406] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[1407] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[1408] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[1409] Fitness Monitoring and Feedback

[1410] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[1411] Device: Collects exercise data using built-in cameras and sensors and sends it to a server. Examples include steps taken, heart rate, and form checks.

[1412] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[1413] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[1414] 2. Progress evaluation and plan adjustment

[1415] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[1416] Device: Sends self-assessment data to the server.

[1417] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[1418] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[1419] Device: Display next week's plan on the user's dashboard.

[1420] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[1421] The processing flow will be explained below.

[1422] Step 1:

[1423] Initial Setup

[1424] User: Launches the application and follows the on-screen prompts to begin initial setup. Answers questions about fitness level, goals, and preferences. For example, enter "Fitness level is beginner," "Goal is weight loss," and "Preference is cardio."

[1425] Terminal: Collects input data, formats it and sends it to the server.

[1426] Server: Creates a user profile based on the received data and stores it in a database.

[1427] Step 2:

[1428] Generate exercise and nutrition plans

[1429] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans, such as designing a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[1430] Server: Sends the generated plan to the user's device.

[1431] On device: Display plan details to the user, for example, daily exercise tasks and meal plans on the app dashboard.

[1432] Step 3:

[1433] Emotional awareness and regulation

[1434] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, and send them to the emotion engine in real time.

[1435] Server: Uses an emotion engine to analyze the user's emotions from the received data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is tired or stressed.

[1436] Server: Adjusts exercise and nutrition plans accordingly based on perceived emotions, for example suggesting yoga instead of walking if the user is feeling stressed.

[1437] Server: Sends the adjusted plan to the device and notifies the user.

[1438] Step 4:

[1439] Fitness Monitoring

[1440] User: Start exercising according to the specified exercise plan and follow the instructions on the device, for example, start walking.

[1441] Device: Uses built-in sensors and cameras to collect exercise data (e.g., steps, heart rate, form) in real time.

[1442] Terminal: Sends collected data to the server.

[1443] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking form accuracy and heart rate range.

[1444] Server: Generates and sends feedback to the device as needed, such as advice like "walk faster" or "check your form."

[1445] Step 5:

[1446] Progress evaluation and plan adjustment

[1447] User: Periodically (e.g., on weekends) enter their impressions and self-evaluation of their exercise. For example, they may write comments such as "exercise is getting easier" or "I feel less tired."

[1448] Terminal: Sends the entered self-evaluation data to the server.

[1449] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to comprehensively evaluate the user's progress, for example, by reviewing the intensity and frequency of exercise and adjusting the plan as needed.

[1450] Server: Updates the next week's exercise and nutrition plans and sends them to the device.

[1451] Device: Display the updated plan on the user's dashboard and guide them through the next steps.

[1452] Through these steps, the system can effectively support users' fitness activities, provide customized exercise and nutrition plans that take users' emotions into account, and provide real-time monitoring, feedback, and progress evaluation.

[1453] Example 2

[1454] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1455] Conventional fitness systems struggle to provide exercise and meal plans that fully consider a user's individual fitness level, goals, and preferences. Furthermore, they focus solely on the user's exercise, failing to adequately provide feedback or adjust plans that reflect the user's emotional state or subjective evaluation. This makes it difficult to maintain user motivation and effectively monitor progress.

[1456] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1457] In this invention, the server includes: means for generating a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and meal plan to the user; means for collecting data using an optical sensor or a voice input device to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and meal plan as needed; means for analyzing the user's emotions and adjusting the exercise plan and meal plan based on the collected data; and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals. This allows the server to provide an exercise plan and nutrition plan tailored to the user's individual needs and adjust the plan based on real-time feedback and emotional state, thereby maintaining the user's motivation and enabling more effective progress management.

[1458] "User's fitness level" is an index that indicates the user's current athletic ability and experience level, and is classified as beginner, intermediate, advanced, etc.

[1459] "Goal" refers to a specific fitness or health objective that a user wishes to achieve, such as losing weight, gaining strength, or improving endurance.

[1460] "Preferences" refers to the type of exercise and diet preferred by the user, including likes and dislikes of aerobic exercise and preferences for specific foods.

[1461] "Exercise plan" refers to a customized exercise schedule and content based on a user's fitness level, goals, and preferences.

[1462] "Meal Plan" refers to a meal schedule or composition designed to meet a user's fitness goals.

[1463] "Optical sensor" refers to a device that uses light to detect physical actions or conditions, including cameras.

[1464] "Audio input device" refers to hardware for collecting audio data, including a microphone.

[1465] "Feedback" refers to performance-based information provided to a user, such as progress and form evaluation.

[1466] An "emotion recognition engine" refers to software or hardware that analyzes a user's facial expressions, voice, etc. to recognize their emotional state.

[1467] "Progress assessment" refers to the process of analyzing a user's exercise data and self-assessment to assess their progress toward their fitness goals.

[1468] "Plan update" refers to the act of reviewing and adjusting the exercise and meal plans based on the user's progress and emotional state.

[1469] The present invention is a system that provides a user with a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences. The system includes a means for monitoring the user's exercise, a means for analyzing collected data and providing feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[1470] Overall system overview

[1471] Initial Setup

[1472] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences. For example, enter "Beginner level," "Weight loss," or "Prefers cardio."

[1473] Device: This information is collected and sent to the server using the submitProfileData() method, and the data is sent to the server in JSON format.

[1474] Server: Create a user profile based on the received data and save it in the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[1475] Generate exercise and nutrition plans

[1476] Server: Runs AI algorithms (e.g., TensorFlow) based on the user's profile information to generate customized exercise and meal plans.

[1477] Server: Sends the generated plan to the user's device using structured data (e.g., JSON).

[1478] On the device: Display the exercise and meal plans to the user. Example: Use the displayPlans() method to reflect the received data in UI components.

[1479] Emotional awareness and regulation

[1480] Device: Use the built-in camera (e.g., Logitech C920) or microphone (e.g., Samson Go Mic) to capture the user's facial expressions and voice, and analyze them with the emotion engine (e.g., Affectiva SDK). Example: Use the captureEmotionData() method.

[1481] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[1482] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[1483] Fitness Monitoring and Feedback

[1484] Collecting user exercise data

[1485] User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[1486] Device: Collect exercise data using the built-in camera or sensor (e.g., Apple Watch sensor) and send it to the server. Example: Use the collectExerciseData() method to send the collected data to the server in real time.

[1487] Server: Analyzes data and evaluates user progress and form. For example, ensures form is correct and heart rate is within an appropriate range. Analyzes data using SQL queries and a secure data processing pipeline.

[1488] Providing feedback and adjusting plans

[1489] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[1490] Terminal: Show feedback to the user and move on to the next step.

[1491] Progress assessment and plan updates

[1492] Entering user self-assessment

[1493] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[1494] Device: Sends self-assessment data to the server.

[1495] Progress assessment and plan updates

[1496] Server: Integrates subjective assessments, exercise data, and data from an emotion recognition engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[1497] Server: Updates next week's exercise and meal plans and sends them to your device.

[1498] On the device: Display next week's plan on the user's dashboard and notify the user.

[1499] This system can provide personalized support for users' fitness activities, allowing them to more effectively monitor their progress, and can also adjust the system to take their emotional state into account to increase their motivation and help them achieve their goals.

[1500] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1501] Step 1: Enter user data

[1502] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[1503] Input: Fitness level (e.g., "Beginner"), goal (e.g., "Weight loss"), preference (e.g., "Cardio").

[1504] Output: User's answers to the questions.

[1505] Specific behavior: The data entered by the user is temporarily stored within the application and prepared for sending to the server.

[1506] Step 2: Send data to the server

[1507] Terminal: Collects input user data, converts it into JSON format, and sends it to the server.

[1508] Input: User response data.

[1509] Output: Data in JSON format.

[1510] Specific operation: Calls the submitProfileData() method and sends a POST request to the server's API endpoint with the user's input data.

[1511] Step 3: Create a profile

[1512] Server: Analyzes the received data, creates a user profile, and stores the data in a database.

[1513] Input: User data in JSON format.

[1514] Output: User profile, database entry.

[1515] What it does: Parses the received data using the receiveUserData() method, executes a SQL query, and saves it to the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[1516] Step 4: Create an exercise and meal plan

[1517] Server: Runs AI algorithms (e.g., TensorFlow) based on the user's profile to generate customized exercise and meal plans.

[1518] Input: User profile data.

[1519] Output: A customized exercise and meal plan.

[1520] Specific behavior: Using the TensorFlow model, execute the model.predict(user_profile_data) method and reprocess the generated plan to save it in the database.

[1521] Step 5: Submit your plan data

[1522] Server: Converts the generated plan data into JSON format and sends it to the user's device.

[1523] Enter: a customized exercise and meal plan.

[1524] Output: Plan data in JSON format.

[1525] Specific operation: The generated plan is converted into JSON and sent to the user's device via an HTTP request.

[1526] Step 6: View your plan

[1527] Terminal: Parses the received plan data and displays it to the user.

[1528] Input: Plan data in JSON format.

[1529] Output: The exercise and meal plans displayed to the user.

[1530] Specific behavior: Calls the displayPlans() method and displays the received data as UI components.

[1531] Step 7: Capturing Emotional Data

[1532] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[1533] Input: User's facial expressions and voice.

[1534] Output: Emotion data.

[1535] What it does: Use the captureEmotionData() method to collect image and audio data and pass it to an emotion recognition engine in real time (e.g., Affectiva SDK).

[1536] Step 8: Emotional Data Analysis and Feedback

[1537] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine.

[1538] Input: Emotion data.

[1539] Output: Tailored exercise and meal plans.

[1540] Specific operation: Receives the analysis results of the emotion engine, uses them to dynamically adjust exercise and meal plans, and sends them back to the device.

[1541] Step 9: Collect exercise data

[1542] User: Starts exercising according to the specified exercise plan.

[1543] Device: Collects exercise data using the built-in camera and sensors and sends it to a server.

[1544] Input: User exercise data (e.g. steps, heart rate, form check).

[1545] Output: The raw data sent to the server.

[1546] Specific operation: Use the collectExerciseData() method to send collected data to the server in real time.

[1547] Step 10: Analyze data and provide feedback

[1548] Server: Analyzes the received data and evaluates the user's progress and exercise form.

[1549] Input: Raw data (e.g. steps, heart rate, forms).

[1550] Output: The feedback message.

[1551] What it does: Evaluates data using SQL queries and data processing algorithms, and generates and sends feedback to the device, such as "pace yourself a little faster" or "add some stretching next time."

[1552] Step 11: Enter your self-assessment

[1553] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[1554] Input: Subjective evaluation data.

[1555] Output: Self-assessment data sent to the server.

[1556] Specific behavior: The self-evaluation form is filled out within the application and the data is sent to the server using the submitSelfEvaluation() method.

[1557] Step 12: Evaluate your progress and update your plan

[1558] Server: Integrates subjective assessment, motion data, and data from the emotion engine to evaluate the user's progress.

[1559] Input: Subjective evaluation data, motion data, and emotion data.

[1560] Output: Updated exercise and meal plans.

[1561] Specific operation: Executes a comprehensive evaluation algorithm using multiple data sources to generate a new plan.

[1562] Step 13: Notification and display of new plans

[1563] Server: Updates the next week's exercise and meal plans and sends them to the device.

[1564] Enter: your updated exercise and meal plans.

[1565] Output: Plan data in JSON format.

[1566] Specific operation: Convert the updated plan into JSON format and send it to the user's device.

[1567] Device: Display next week's plan on the dashboard and notify the user.

[1568] Specific behavior: Calls the displayUpdatedPlans() method to display the new plans in the user's UI.

[1569] (Application example 2)

[1570] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1571] Conventional fitness systems have limited means of providing personalized exercise and nutrition plans, and are unable to adjust plans to take into account the user's emotions and stress levels. They also struggle to accurately assess a user's progress and appropriately update plans to achieve long-term goals. As a result, users struggle to maintain their motivation and achieve their goals.

[1572] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1573] In this invention, the server includes: means for generating an exercise plan and nutrition plan individually designed based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and nutrition plan to the user; means for collecting data using a sensor or camera to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, recognizing the user's emotions, and adjusting the exercise plan and nutrition plan as needed; means for analyzing the user's emotions using an emotion recognition engine and suggesting appropriate relaxation exercises and meals; and means for periodically evaluating the user's progress and updating the plan to help the user achieve their long-term goals. This makes it possible to provide an individual exercise plan and nutrition plan that takes the user's emotions into consideration.

[1574] "User's fitness level" is an index that indicates the user's level of exercise experience and physical strength.

[1575] A "goal" is a specific purpose or result that a user wants to achieve through fitness activities.

[1576] "Preferences" are personal preferences such as the type, content, and format of exercise and nutrition preferred by the user.

[1577] An "exercise plan" is a plan of exercise content and schedule designed to suit the user.

[1578] A "nutrition plan" is a meal plan and schedule that is appropriate for a user's health and fitness goals.

[1579] The "generating means" refers to the technology or algorithm that creates the exercise and nutrition plans based on the user's input data.

[1580] The "means for providing" refers to a technology or interface for displaying or notifying the user of the generated exercise plan and nutrition plan.

[1581] "Sensors or cameras for monitoring" are measuring devices or photographing devices for collecting information on the user's physical activity in real time.

[1582] "Means for analyzing collected data and providing feedback" refers to technologies and systems that analyze exercise data collected by sensors and cameras and inform users of areas for improvement and evaluation results.

[1583] "Emotion recognition and regulation" refers to technologies and algorithms that detect a user's emotional state and use that information to appropriately modify their exercise and nutrition plans.

[1584] An "emotion recognition engine" is software or AI technology that analyzes emotions from a user's facial expressions, voice, etc.

[1585] "Means for assessing progress and updating the plan" refers to technology or algorithms that periodically evaluate a user's exercise performance and redesign an optimal plan to meet their fitness goals.

[1586] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing the collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[1587] Overall system overview

[1588] 1. Initial Setup

[1589] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[1590] Terminal: Collects this information and sends it to the server.

[1591] Server: Creates a user profile based on the received data and stores it in a database.

[1592] 2. Generate exercise and nutrition plans

[1593] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans, using AI models such as TensorFlow.

[1594] Server: Sends the generated plan to the user's device.

[1595] Device: Displaying exercise and nutrition plans to the user.

[1596] 3. Emotional awareness and regulation

[1597] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, such as the Microsoft Azure Face API.

[1598] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[1599] Specific examples

[1600] 1. Example of initial settings

[1601] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[1602] Terminal: Collects response data and sends it to the server.

[1603] Server: Create user profiles based on the data and store them in a database, for example, using Amazon RDS.

[1604] 2. Examples of creating exercise and nutrition plans

[1605] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[1606] Server: Sends the generated plan to the user's device.

[1607] Device: View plan details on your dashboard.

[1608] 3. Examples of Emotion Recognition and Regulation

[1609] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[1610] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[1611] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[1612] Fitness Monitoring and Feedback

[1613] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[1614] Device: Collects exercise data using built-in cameras and sensors and sends it to a server, such as steps, heart rate, and form checks.

[1615] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[1616] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[1617] 2. Progress evaluation and plan adjustment

[1618] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[1619] Device: Sends self-assessment data to the server.

[1620] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[1621] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[1622] Device: Display next week's plan on the user's dashboard.

[1623] Prompt Sentence Examples

[1624] Example prompt for a generative AI model:

[1625] "Generate a beginner's fitness plan. My goal is weight loss and I prefer cardio."

[1626] "If the user is feeling stressed, suggest a relaxing exercise."

[1627] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[1628] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1629] Step 1:

[1630] Initial Setup

[1631] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[1632] Input: User's fitness level, goals, and preferences.

[1633] Output: Constructed user profile data.

[1634] Step 2:

[1635] Collection and transmission of user data

[1636] Terminal: Collects user response information and sends it to the cloud server.

[1637] Input: User input data.

[1638] Output: Data sent from the device to the server.

[1639] Step 3:

[1640] Creating a User Profile

[1641] Server: Generates a user profile based on the received data and stores it in a database.

[1642] Input: User response data received from the device.

[1643] Output: User profile data.

[1644] Step 4:

[1645] Generate exercise and nutrition plans

[1646] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[1647] Input: User profile data.

[1648] Output: A customized exercise and nutrition plan.

[1649] Step 5:

[1650] Plan Delivery

[1651] Server: Sends the generated plan to the user's device.

[1652] Input: Customized plan data.

[1653] Output: Send the plan to the device.

[1654] Step 6:

[1655] View Plans

[1656] Device: Displays the received exercise and nutrition plans to the user.

[1657] Input: Plan data sent by the server.

[1658] Output: Plan information displayed to the user.

[1659] Step 7:

[1660] Movement and Emotion Monitoring

[1661] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[1662] Input: Real-time user movement and emotion data.

[1663] Output: Camera and microphone capture data.

[1664] Step 8:

[1665] Emotional Data Analysis

[1666] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[1667] Input: Camera and microphone capture data.

[1668] Output: Analyzed emotion data.

[1669] Step 9:

[1670] Emotionally-driven plan adjustments

[1671] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[1672] Input: Analyzed emotion data.

[1673] Output: Tailored exercise and nutrition plans.

[1674] Step 10:

[1675] Providing Feedback

[1676] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[1677] Input: User's motion and emotion data.

[1678] Output: The generated feedback information.

[1679] Step 11:

[1680] View Feedback

[1681] Terminal: Display feedback to the user.

[1682] Input: Feedback data sent by the server.

[1683] Output: Feedback information that is displayed to the user.

[1684] Step 12:

[1685] Collecting self-assessments

[1686] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[1687] Input: User self-assessment data.

[1688] Output: Collected self-assessment data.

[1689] Step 13:

[1690] Submitting and integrating self-assessments

[1691] Device: Sends self-evaluation data to the server, which then combines the subjective evaluation, movement data, and data from the emotion engine to evaluate the user's progress.

[1692] Input: User self-evaluation data, movement data, and emotion data.

[1693] Output: Consolidated progress assessment data.

[1694] Step 14:

[1695] Update your plan based on progress

[1696] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[1697] Input: Consolidated progress assessment data.

[1698] Output: Updated exercise and nutrition plans.

[1699] Step 15:

[1700] View renewal plans

[1701] Device: Display next week's plan on the user's dashboard.

[1702] Input: The renewal plan sent by the server.

[1703] Output: Renewal plan information displayed to the user.

[1704] In this way, the system can provide detailed support for users' fitness activities and provide them with customized exercise and nutrition plans that take their emotions into account.

[1705] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1706] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1707] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1708] [Fourth embodiment]

[1709] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1710] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1711] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1712] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1713] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1714] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1715] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1716] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1717] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1718] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1719] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1720] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1721] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1722] The present invention is a system that provides individually designed exercise and nutrition plans based on a user's fitness level, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. This section describes the specific program processing of the system in natural language.

[1723] Overall system overview

[1724] 1. Initial Setup

[1725] User: Opens the application and begins the initial setup. The user answers questions about their fitness level, goals, and preferences and enters the required data.

[1726] Terminal: Collects this information and sends it to the server.

[1727] Server: Creates a user profile based on the received data and stores it in a database.

[1728] 2. Generate exercise and nutrition plans

[1729] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans.

[1730] Server: Sends the generated plan to the user's device.

[1731] Device: Displaying exercise and nutrition plans to the user.

[1732] Specific examples

[1733] 1. Example of initial settings

[1734] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[1735] Terminal: Collects questions and answers as data, formats the user's responses, and sends the data to the server.

[1736] Server: Based on the received information, create a new user entry in the database and register the fitness level "Beginner," goal "Weight loss," and preference "Aerobic exercise."

[1737] 2. Examples of creating exercise and nutrition plans

[1738] Server: Runs AI algorithms based on the user's profile information to generate a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[1739] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[1740] On your device: Display plan details on a dashboard and allow users to track their daily progress.

[1741] Fitness Monitoring and Feedback

[1742] 1. User: Starts exercising according to a specified exercise plan. For example, starts walking.

[1743] Device: Collects user exercise data (e.g., steps, heart rate) in real time using built-in cameras and sensors (e.g., heart rate monitors on smartwatches).

[1744] Terminal: Sends collected data to the server.

[1745] 2. Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[1746] Server: Generates feedback as needed, for example, creating a message like "You're walking too slowly, please walk faster."

[1747] Device: Notify the user of the feedback.

[1748] 3. Progress evaluation and plan adjustment

[1749] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[1750] Device: Sends self-assessment data to the server.

[1751] Server: Integrates subjective assessment and exercise data to comprehensively evaluate the user's progress. Updates the next week's exercise and nutrition plans as needed.

[1752] Server: Sends the updated plan to the user's device.

[1753] Device: Display next week's plan on the user's dashboard.

[1754] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[1755] The processing flow will be explained below.

[1756] Step 1:

[1757] Initial Setup

[1758] User: Opens the application and taps the start button for initial setup.

[1759] Device: Prompts the user with questions about their fitness level, goals, and preferences, and asks them to enter their answers. For example, they might enter "Fitness level is beginner," "Goal is weight loss," and "Preferences are aerobic exercise."

[1760] Terminal: Formats the entered data and sends it to the server.

[1761] Server: Creates a user profile based on the received data and stores it in a database.

[1762] Step 2:

[1763] Generate exercise and nutrition plans

[1764] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[1765] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[1766] On the device: Display an exercise and nutrition plan to the user, for example, a balanced diet plan of 2000 kcal and 30 minutes of walking three times a week.

[1767] Step 3:

[1768] Fitness Monitoring

[1769] User: Starts exercising according to the specified exercise plan. For example, starts walking.

[1770] Device: Uses built-in cameras and sensors to collect real-time exercise data from users, such as capturing their form with a camera and measuring their heart rate with a heart rate monitor.

[1771] Terminal: Sends collected data to the server.

[1772] Step 4:

[1773] Feedback and Adjustments

[1774] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking whether the user is exercising in the appropriate heart rate zone.

[1775] Server: Generates feedback on areas that need adjustment or improvement. For example, create a message like "Your pace is too slow, please walk a little faster."

[1776] Server: Sends feedback to the user's device.

[1777] Device: Notify the user of the feedback.

[1778] Step 5:

[1779] Progress evaluation and plan adjustment

[1780] User: Periodically (e.g., on weekends), enter your thoughts and self-evaluation of your exercise.

[1781] Terminal: Sends the entered self-evaluation data to the server.

[1782] Server: Integrates subjective evaluations and exercise data to comprehensively evaluate the user's progress, such as improving form or increasing exercise intensity.

[1783] Server: Adjust / update next week's exercise and nutrition plan as needed, for example, increase walking time to 40 minutes.

[1784] Server: Sends the updated plan to the user's device.

[1785] Device: Display next week's plan on the user's dashboard.

[1786] Through these steps, the system can effectively support users' fitness activities, encouraging them to maintain continuous motivation and achieve their goals.

[1787] Example 1

[1788] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1789] To meet today's individual fitness needs, many users are seeking individually designed exercise and nutrition plans. However, existing systems lack sufficient customization based on a user's physiological information, goals, and preferences, and lack continuous feedback and plan adjustments to achieve long-term goals. This makes it difficult for users to effectively achieve their goals, and providing a sustainable fitness plan is a challenge.

[1790] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1791] In this invention, the server includes means for generating a personalized exercise plan and nutrition plan based on the user's physiological information, goals, and preferences, means for providing the generated exercise plan and nutrition plan to the user, means for collecting data using a sensor or imaging device to monitor the user's exercise, means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and nutrition plan as needed, means for adjusting the exercise plan and nutrition plan using a generative AI model, and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals, thereby enabling customization of the exercise and nutrition plan to meet the user's individual needs, continuous monitoring and feedback, and adjustment of the plan according to progress.

[1792] "Physiological information" is data related to the user's health condition and physical characteristics, and examples include height, weight, heart rate, blood pressure, etc.

[1793] A "goal" is a goal that a user wishes to achieve through a fitness activity, examples of which include weight loss, muscle gain, and endurance improvement.

[1794] "Preferences" are the user's preferred types of fitness and eating style, examples of which include cardio, strength training, plant-based eating, etc.

[1795] An "exercise plan" is a specific exercise schedule or menu provided to the user, and examples include 30 minutes of walking three times a week and strength training twice a week.

[1796] A "nutritional plan" is a specific dietary instruction or meal plan provided to a user, and examples include a calorie-restricted diet, a protein-enriched diet, etc.

[1797] A "sensor" is a device that collects a user's exercise data in real time, and examples include smartwatches and fitness trackers.

[1798] "Photography device" refers to a camera or video device used to monitor the user's posture and exercise form.

[1799] "Means for analyzing data" refers to algorithms or software used to analyze collected data and assess the user's progress or condition.

[1800] "Feedback" refers to suggestions for improvement or advice provided to users based on the analysis results, such as "Please speed up your pace" or "Please add this exercise next time."

[1801] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate and adjust a user's plan.

[1802] A "human interface" is an interface through which a user interacts with a system, and examples include smartphone applications and web browsers.

[1803] A "database" is an information storage system for managing user profiles and history data stored on a server.

[1804] "Long-term progress" refers to the results or changes in a user's fitness activities over a period of time.

[1805] The above definitions allow a clear understanding of each element in the present invention.

[1806] The present invention provides a system that provides individually designed exercise and nutrition plans based on a user's physiological information, goals, and preferences. The system allows users to enjoy a customized fitness experience at home and efficiently achieve their goals based on ongoing fitness monitoring and feedback. The operation of the system is described in detail below.

[1807] Initial Setup

[1808] 1. User: Launches the application on their smartphone or computer and begins the first-time setup. The user answers questions about their fitness level, goals, and preferences and enters them into the application.

[1809] 2. Terminal: Collects data entered by the user and sends it to the server.

[1810] 3. Server: Creates a user profile based on the received data and stores it in a database, registering the user's fitness level, goals, preferences, etc.

[1811] Generate exercise and nutrition plans

[1812] 1. Server: Runs AI algorithms based on the stored user profile information to generate customized exercise and nutrition plans. The AI ​​models used can be deep learning or machine learning algorithms.

[1813] 2. Server: Sends the generated plan to the user's device.

[1814] 3. Terminal: The received exercise plan and nutrition plan are displayed on the user interface and provided to the user.

[1815] Fitness Monitoring and Feedback

[1816] 1. User: Starts exercising according to a specified exercise plan, for example, walking.

[1817] 2. Device: Utilizing built-in sensors in smartwatches and smartphones, users' exercise data (e.g., steps, heart rate) is collected in real time. Examples of sensors include heart rate monitors and pedometers.

[1818] 3. Terminal: Sends the collected exercise data to the server.

[1819] 4. Server: Analyzes the received data and evaluates the user's progress and exercise form using statistical methods and machine learning techniques.

[1820] 5. Server: Generates feedback as needed, for example, "Your heart rate is dropping. Please pick up the pace a bit."

[1821] 6. Terminal: Providing feedback to the user interface.

[1822] Progress evaluation and plan adjustment

[1823] 1. User: Enter your thoughts and self-evaluation of your exercise regularly. For example, you could do a self-evaluation every weekend.

[1824] 2. Terminal: Sends self-assessment data to the server.

[1825] 3. Server: Integrates subjective and objective exercise data to comprehensively evaluate the user's progress and update the next week's exercise and nutrition plans as needed.

[1826] 4. Server: Sends the updated plan to the user's device.

[1827] 5. Terminal: Display next week's plan on the user interface.

[1828] In a concrete example of this system, if a user selects "beginner level," "weight loss," and "aerobic exercise," the server generates an exercise plan that includes 30 minutes of walking three times a week and a nutrition plan that includes a calorie-restricted diet.The system also uses a smartwatch to monitor heart rate and step count, providing real-time feedback to adjust the pace as needed.

[1829] Prompt Sentence Examples

[1830] "What are your fitness goals? (e.g., weight loss, strength gain, endurance improvement)"

[1831] "What type of exercise do you prefer? (e.g., cardio, strength training, yoga)"

[1832] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[1833] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1834] Step 1:

[1835] User: Launches the application and begins the initial setup. The user answers questions and inputs their fitness level, goals, and preferences. Examples of inputs include "Beginner," "Weight loss," and "Prefers cardio."

[1836] Input: Fitness level, goals, and preferences

[1837] Output: Initial setting data

[1838] Step 2:

[1839] Terminal: Collects data entered by the user and formats it, for example, into JSON format data, and then sends it to the server.

[1840] Input: Initialization data

[1841] Output: Formatted data

[1842] Step 3:

[1843] Server: Receives the formatted data and stores it in a database. Creates a new user profile with fitness level "Beginner", goal "Weight loss", and preference "Aerobic exercise".

[1844] Input: Formatted data

[1845] Output: User profile stored in the database

[1846] Step 4:

[1847] Server: Runs AI algorithms based on the user profile. Specifically, it uses deep learning algorithms to generate exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to the user's needs.

[1848] Input: Database user profile

[1849] Output: Customized exercise and nutrition plans

[1850] Step 5:

[1851] Server: Sends the generated exercise plan and nutrition plan to the user's device.

[1852] Enter: a customized exercise and nutrition plan.

[1853] Output: The plan sent to the user's device

[1854] Step 6:

[1855] Device: Displays the received exercise and nutrition plans on a user interface. A screen appears providing the user with specific exercise and dietary instructions.

[1856] Input: The plan sent to the user's device

[1857] Output: Exercise and nutrition plans displayed in a user interface

[1858] Step 7:

[1859] User: Follows the specified exercise plan and starts exercising. For example, starts walking.

[1860] Input: Exercise plan displayed in the user interface

[1861] Output: The exercise performed

[1862] Step 8:

[1863] Device: Using built-in sensors in smartwatches and smartphones (e.g., heart rate monitors, pedometers), we collect data on your exercise in real time.

[1864] Input: Exercise to be performed

[1865] Output: Collected exercise data

[1866] Step 9:

[1867] Device: Sends collected exercise data to the server.

[1868] Input: Collected exercise data

[1869] Output: Exercise data sent to the server

[1870] Step 10:

[1871] Server: Analyzes the received exercise data. It uses statistical methods and machine learning algorithms to evaluate the user's progress and exercise form. For example, it checks whether the heart rate is within the appropriate range.

[1872] Input: Exercise data sent to the server

[1873] Output: Analysis results

[1874] Step 11:

[1875] Server: Generates feedback based on the analysis results, such as a message like "Your heart rate is dropping. Please pick up the pace a bit."

[1876] Input: Analysis results

[1877] Output: Feedback message

[1878] Step 12:

[1879] Server: Sends feedback messages to the user's device and notifies them through the user interface.

[1880] Input: Feedback message

[1881] Output: Feedback message sent to the user's device

[1882] Step 13:

[1883] User: Periodically (e.g., at the end of the week) enter a self-evaluation, which includes thoughts on exercise and achievement.

[1884] Input: Feedback message

[1885] Output: Self-assessment

[1886] Step 14:

[1887] Terminal: Collects self-assessment data and sends it to the server.

[1888] Input: Self-assessment data

[1889] Output: Self-assessment data sent to the server

[1890] Step 15:

[1891] Server: Combines subjective assessments and objective exercise data to provide a comprehensive assessment of the user's progress, and determines whether the user's exercise and nutrition plans for the next week need to be updated.

[1892] Input: Subjective assessment data and exercise data

[1893] Output: Exercise and nutrition plans that need updating

[1894] Step 16:

[1895] Server: Sends the updated plan to the user's device.

[1896] Input: Exercise and nutrition plans that need updating

[1897] Output: Renewal plan sent to user's device

[1898] Step 17:

[1899] Terminal: Display the updated plan in the user interface and provide the user with instructions for the next week.

[1900] Input: The renewal plan sent to the user's device

[1901] Output: Update plan displayed in the user interface

[1902] (Application example 1)

[1903] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1904] While existing fitness systems offer personalized exercise and nutritional plans, they often lack the ability to analyze users' movements in real time and provide interactive feedback. Furthermore, they lack the means to effectively enhance the user experience through specific wearable displays, making it difficult to achieve long-term fitness goals.

[1905] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1906] In this invention, the server includes means for analyzing the user's movements in real time and providing interactive feedback using the wearable display, means for formatting the collected data and transmitting it to the server, and means for periodically evaluating the user's progress and updating the plan for long-term goal achievement, thereby enabling continuous adjustment of the plan for long-term goal achievement while monitoring the user's movements in real time and providing appropriate feedback.

[1907] "User's fitness level" is an index that indicates the user's current physical ability and health condition.

[1908] "Goal" refers to the outcome or goal that a user wishes to achieve through their fitness activity.

[1909] "Preferences" refers to the types of exercise and eating habits that a user prefers.

[1910] An "exercise plan" is a specific exercise schedule designed based on a user's fitness level, goals, and preferences.

[1911] A "nutrition plan" is a meal plan designed to suit a user's fitness goals and health status.

[1912] "Sensor or camera" refers to a device used to monitor a user's activity and collect data.

[1913] "Feedback" is advice or guidance provided to users based on collected data.

[1914] A "wearable display device" is a display device that is worn by the user, such as a head-mounted display.

[1915] "Interactive feedback" is feedback that is provided immediately in response to a user's real-time actions.

[1916] "Means for formatting collected data and sending it to the server" refers to the process for converting data collected from users into an appropriate format and sending it to the server.

[1917] "Means to regularly evaluate your progress and update your plan to meet your long-term goals" refers to the process of regularly evaluating your fitness progress and adjusting and updating your exercise and nutrition plans as needed.

[1918] The "server" is a central management system that processes and stores data, generates feedback, updates exercise and nutrition plans, etc.

[1919] The present invention is a system that provides users with individually designed exercise and nutrition plans based on their fitness level, goals, and preferences, allowing them to enjoy a customized fitness experience at home and efficiently reach their goals based on ongoing fitness monitoring and feedback.

[1920] First, the user puts on a head-mounted display (HMD) and performs the initial setup. The HMD is equipped with an interface, and the user answers questions about their fitness level, goals, and preferences through voice and visual input, inputting the necessary data. The device collects this information and sends it to a server. The server creates a user profile based on the received data and stores it in a database.

[1921] The server then runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan, which is then sent to the user's HMD, where it is displayed to the user. The user can then begin their fitness activities according to the displayed exercise and nutrition plan.

[1922] During fitness activities, the HMD's built-in cameras and sensors collect real-time data on the user's movements, heart rate, and other data. The device then sends this data to a server, which analyzes it. Based on the results of this analysis, the device evaluates whether the user's movements are appropriate, whether their heart rate is within the target zone, and other factors, and generates appropriate feedback. The generated feedback is displayed on the user's HMD.

[1923] For example, while a user is walking, the HMD monitors the user's posture, number of steps, and heart rate, and if the user's stride is small, it provides real-time feedback such as "Take longer steps." In this way, the user can exercise efficiently with proper form.

[1924] Furthermore, the server periodically evaluates the user's progress and updates the exercise and nutrition plan to achieve long-term goals. The server also collects the user's self-evaluation data (subjective feedback), and combines this data with the exercise data to adjust the next plan. This process helps the user to continuously progress toward their goals.

[1925] Below are some example prompts to input to a generative AI model:

[1926] "Please provide example code to build an algorithm that captures video of a user walking at a heart rate of 72 and then evaluates their form and provides feedback."

[1927] In this way, the system effectively supports users in their fitness activities and helps them achieve their goals by providing individually customized exercise and nutrition plans, real-time monitoring, feedback, and progress evaluation.

[1928] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1929] Step 1:

[1930] The user puts on a head-mounted display (HMD) and launches the application. On the initial setup screen, the user answers questions about their fitness level, goals, and preferences and enters the required data. This input data is sent to the device, formatted, and transferred to the server. The input data includes the user's fitness level, goals, and preferences. The server receives this data, creates a user profile, and stores it in a database.

[1931] Step 2:

[1932] The server runs an AI algorithm based on the user's profile to generate a customized exercise and nutrition plan. The server then performs data calculations based on input data (fitness level, goals, and preferences) to create the optimal plan. The generated plan is sent from the server to the user's HMD and displayed to the user, allowing the user to review the exercise and nutrition plan.

[1933] Step 3:

[1934] The user begins fitness activities based on the displayed exercise and nutrition plans. The HMD's built-in cameras and sensors collect data on the user's movements and heart rate in real time. This collected data includes the user's posture, number of steps, and heart rate. The device then transmits the collected data to a server.

[1935] Step 4:

[1936] The server analyzes the received data. Here, the server processes the data and evaluates whether the user's movement and heart rate are within the target zone. Based on this evaluation, the server generates appropriate feedback. The generated feedback is sent from the server to the user's HMD and displayed to the user. For example, if the user's stride is small, the server will display feedback such as "Please take longer steps."

[1937] Step 5:

[1938] The server periodically evaluates the user's progress. The user inputs their thoughts and self-evaluation of their exercise, which is then sent to the server. The server then combines the self-evaluation data with the exercise data to evaluate long-term progress. Based on this progress evaluation, the server adjusts the user's exercise and nutrition plans and updates the next plan. The updated plan is sent to the user's HMD and displayed.

[1939] Step 6:

[1940] This process is repeated until the user achieves their goal. Through regularly updated plans and feedback, users can efficiently progress towards their long-term fitness goals.

[1941] Through these steps, the present invention can effectively support users' fitness activities, providing real-time feedback and continuous adjustments toward achieving long-term goals. For example, a possible input prompt for a generative AI model could be: "Please provide sample code to build an algorithm that captures video of a user walking at a heart rate of 72 and evaluates their form and provides feedback."

[1942] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1943] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions, enabling more personalized and effective support for the user's fitness experience. This section describes the specific program processing of the system in natural language.

[1944] Overall system overview

[1945] 1. Initial Setup

[1946] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[1947] Terminal: Collects this information and sends it to the server.

[1948] Server: Creates a user profile based on the received data and stores it in a database.

[1949] 2. Generate exercise and nutrition plans

[1950] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[1951] Server: Sends the generated plan to the user's device.

[1952] Device: Displaying exercise and nutrition plans to the user.

[1953] 3. Emotional awareness and regulation

[1954] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1955] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[1956] Specific examples

[1957] 1. Example of initial settings

[1958] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[1959] Terminal: Collects response data and sends it to the server.

[1960] Server: Creates a new user profile based on the data and saves it in the database. The user's fitness level is "Beginner," their goal is "Weight loss," and their preference is "Aerobic exercise."

[1961] 2. Examples of creating exercise and nutrition plans

[1962] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[1963] Server: Sends the generated plan to the user's device.

[1964] Device: View plan details on your dashboard.

[1965] 3. Examples of Emotion Recognition and Regulation

[1966] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[1967] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[1968] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[1969] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[1970] Fitness Monitoring and Feedback

[1971] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[1972] Device: Collects exercise data using built-in cameras and sensors and sends it to a server. Examples include steps taken, heart rate, and form checks.

[1973] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[1974] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[1975] 2. Progress evaluation and plan adjustment

[1976] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[1977] Device: Sends self-assessment data to the server.

[1978] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[1979] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[1980] Device: Display next week's plan on the user's dashboard.

[1981] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[1982] The processing flow will be explained below.

[1983] Step 1:

[1984] Initial Setup

[1985] User: Launches the application and follows the on-screen prompts to begin initial setup. Answers questions about fitness level, goals, and preferences. For example, enter "Fitness level is beginner," "Goal is weight loss," and "Preference is cardio."

[1986] Terminal: Collects input data, formats it and sends it to the server.

[1987] Server: Creates a user profile based on the received data and stores it in a database.

[1988] Step 2:

[1989] Generate exercise and nutrition plans

[1990] Server: Runs AI algorithms based on the user's profile to generate customized exercise and nutrition plans, such as designing a beginner's aerobic exercise plan (e.g., 30 minutes of walking three times a week) and a nutrition plan (e.g., a calorie-restricted diet) that matches the user's weight loss goals.

[1991] Server: Sends the generated plan to the user's device.

[1992] On device: Display plan details to the user, for example, daily exercise tasks and meal plans on the app dashboard.

[1993] Step 3:

[1994] Emotional awareness and regulation

[1995] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, and send them to the emotion engine in real time.

[1996] Server: Uses an emotion engine to analyze the user's emotions from the received data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is tired or stressed.

[1997] Server: Adjusts exercise and nutrition plans accordingly based on perceived emotions, for example suggesting yoga instead of walking if the user is feeling stressed.

[1998] Server: Sends the adjusted plan to the device and notifies the user.

[1999] Step 4:

[2000] Fitness Monitoring

[2001] User: Start exercising according to the specified exercise plan and follow the instructions on the device, for example, start walking.

[2002] Device: Uses built-in sensors and cameras to collect exercise data (e.g., steps, heart rate, form) in real time.

[2003] Terminal: Sends collected data to the server.

[2004] Server: Analyzes the received data and evaluates the user's progress and exercise form, for example, checking form accuracy and heart rate range.

[2005] Server: Generates and sends feedback to the device as needed, such as advice like "walk faster" or "check your form."

[2006] Step 5:

[2007] Progress evaluation and plan adjustment

[2008] User: Periodically (e.g., on weekends) enter their impressions and self-evaluation of their exercise. For example, they may write comments such as "exercise is getting easier" or "I feel less tired."

[2009] Terminal: Sends the entered self-evaluation data to the server.

[2010] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to comprehensively evaluate the user's progress, for example, by reviewing the intensity and frequency of exercise and adjusting the plan as needed.

[2011] Server: Updates the next week's exercise and nutrition plans and sends them to the device.

[2012] Device: Display the updated plan on the user's dashboard and guide them through the next steps.

[2013] Through these steps, the system can effectively support users' fitness activities, provide customized exercise and nutrition plans that take users' emotions into account, and provide real-time monitoring, feedback, and progress evaluation.

[2014] Example 2

[2015] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2016] Conventional fitness systems struggle to provide exercise and meal plans that fully consider a user's individual fitness level, goals, and preferences. Furthermore, they focus solely on the user's exercise, failing to adequately provide feedback or adjust plans that reflect the user's emotional state or subjective evaluation. This makes it difficult to maintain user motivation and effectively monitor progress.

[2017] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2018] In this invention, the server includes: means for generating a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and meal plan to the user; means for collecting data using an optical sensor or a voice input device to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, and adjusting the exercise plan and meal plan as needed; means for analyzing the user's emotions and adjusting the exercise plan and meal plan based on the collected data; and means for periodically evaluating the user's progress and updating the plan to achieve long-term goals. This allows the server to provide an exercise plan and nutrition plan tailored to the user's individual needs and adjust the plan based on real-time feedback and emotional state, thereby maintaining the user's motivation and enabling more effective progress management.

[2019] "User's fitness level" is an index that indicates the user's current athletic ability and experience level, and is classified as beginner, intermediate, advanced, etc.

[2020] "Goal" refers to a specific fitness or health objective that a user wishes to achieve, such as losing weight, gaining strength, or improving endurance.

[2021] "Preferences" refers to the type of exercise and diet preferred by the user, including likes and dislikes of aerobic exercise and preferences for specific foods.

[2022] "Exercise plan" refers to a customized exercise schedule and content based on a user's fitness level, goals, and preferences.

[2023] "Meal Plan" refers to a meal schedule or composition designed to meet a user's fitness goals.

[2024] "Optical sensor" refers to a device that uses light to detect physical actions or conditions, including cameras.

[2025] "Audio input device" refers to hardware for collecting audio data, including a microphone.

[2026] "Feedback" refers to performance-based information provided to a user, such as progress and form evaluation.

[2027] An "emotion recognition engine" refers to software or hardware that analyzes a user's facial expressions, voice, etc. to recognize their emotional state.

[2028] "Progress assessment" refers to the process of analyzing a user's exercise data and self-assessment to assess their progress toward their fitness goals.

[2029] "Plan update" refers to the act of reviewing and adjusting the exercise and meal plans based on the user's progress and emotional state.

[2030] The present invention is a system that provides a user with a personalized exercise plan and meal plan based on the user's fitness level, goals, and preferences. The system includes a means for monitoring the user's exercise, a means for analyzing collected data and providing feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[2031] Overall system overview

[2032] Initial Setup

[2033] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences. For example, enter "Beginner level," "Weight loss," or "Prefers cardio."

[2034] Device: This information is collected and sent to the server using the submitProfileData() method, and the data is sent to the server in JSON format.

[2035] Server: Create a user profile based on the received data and save it in the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[2036] Generate exercise and nutrition plans

[2037] Server: Runs AI algorithms (e.g., TensorFlow) based on the user's profile information to generate customized exercise and meal plans.

[2038] Server: Sends the generated plan to the user's device using structured data (e.g., JSON).

[2039] On the device: Display the exercise and meal plans to the user. Example: Use the displayPlans() method to reflect the received data in UI components.

[2040] Emotional awareness and regulation

[2041] Device: Use the built-in camera (e.g., Logitech C920) or microphone (e.g., Samson Go Mic) to capture the user's facial expressions and voice, and analyze them with the emotion engine (e.g., Affectiva SDK). Example: Use the captureEmotionData() method.

[2042] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[2043] On the device: Show the user the adjusted plan and feedback so they can take next steps.

[2044] Fitness Monitoring and Feedback

[2045] Collecting user exercise data

[2046] User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[2047] Device: Collect exercise data using the built-in camera or sensor (e.g., Apple Watch sensor) and send it to the server. Example: Use the collectExerciseData() method to send the collected data to the server in real time.

[2048] Server: Analyzes data and evaluates user progress and form. For example, ensures form is correct and heart rate is within an appropriate range. Analyzes data using SQL queries and a secure data processing pipeline.

[2049] Providing feedback and adjusting plans

[2050] Server: Generates and sends feedback to the device as needed. For example, "Pace yourself a little faster" or "Add some stretching next time."

[2051] Terminal: Show feedback to the user and move on to the next step.

[2052] Progress assessment and plan updates

[2053] Entering user self-assessment

[2054] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[2055] Device: Sends self-assessment data to the server.

[2056] Progress assessment and plan updates

[2057] Server: Integrates subjective assessments, exercise data, and data from an emotion recognition engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[2058] Server: Updates next week's exercise and meal plans and sends them to your device.

[2059] On the device: Display next week's plan on the user's dashboard and notify the user.

[2060] This system can provide personalized support for users' fitness activities, allowing them to more effectively monitor their progress, and can also adjust the system to take their emotional state into account to increase their motivation and help them achieve their goals.

[2061] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2062] Step 1: Enter user data

[2063] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[2064] Input: Fitness level (e.g., "Beginner"), goal (e.g., "Weight loss"), preference (e.g., "Cardio").

[2065] Output: User's answers to the questions.

[2066] Specific behavior: The data entered by the user is temporarily stored within the application and prepared for sending to the server.

[2067] Step 2: Send data to the server

[2068] Terminal: Collects input user data, converts it into JSON format, and sends it to the server.

[2069] Input: User response data.

[2070] Output: Data in JSON format.

[2071] Specific operation: Calls the submitProfileData() method and sends a POST request to the server's API endpoint with the user's input data.

[2072] Step 3: Create a profile

[2073] Server: Analyzes the received data, creates a user profile, and stores the data in a database.

[2074] Input: User data in JSON format.

[2075] Output: User profile, database entry.

[2076] What it does: Parses the received data using the receiveUserData() method, executes a SQL query, and saves it to the database. Example: INSERT INTO user_profiles (fitness_level, goal, preference) VALUES ('Beginner', 'Weight loss', 'Cardio');

[2077] Step 4: Create an exercise and meal plan

[2078] Server: Runs AI algorithms (e.g., TensorFlow) based on the user's profile to generate customized exercise and meal plans.

[2079] Input: User profile data.

[2080] Output: A customized exercise and meal plan.

[2081] Specific behavior: Using the TensorFlow model, execute the model.predict(user_profile_data) method and reprocess the generated plan to save it in the database.

[2082] Step 5: Submit your plan data

[2083] Server: Converts the generated plan data into JSON format and sends it to the user's device.

[2084] Enter: a customized exercise and meal plan.

[2085] Output: Plan data in JSON format.

[2086] Specific operation: The generated plan is converted into JSON and sent to the user's device via an HTTP request.

[2087] Step 6: View your plan

[2088] Terminal: Parses the received plan data and displays it to the user.

[2089] Input: Plan data in JSON format.

[2090] Output: The exercise and meal plans displayed to the user.

[2091] Specific behavior: Calls the displayPlans() method and displays the received data as UI components.

[2092] Step 7: Capturing Emotional Data

[2093] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[2094] Input: User's facial expressions and voice.

[2095] Output: Emotion data.

[2096] What it does: Use the captureEmotionData() method to collect image and audio data and pass it to an emotion recognition engine in real time (e.g., Affectiva SDK).

[2097] Step 8: Emotional Data Analysis and Feedback

[2098] Server: Adjusts exercise and meal plans based on the user's emotions recognized by the emotion engine.

[2099] Input: Emotion data.

[2100] Output: Tailored exercise and meal plans.

[2101] Specific operation: Receives the analysis results of the emotion engine, uses them to dynamically adjust exercise and meal plans, and sends them back to the device.

[2102] Step 9: Collect exercise data

[2103] User: Starts exercising according to the specified exercise plan.

[2104] Device: Collects exercise data using the built-in camera and sensors and sends it to a server.

[2105] Input: User exercise data (e.g. steps, heart rate, form check).

[2106] Output: The raw data sent to the server.

[2107] Specific operation: Use the collectExerciseData() method to send collected data to the server in real time.

[2108] Step 10: Analyze data and provide feedback

[2109] Server: Analyzes the received data and evaluates the user's progress and exercise form.

[2110] Input: Raw data (e.g. steps, heart rate, forms).

[2111] Output: The feedback message.

[2112] What it does: Evaluates data using SQL queries and data processing algorithms, and generates and sends feedback to the device, such as "pace yourself a little faster" or "add some stretching next time."

[2113] Step 11: Enter your self-assessment

[2114] User: Periodically enter self-evaluations. Examples: "Exercising is becoming easier" or "I feel less tired."

[2115] Input: Subjective evaluation data.

[2116] Output: Self-assessment data sent to the server.

[2117] Specific behavior: The self-evaluation form is filled out within the application and the data is sent to the server using the submitSelfEvaluation() method.

[2118] Step 12: Evaluate your progress and update your plan

[2119] Server: Integrates subjective assessment, motion data, and data from the emotion engine to evaluate the user's progress.

[2120] Input: Subjective evaluation data, motion data, and emotion data.

[2121] Output: Updated exercise and meal plans.

[2122] Specific operation: Executes a comprehensive evaluation algorithm using multiple data sources to generate a new plan.

[2123] Step 13: Notification and display of new plans

[2124] Server: Updates the next week's exercise and meal plans and sends them to the device.

[2125] Enter: your updated exercise and meal plans.

[2126] Output: Plan data in JSON format.

[2127] Specific operation: Convert the updated plan into JSON format and send it to the user's device.

[2128] Device: Display next week's plan on the dashboard and notify the user.

[2129] Specific behavior: Calls the displayUpdatedPlans() method to display the new plans in the user's UI.

[2130] (Application example 2)

[2131] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2132] Conventional fitness systems have limited means of providing personalized exercise and nutrition plans, and are unable to adjust plans to take into account the user's emotions and stress levels. They also struggle to accurately assess a user's progress and appropriately update plans to achieve long-term goals. As a result, users struggle to maintain their motivation and achieve their goals.

[2133] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2134] In this invention, the server includes: means for generating an exercise plan and nutrition plan individually designed based on the user's fitness level, goals, and preferences; means for providing the generated exercise plan and nutrition plan to the user; means for collecting data using a sensor or camera to monitor the user's exercise; means for analyzing the collected data, providing feedback to the user, recognizing the user's emotions, and adjusting the exercise plan and nutrition plan as needed; means for analyzing the user's emotions using an emotion recognition engine and suggesting appropriate relaxation exercises and meals; and means for periodically evaluating the user's progress and updating the plan to help the user achieve their long-term goals. This makes it possible to provide an individual exercise plan and nutrition plan that takes the user's emotions into consideration.

[2135] "User's fitness level" is an index that indicates the user's level of exercise experience and physical strength.

[2136] A "goal" is a specific purpose or result that a user wants to achieve through fitness activities.

[2137] "Preferences" are personal preferences such as the type, content, and format of exercise and nutrition preferred by the user.

[2138] An "exercise plan" is a plan of exercise content and schedule designed to suit the user.

[2139] A "nutrition plan" is a meal plan and schedule that is appropriate for a user's health and fitness goals.

[2140] The "generating means" refers to the technology or algorithm that creates the exercise and nutrition plans based on the user's input data.

[2141] The "means for providing" refers to a technology or interface for displaying or notifying the user of the generated exercise plan and nutrition plan.

[2142] "Sensors or cameras for monitoring" are measuring devices or photographing devices for collecting information on the user's physical activity in real time.

[2143] "Means for analyzing collected data and providing feedback" refers to technologies and systems that analyze exercise data collected by sensors and cameras and inform users of areas for improvement and evaluation results.

[2144] "Emotion recognition and regulation" refers to technologies and algorithms that detect a user's emotional state and use that information to appropriately modify their exercise and nutrition plans.

[2145] An "emotion recognition engine" is software or AI technology that analyzes emotions from a user's facial expressions, voice, etc.

[2146] "Means for assessing progress and updating the plan" refers to technology or algorithms that periodically evaluate a user's exercise performance and redesign an optimal plan to meet their fitness goals.

[2147] The present invention is a system that provides a user with a personalized exercise and nutrition plan based on their fitness level, goals, and preferences. The system combines a means for monitoring the user's exercise, a means for analyzing the collected data to provide feedback, a means for evaluating progress and updating the plan, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[2148] Overall system overview

[2149] 1. Initial Setup

[2150] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[2151] Terminal: Collects this information and sends it to the server.

[2152] Server: Creates a user profile based on the received data and stores it in a database.

[2153] 2. Generate exercise and nutrition plans

[2154] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans, using AI models such as TensorFlow.

[2155] Server: Sends the generated plan to the user's device.

[2156] Device: Displaying exercise and nutrition plans to the user.

[2157] 3. Emotional awareness and regulation

[2158] Device: The built-in camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by an emotion engine, such as the Microsoft Azure Face API.

[2159] Server: Adjusts exercise and nutrition plans based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, it recommends relaxing exercises and meals.

[2160] Specific examples

[2161] 1. Example of initial settings

[2162] User: Opens the application and selects "First Time Use." Answers the questions displayed on the screen and enters "Beginner level," "Weight loss," and "Prefers aerobic exercise."

[2163] Terminal: Collects response data and sends it to the server.

[2164] Server: Create user profiles based on the data and store them in a database, for example, using Amazon RDS.

[2165] 2. Examples of creating exercise and nutrition plans

[2166] Server: Uses AI algorithms to generate beginner-level cardio exercise plans (e.g., 30 minutes of walking three times a week) and nutrition plans (e.g., a calorie-restricted diet) tailored to weight loss goals.

[2167] Server: Sends the generated plan to the user's device.

[2168] Device: View plan details on your dashboard.

[2169] 3. Examples of Emotion Recognition and Regulation

[2170] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[2171] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[2172] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[2173] Fitness Monitoring and Feedback

[2174] 1. User: Start exercising according to the specified exercise plan and follow the instructions on the device.

[2175] Device: Collects exercise data using built-in cameras and sensors and sends it to a server, such as steps, heart rate, and form checks.

[2176] Server: Analyzes the data and evaluates the user's progress and exercise form. For example, checks whether their form is correct or whether their heart rate is within the appropriate range.

[2177] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[2178] 2. Progress evaluation and plan adjustment

[2179] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[2180] Device: Sends self-assessment data to the server.

[2181] Server: Integrates subjective assessment, exercise data, and data from the emotion engine to evaluate the user's progress. For example, if the emotion data indicates a high stress level, the plan is adjusted.

[2182] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[2183] Device: Display next week's plan on the user's dashboard.

[2184] Prompt Sentence Examples

[2185] Example prompt for a generative AI model:

[2186] "Generate a beginner's fitness plan. My goal is weight loss and I prefer cardio."

[2187] "If the user is feeling stressed, suggest a relaxing exercise."

[2188] In this way, the system effectively supports users' fitness activities and helps them achieve their goals by providing customized exercise and nutrition plans that take into account the user's emotions, as well as real-time monitoring, feedback, and progress evaluation.

[2189] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2190] Step 1:

[2191] Initial Setup

[2192] User: Opens the app and taps the initial start button. Answers questions about fitness level, goals, and preferences.

[2193] Input: User's fitness level, goals, and preferences.

[2194] Output: Constructed user profile data.

[2195] Step 2:

[2196] Collection and transmission of user data

[2197] Terminal: Collects user response information and sends it to the cloud server.

[2198] Input: User input data.

[2199] Output: Data sent from the device to the server.

[2200] Step 3:

[2201] Creating a User Profile

[2202] Server: Generates a user profile based on the received data and stores it in a database.

[2203] Input: User response data received from the device.

[2204] Output: User profile data.

[2205] Step 4:

[2206] Generate exercise and nutrition plans

[2207] Server: Runs AI algorithms based on the user's profile information to generate customized exercise and nutrition plans.

[2208] Input: User profile data.

[2209] Output: A customized exercise and nutrition plan.

[2210] Step 5:

[2211] Plan Delivery

[2212] Server: Sends the generated plan to the user's device.

[2213] Input: Customized plan data.

[2214] Output: Send the plan to the device.

[2215] Step 6:

[2216] View Plans

[2217] Device: Displays the received exercise and nutrition plans to the user.

[2218] Input: Plan data sent by the server.

[2219] Output: Plan information displayed to the user.

[2220] Step 7:

[2221] Movement and Emotion Monitoring

[2222] User: Initiates a fitness session at the device, using the camera and microphone to monitor movement and emotions.

[2223] Input: Real-time user movement and emotion data.

[2224] Output: Camera and microphone capture data.

[2225] Step 8:

[2226] Emotional Data Analysis

[2227] On the device: Uses facial recognition and voice analysis to detect if the user is tired or stressed.

[2228] Input: Camera and microphone capture data.

[2229] Output: Analyzed emotion data.

[2230] Step 9:

[2231] Emotionally-driven plan adjustments

[2232] Server: Receives the analysis results of the emotion engine and adjusts the exercise and nutrition plan as needed. For example, if the user feels tired, it suggests lowering the exercise intensity or providing relaxing exercises.

[2233] Input: Analyzed emotion data.

[2234] Output: Tailored exercise and nutrition plans.

[2235] Step 10:

[2236] Providing Feedback

[2237] Server: Generates and sends feedback to the device as needed, such as "pace yourself a little faster" or "add some stretches next time."

[2238] Input: User's motion and emotion data.

[2239] Output: The generated feedback information.

[2240] Step 11:

[2241] View Feedback

[2242] Terminal: Display feedback to the user.

[2243] Input: Feedback data sent by the server.

[2244] Output: Feedback information that is displayed to the user.

[2245] Step 12:

[2246] Collecting self-assessments

[2247] User: Periodically enter self-evaluations, such as "Exercising is becoming easier" or "I feel less fatigued."

[2248] Input: User self-assessment data.

[2249] Output: Collected self-assessment data.

[2250] Step 13:

[2251] Submitting and integrating self-assessments

[2252] Device: Sends self-evaluation data to the server, which then combines the subjective evaluation, movement data, and data from the emotion engine to evaluate the user's progress.

[2253] Input: User self-evaluation data, movement data, and emotion data.

[2254] Output: Consolidated progress assessment data.

[2255] Step 14:

[2256] Update your plan based on progress

[2257] Server: Updates next week's exercise and nutrition plans and sends them to your device.

[2258] Input: Consolidated progress assessment data.

[2259] Output: Updated exercise and nutrition plans.

[2260] Step 15:

[2261] View renewal plans

[2262] Device: Display next week's plan on the user's dashboard.

[2263] Input: The renewal plan sent by the server.

[2264] Output: Renewal plan information displayed to the user.

[2265] In this way, the system can provide detailed support for users' fitness activities and provide them with customized exercise and nutrition plans that take their emotions into account.

[2266] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2267] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2268] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2269] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2270] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2271] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2272] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2273] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2274] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2275] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2276] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2277] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2278] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2279] 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.

[2280] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2281] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2282] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2283] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2284] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2285] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of th...

Claims

1. means for generating a personalized exercise and nutrition plan based on the user's fitness level, goals, and preferences; means for providing the generated exercise and nutrition plans to a user; a means for collecting data using sensors or cameras to monitor the user's movements; means for analyzing the collected data and providing feedback to the user to adjust their exercise and nutrition plans as needed; and a means for periodically assessing the user's progress and updating the plan to achieve long-term goals.

2. 10. The system of claim 1, further comprising means for providing an interface for initializing the system based on user input.

3. 10. The system of claim 1, further comprising means for integrating user exercise data and subjective assessments to assess long-term progress.

Images