system

Abstract

We provide the system. [Solution] A calculation means for automatically generating an individualized exercise plan based on user input information, A display means that visually provides exercise guidance using holograms based on the generated exercise plan, A detection means that detects the user's body movements and analyzes the progress of the exercise in real time, An output means that provides real-time corrections to the user's exercise form based on the analysis results, A data processing means for learning the user's exercise history and optimizing the next exercise plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When many people try to exercise at home, there is a problem that there is a lack of appropriate planning and guidance, and the risk of injury due to self-styled exercise increases. Also, there is a problem that motivation to exercise continuously cannot be obtained and it is difficult to continue exercising. With conventional methods, it is difficult to provide flexible guidance and progress management according to the situation of individual users, and effective exercise support cannot be provided.

Means for Solving the Problems

[0005] This invention provides a calculation means for automatically generating an individualized exercise plan and a display means for visually guiding the exercise based on this plan, thereby enabling users to exercise safely with correct form. Furthermore, a detection means detects the user's physical movements and analyzes their progress in real time, determining the accuracy of the movements and providing necessary feedback to reduce the risk of injury. In addition, motivational information based on the user's goal achievement status is provided via an output means, supporting continued exercise and realizing an effective fitness experience.

[0006] "User" refers to an individual who uses this system to engage in fitness activities.

[0007] "Input information" refers to data that users provide to the system, such as age, fitness level, and fitness goals.

[0008] An "exercise plan" refers to a specific program of fitness activities automatically generated by AI based on the user's individual information.

[0009] "Computation means" refers to a device or algorithm that performs the process of creating an exercise plan based on user input information.

[0010] "Display means" refers to a device or method for providing visual exercise guidance to a user based on a generated exercise plan.

[0011] "Detection means" refers to sensors and software that acquire user movement data in real time and digitally recognize those movements.

[0012] "Analysis method" refers to an algorithm or process for evaluating user movements based on detected motion data and identifying areas for improvement.

[0013] "Output means" refers to a device or method for providing feedback or motivational information to the user based on the analyzed information.

[0014] "Feedback" refers to information that includes evaluations of the user's exercise and suggestions for improvement, and is intended to help users exercise safely and effectively.

[0015] "Motivational information" refers to information that provides psychological support and encouragement to help users continue exercising. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention provides a system that allows users to exercise safely and effectively at home. This system automatically generates an exercise plan tailored to the user's characteristics and provides visual guidance, making it adaptable to a wide range of users.

[0038] First, the user enters their basic information into the system using a terminal. This information is extensive and includes age, gender, current fitness level, past injury history, and specific fitness goals. The terminal sends this information to the server, which then creates an individual profile for the user.

[0039] The server generates an optimized exercise plan for the user based on the information it receives. An AI algorithm is involved in this process, determining the type of exercise, frequency, and intensity that matches the user's level and goals. For example, a beginner user might be offered short sessions that include low-intensity strength training.

[0040] The generated exercise plan is sent to the device, which then activates a hologram assistant based on it. The hologram assistant appears as a 3D image in front of the user, providing detailed instructions on how to perform the exercises. This makes it easier for the user to visually understand the correct form of the exercises.

[0041] The device also features sensors that detect user movements in real time. This allows it to collect data on the exercises the user is actually performing and send it to a server. The server analyzes this data to determine if the exercises are being performed correctly. If necessary, it provides feedback to the user via the device, instructing them on areas that need correction.

[0042] Furthermore, the server tracks the user's exercise progress and provides motivational information through the device. This information encourages users to continue exercising, gives them a sense of accomplishment, and helps them set their next goals. For example, a message such as, "You're making good progress today; let's try a little harder next time," might be displayed.

[0043] Thus, the system of the present invention provides an exercise experience tailored to each individual user, minimizing the risk of injury and supporting sustained exercise.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0047] Step 2:

[0048] The terminal sends the entered user information to the server to prepare data for creating the user's individual profile.

[0049] Step 3:

[0050] Based on the received user information, the server automatically generates an optimized exercise plan for the user using an AI algorithm. This plan includes the type of exercise, the number of repetitions, and the intensity level.

[0051] Step 4:

[0052] The server sends the generated motion plan to the terminal. The terminal receives this plan and prepares to activate the hologram assistant.

[0053] Step 5:

[0054] The device activates a hologram assistant, which displays a 3D image in front of the user. The hologram assistant visually demonstrates the correct form and procedure for the exercises to the user.

[0055] Step 6:

[0056] The device uses sensors to detect the user's movements in real time and collects movement data. This allows the system to understand how the user is exercising.

[0057] Step 7:

[0058] The device sends the collected motion data to the server. The server analyzes the data and determines whether the user's movements are correct.

[0059] Step 8:

[0060] The server generates feedback based on the results of the motion analysis, including advice for form correction and motivation as needed.

[0061] Step 9:

[0062] The device receives feedback from the server and provides it to the user. This allows the user to understand their exercise progress and make necessary adjustments immediately.

[0063] Step 10:

[0064] The server saves user progress information and incorporates it into future exercise plans. It also provides motivational information to help users evaluate their ability to continue exercising.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] Conventional exercise instruction systems have struggled to generate personalized exercise plans for each user and provide accurate, real-time feedback. Therefore, they have been unable to create an environment that allows users to continue exercising safely and effectively. Furthermore, they lacked effective means of motivating users to exercise.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes numerical analysis means for automatically generating an individualized exercise plan using a generation AI model based on user input information, stereoscopic image display means for visually providing exercise guidance based on the generated exercise plan, and motion detection means for detecting the user's movements and analyzing the progress of the exercise. This makes it possible to provide an exercise plan optimized for the user and to provide accurate feedback and motivational information in real time.

[0070] The "numerical analysis means" is a mechanism for automatically generating personalized exercise plans using an AI model based on user input information.

[0071] A "stereoscopic image display means" is a system that displays 3D images to visually provide exercise guidance to the user based on the generated exercise plan.

[0072] A "motion detection means" is a system that senses the user's physical movements in real time and analyzes the collected motion data.

[0073] An "information output method" is a method for providing users with analysis results and motivational information, and for conveying exercise feedback in real time.

[0074] This invention provides a system that allows users to receive personalized exercise guidance at home and exercise effectively and safely. The system primarily operates through the cooperation of a server and a terminal.

[0075] Users first enter their basic information using a terminal. This includes age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to a server and stored in a dedicated database.

[0076] The server uses the user's profile data to generate an optimal exercise plan through a generative AI model. The program uses an AI algorithm to determine the type, frequency, and intensity of exercise that matches each user's level and goals. An example of a prompt used in this process is, "Please create an exercise plan based on the user's profile."

[0077] The exercise plan generated on the server is sent to the terminal, which then activates a hologram assistant based on it. This hologram assistant appears before the user as a 3D image and provides detailed instructions on the exercise. This makes it easier for the user to visually learn the correct exercise form.

[0078] Furthermore, the device is equipped with sensors that detect user movements in real time. This allows data on the user's actual exercises to be transmitted to a server. The server analyzes this data and evaluates whether the movements are being performed correctly. If necessary, it provides feedback to the user through the device and instructs them to correct their exercise form.

[0079] In this way, the server and terminal work together to provide the user with an optimized exercise experience, ensuring user safety while supporting effective training.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users enter basic information using a terminal. This information includes a wide range of details such as age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to the server. The server receives this data and stores the user's individual profile in a database.

[0083] Step 2:

[0084] The server generates an exercise plan using a generative AI model based on the user's profile information. Based on the input information, the generative AI model calculates the optimal exercise type, frequency, and intensity using the prompt message "Create an exercise plan based on the user's profile." As output, a personalized exercise plan is generated.

[0085] Step 3:

[0086] The server sends the generated exercise plan to the terminal. Based on the received exercise plan, the terminal activates a hologram assistant. The hologram assistant uses 3D images to visually explain to the user detailed instructions on how to perform the exercises. This makes it easy for the user to understand the correct exercise form.

[0087] Step 4:

[0088] The user performs exercises following the guidance of a hologram assistant. The device detects the user's movements in real time through its built-in sensors. The input data from the sensors is temporarily processed by the device and sent to the server as exercise data.

[0089] Step 5:

[0090] The server analyzes the received exercise data and evaluates whether the exercise is being performed correctly. Using an AI algorithm, it determines accuracy and generates feedback if improvement is needed. This feedback is sent to the terminal in real time and provided to the user.

[0091] Step 6:

[0092] The user modifies their exercise form based on feedback from the server. The device also displays motivational information tailored to the user's progress toward their goals, supporting continued training. This information includes messages praising the user's efforts and encouraging them to set new goals.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In modern society, many individuals want to exercise efficiently and safely at home, but maintaining correct form and consistently training is difficult. Furthermore, they lack the feedback and motivation tailored to their individual fitness levels, resulting in insufficient solutions for their exercise needs. Therefore, it is necessary to provide a system that enables users to exercise appropriately by offering personalized exercise plans and providing visual, real-time exercise guidance and feedback.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance via holograms based on the generated exercise plan, and a detection means for detecting the user's physical movements and analyzing the progress of the exercise in real time. This enables the user to effectively and safely execute the exercise plan at home, maintain proper form while receiving real-time feedback, and train continuously.

[0098] The "calculation means" refers to a function that automatically generates an individualized exercise plan based on the user's input information.

[0099] A "display means" is a device that provides the generated exercise plan visually using a hologram and guides the user through exercise.

[0100] "Detection means" refers to a function that uses sensors to detect the user's physical movements and analyzes their progress in real time.

[0101] The "output mechanism" is a mechanism for providing users with real-time guidance on correcting their exercise form based on the analysis results.

[0102] A "data processing system" is a system that continuously learns the user's exercise history and executes a process to optimize the next exercise plan.

[0103] This invention provides a system that allows users to exercise safely and effectively at home. Specifically, a server uses an AI algorithm to automatically generate a personalized exercise plan from the user's input information. First, the user inputs information such as age, gender, fitness level, injury history, and fitness goals into a terminal. The terminal sends this information to the server, which then generates an exercise plan optimized for the user based on the received information.

[0104] The generated plan is sent to the terminal and presented to the user as a 3D hologram. This allows the user to visually understand the correct form of the exercise. The terminal is equipped with sensors to detect the user's body movements in real time. This data is sent to a server, where an AI algorithm analyzes the progress of the exercise. The server determines whether the exercise is being performed correctly and, if necessary, provides guidance to correct the exercise form to the user via the terminal.

[0105] Furthermore, the server stores the user's exercise history and processes the data to optimize the next exercise plan. The hardware used includes a physical assistant robot with holographic display capabilities and Microsoft's skeletal tracking sensors. The software utilizes AI algorithms such as TENSORFLOW® and PyTorch, and a real-time feedback system using OpenCV.

[0106] For example, if a user wants to start a beginner-level fitness program, the system suggests a short, low-intensity exercise session for first-time users. Movements are detected in real time, and specific feedback such as "straighten your back a little more" is provided through the device.

[0107] An example of a prompt for a generating AI model is: "I am a 47-year-old male with a history of knee surgery and an intermediate fitness level. Please suggest an exercise plan that will allow me to improve my fitness steadily and without overexertion." In this way, users can effectively achieve their fitness goals through a personalized exercise experience.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user enters basic information (age, gender, fitness level, injury history, fitness goals) using their device. This information is sent from the device to the server and prepared as data necessary for processing by the AI ​​algorithm. As a result, a user profile is generated.

[0111] Step 2:

[0112] The server executes an AI algorithm based on the user profile to automatically generate a personalized exercise plan. The type, frequency, and intensity of the exercise are determined based on the input information. The generated exercise plan is provided as output.

[0113] Step 3:

[0114] The device that receives the exercise plan activates a hologram display and presents the exercise method using 3D images in front of the user. The content is displayed visually based on the entered exercise plan.

[0115] Step 4:

[0116] Sensors built into the device detect the user's body movements in real time. Based on the input data from the sensors, the server analyzes the user's movement progress and evaluates the accuracy of their posture and movements. This analysis determines whether the user's movements are being performed appropriately.

[0117] Step 5:

[0118] Based on the analysis results, the server provides real-time feedback on how to correct or improve exercise form. This feedback is transmitted to the user via the terminal, and specific instructions such as "Raise your arms a little higher" are output.

[0119] Step 6:

[0120] The server learns the user's exercise history and processes the data to optimize the next exercise plan for better suitability. Here, past exercise data is used as input, and the generating AI model is ready to output an improved exercise plan for future training.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention adds emotion recognition functionality to a system that allows users to safely continue exercising at home, further personalizing and improving the traditional fitness experience. This system provides an automatically generated exercise plan based on the user's personal information, real-time feedback, and also recognizes the user's emotional state, reflecting that information in the exercise and feedback.

[0123] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device sends this information to the server to create a personalized profile for the user.

[0124] The server uses an AI algorithm to generate a personalized exercise plan based on user information. The generated plan is sent to the terminal, which then activates a hologram assistant. The hologram assistant demonstrates the correct form for each exercise and provides visual guidance.

[0125] This system also incorporates an emotion engine. The device detects the user's emotional state in real time through facial recognition technology and voice analysis. This emotional data is sent to a server and used to inform exercise plans and feedback.

[0126] For example, if the system detects that a user is experiencing stress, the emotion engine will temporarily reduce the intensity of the exercise and recommend relaxation-focused exercises. Furthermore, if a decrease in motivation is detected, the system will provide encouraging words and feedback tailored to the user's preferences through a hologram assistant.

[0127] The device detects the user's movements in real time and sends motion and emotional data collected by sensors to a server. The server analyzes this data and generates accurate form and emotionally responsive feedback. Emotion-based feedback personalizes the user's fitness experience and maximizes the effectiveness of their workout.

[0128] This system allows users to exercise according to their physical and psychological needs, enhancing fitness while reducing the risk of injury.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0132] Step 2:

[0133] The terminal sends the input information to the server and prepares the data for creating the user's individual profile.

[0134] Step 3:

[0135] The server uses AI algorithms to automatically generate personalized exercise plans based on the user's profile.

[0136] Step 4:

[0137] The server sends the generated motion plan to the terminal, and after the terminal receives the plan, it activates the hologram assistant.

[0138] Step 5:

[0139] The hologram assistant is displayed to the user, visually guiding them through the correct form and procedure of exercise.

[0140] Step 6:

[0141] The device uses facial recognition sensors and voice analysis functions to detect the user's emotional state in real time.

[0142] Step 7:

[0143] The device sends detected emotion data to the server, which then dynamically adjusts the exercise plan and feedback content based on this data.

[0144] Step 8:

[0145] The server generates and sends feedback that reflects emotional data to the terminal. The feedback includes content tailored to the user's emotional state.

[0146] Step 9:

[0147] The device provides users with emotionally responsive feedback through a holographic assistant, increasing their motivation to exercise.

[0148] Step 10:

[0149] User data regarding exercise and emotions is regularly saved to the server and used to plan future exercise sessions.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] Conventional fitness systems can adapt to some extent to a user's physical condition, but they often fail to adequately address the user's emotional state, resulting in uniform exercise plans and feedback. This has made it difficult for users to receive appropriate guidance and feedback that responds to changes in stress and motivation. Therefore, the present invention aims to provide a personalized fitness experience that takes the user's emotional state into account, thereby enhancing the effectiveness of exercise while maintaining motivation for continuous exercise.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes information processing means for automatically generating an individualized exercise plan based on user input information, display means for visually providing exercise guidance based on the generated exercise plan, detection means for detecting the user's exercise and analyzing the progress of the exercise, output means for adjusting feedback based on the analysis results and the user's emotional state, and emotion analysis means for recognizing the user's emotions and reflecting that information in the exercise plan. This enables the provision of appropriate fitness guidance and feedback that takes the user's emotional state into consideration in real time, optimizing the effectiveness of exercise and improving motivation.

[0155] "Information processing means" refers to a device or system that processes data corresponding to a specific purpose based on user input information and automatically generates an individualized exercise plan.

[0156] "Display means" refers to a device or interface that provides exercise guidance to the user by visually outputting information based on the generated exercise plan.

[0157] "Detection means" refers to a device or system that collects the user's movements and actions in real time using sensors and analyzes their progress.

[0158] "Output means" refers to a device or function that provides the user with adjusted feedback based on the analyzed results and the user's emotional state.

[0159] An "emotion analysis tool" is a device or system that recognizes the user's emotional state from their facial expressions, voice, etc., and uses that information to inform the movement plan.

[0160] This invention is a system that allows users to exercise safely at home and incorporates an emotion recognition function. The system generates an individualized exercise plan based on user information and analyzes emotional states in real time, reflecting this in the feedback to provide a more personalized fitness experience.

[0161] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device then sends this information to the server.

[0162] The server uses an AI algorithm to generate an individualized exercise plan based on the received user information. This AI model automatically creates an optimal plan tailored to the user's needs. This exercise plan is then sent to the device, which activates a hologram assistant based on the plan.

[0163] The hologram assistant visually demonstrates the correct exercise form to the user and provides exercise guidance. The device also uses emotion analysis technology, including facial recognition and voice analysis, to detect the user's emotional state. This emotion data is transmitted to a server in real time.

[0164] The server integrates and analyzes behavioral and emotional data to generate feedback that takes the user's emotional state into account. For example, if the user is stressed, the emotional engine will recommend exercises with reduced intensity. If motivation is low, it can also provide encouraging messages or feedback based on the user's preferences.

[0165] This system allows users to engage in exercise that meets their physical and mental needs, maximizing fitness benefits and maintaining sustained motivation.

[0166] Examples of prompts include "Generate an exercise plan based on the user profile" and "Provide feedback tailored to specific emotional states."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] Users enter their profile information on their home device. This information includes age, gender, fitness level, injury history, and fitness goals. The device receives this information as input data and sends it to the server. The data is encrypted and transmitted securely.

[0170] Step 2:

[0171] The server supplies the received user information as input to the AI ​​algorithm. This algorithm uses a generative AI model to automatically generate an individualized exercise plan based on the user's needs and goals. The generated exercise plan is sent to the terminal as output. High-precision personalization is achieved by utilizing the AI ​​model.

[0172] Step 3:

[0173] The device activates a hologram assistant based on the received exercise plan. The hologram assistant provides visual guidance, showing the user the correct form for the exercises according to the plan. This is important as part of visual feedback.

[0174] Step 4:

[0175] The device detects the user's emotional state in real time by capturing the user's face with a camera and picking up their voice with a microphone. The data obtained through facial recognition and voice analysis is used as input data for emotional state detection. The detected emotional data is sent to a server.

[0176] Step 5:

[0177] The server analyzes user motion data (obtained from sensors) and emotional data as input. Through this data processing, it generates output data that adjusts the exercise plan and feedback, taking the user's emotional state into account.

[0178] Step 6:

[0179] The device provides real-time feedback to the user based on analytical data sent from the server. This feedback includes encouraging messages tailored to the user's emotional state, adjustments to exercise intensity, and specific guidance to improve the user experience. In this step, the feedback content is customized to enhance user motivation and reduce stress.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] In recent years, with the rise of health consciousness, home exercise habits have gained attention. However, if exercise plans do not adapt to the user's physical condition or emotions, it can lead to decreased motivation and an increased risk of injury. Furthermore, conventional fitness systems lack the ability to dynamically reflect the user's emotions when suggesting exercises, making it difficult to provide a personalized exercise experience.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance based on the generated exercise plan, a detection means for detecting the user's movements and analyzing the progress of the exercise, an analysis means for recognizing the user's emotional state and reflecting that information in the exercise plan, and an output means for providing real-time feedback adjusted based on the user's emotions. This enables the provision of an exercise plan and feedback adapted to the user's physical and emotional state, resulting in an individualized fitness experience.

[0185] A "user" is an individual who uses the system to experience fitness.

[0186] "Input information" refers to data provided by the user, such as age, gender, fitness level, injury history, and fitness goals.

[0187] A "personalized exercise plan" is a program that suggests a specific exercise menu based on the user's input information.

[0188] "Calculation means" refers to a computing device or program designed to generate an optimal motion plan from user input information.

[0189] "Display means" refers to a device or method for visually providing exercise instructions based on a generated exercise plan.

[0190] "Detection means" refers to a device or technology for understanding a user's physical movements in real time and evaluating their progress and accuracy.

[0191] "Analysis means" refers to a system that analyzes data obtained from detection means and the user's emotional state to generate appropriate exercise plans and feedback.

[0192] "Emotional state" refers to the psychological condition inferred from the user's facial expressions, voice, and other factors.

[0193] "Real-time feedback" refers to guidance and encouragement provided in immediate response to a user's exercise.

[0194] "Output means" refers to a device or program that provides information or instructions to the user based on the results of the analysis.

[0195] The system for carrying out this invention provides a method for creating an individualized exercise plan based on information input by the user and adjusting it according to the user's emotional state.

[0196] Users enter personal information such as age, gender, fitness level, injury status, and fitness goals into a terminal. This information is sent to a server, which uses AI algorithms to perform calculations and generate a personalized exercise plan. This generation process utilizes AI frameworks known as data analysis technologies. Specific examples include libraries such as TensorFlow and PyTorch.

[0197] The generated motion plan is visually presented to the user through holograms or displays. Projector devices or AR-enabled displays can be used to provide visual information.

[0198] The user's actual movements are monitored in real time using detection methods that utilize cameras and sensors. Cameras can include depth sensors such as Intel RealSense. Furthermore, tools such as OpenCV and the Emotion API are used to analyze the user's facial expressions and voice to determine their emotional state.

[0199] Based on this detection data and user sentiment information, the server adjusts the exercise plan and real-time feedback, providing it to the user through output devices. The feedback includes voice guidance and encouragement using speech synthesis technology such as Google® Text-to-Speech.

[0200] For example, if a user notices they are feeling a bit down today, the system might suggest a gentle yoga routine. The prompt for the generating AI model might look something like, "What kind of exercise have you been doing lately? Today, let's make time to relax and pamper yourself."

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] Users use a device to input information such as their age, gender, fitness level, injury history, and fitness goals. This information is retrieved by the device and sent to the server. It is crucial that the input information is accurately transmitted to the server.

[0204] Step 2:

[0205] The server generates a personalized exercise plan using an AI algorithm based on the received user information. During this process, it performs calculations based on the input information and extracts the optimal exercises from a database. The calculation results are output as a plan and sent back to the terminal.

[0206] Step 3:

[0207] The terminal receives the personalized exercise plan transmitted from the server and presents it visually to the user using a display device. At this time, a projector device or AR display is used to visually show the exercise content.

[0208] Step 4:

[0209] The user's movements are detected in real time by cameras and sensors connected to the device. This allows the device to input the user's movement data and understand their actual movement patterns. Emotion recognition is also performed simultaneously, analyzing the user's emotional state from their facial expressions and voice.

[0210] Step 5:

[0211] The server receives exercise and emotional data transmitted from the terminal and analyzes them using AI. This allows it to evaluate whether the exercise is being performed as planned and what the emotional state is. By synthesizing the data, it monitors the progress of the exercise and changes in emotions.

[0212] Step 6:

[0213] Based on the analysis results, the server generates real-time feedback adapted to the user's emotional state. This feedback is output as voice from the device using speech synthesis technology. Specifically, it outputs encouraging words when motivation is low.

[0214] Step 7:

[0215] Users receive real-time feedback to adjust their exercise routine and form, thus continuing their fitness experience. The personalized experience is provided through feedback that takes into account the user's emotions and exercise progress.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention provides a system that allows users to exercise safely and effectively at home. This system automatically generates an exercise plan tailored to the user's characteristics and provides visual guidance, making it adaptable to a wide range of users.

[0233] First, the user enters their basic information into the system using a terminal. This information is extensive and includes age, gender, current fitness level, past injury history, and specific fitness goals. The terminal sends this information to the server, which then creates an individual profile for the user.

[0234] The server generates an optimized exercise plan for the user based on the information it receives. An AI algorithm is involved in this process, determining the type of exercise, frequency, and intensity that matches the user's level and goals. For example, a beginner user might be offered short sessions that include low-intensity strength training.

[0235] The generated exercise plan is sent to the device, which then activates a hologram assistant based on it. The hologram assistant appears as a 3D image in front of the user, providing detailed instructions on how to perform the exercises. This makes it easier for the user to visually understand the correct form of the exercises.

[0236] The device also features sensors that detect user movements in real time. This allows it to collect data on the exercises the user is actually performing and send it to a server. The server analyzes this data to determine if the exercises are being performed correctly. If necessary, it provides feedback to the user via the device, instructing them on areas that need correction.

[0237] Furthermore, the server tracks the user's exercise progress and provides motivational information through the device. This information encourages users to continue exercising, gives them a sense of accomplishment, and helps them set their next goals. For example, a message such as, "You're making good progress today; let's try a little harder next time," might be displayed.

[0238] Thus, the system of the present invention provides an exercise experience tailored to each individual user, minimizing the risk of injury and supporting sustained exercise.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0242] Step 2:

[0243] The terminal sends the entered user information to the server to prepare data for creating the user's individual profile.

[0244] Step 3:

[0245] Based on the received user information, the server automatically generates an optimized exercise plan for the user using an AI algorithm. This plan includes the type of exercise, the number of repetitions, and the intensity level.

[0246] Step 4:

[0247] The server sends the generated motion plan to the terminal. The terminal receives this plan and prepares to activate the hologram assistant.

[0248] Step 5:

[0249] The device activates a hologram assistant, which displays a 3D image in front of the user. The hologram assistant visually demonstrates the correct form and procedure for the exercises to the user.

[0250] Step 6:

[0251] The device uses sensors to detect the user's movements in real time and collects movement data. This allows the system to understand how the user is exercising.

[0252] Step 7:

[0253] The device sends the collected motion data to the server. The server analyzes the data and determines whether the user's movements are correct.

[0254] Step 8:

[0255] The server generates feedback based on the results of the motion analysis, including advice for form correction and motivation as needed.

[0256] Step 9:

[0257] The device receives feedback from the server and provides it to the user. This allows the user to understand their exercise progress and make necessary adjustments immediately.

[0258] Step 10:

[0259] The server saves user progress information and incorporates it into future exercise plans. It also provides motivational information to help users evaluate their ability to continue exercising.

[0260] (Example 1)

[0261] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0262] Conventional exercise instruction systems have struggled to generate personalized exercise plans for each user and provide accurate, real-time feedback. Therefore, they have been unable to create an environment that allows users to continue exercising safely and effectively. Furthermore, they lacked effective means of motivating users to exercise.

[0263] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0264] In this invention, the server includes numerical analysis means for automatically generating an individualized exercise plan using a generation AI model based on user input information, stereoscopic image display means for visually providing exercise guidance based on the generated exercise plan, and motion detection means for detecting the user's movements and analyzing the progress of the exercise. This makes it possible to provide an exercise plan optimized for the user and to provide accurate feedback and motivational information in real time.

[0265] The "numerical analysis means" is a mechanism for automatically generating personalized exercise plans using an AI model based on user input information.

[0266] A "stereoscopic image display means" is a system that displays 3D images to visually provide exercise guidance to the user based on the generated exercise plan.

[0267] A "motion detection means" is a system that senses the user's physical movements in real time and analyzes the collected motion data.

[0268] An "information output method" is a method for providing users with analysis results and motivational information, and for conveying exercise feedback in real time.

[0269] This invention provides a system that allows users to receive personalized exercise guidance at home and exercise effectively and safely. The system primarily operates through the cooperation of a server and a terminal.

[0270] Users first enter their basic information using a terminal. This includes age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to a server and stored in a dedicated database.

[0271] The server uses the user's profile data to generate an optimal exercise plan through a generative AI model. The program uses an AI algorithm to determine the type, frequency, and intensity of exercise that matches each user's level and goals. An example of a prompt used in this process is, "Please create an exercise plan based on the user's profile."

[0272] The exercise plan generated on the server is sent to the terminal, which then activates a hologram assistant based on it. This hologram assistant appears before the user as a 3D image and provides detailed instructions on the exercise. This makes it easier for the user to visually learn the correct exercise form.

[0273] Furthermore, the device is equipped with sensors that detect user movements in real time. This allows data on the user's actual exercises to be transmitted to a server. The server analyzes this data and evaluates whether the movements are being performed correctly. If necessary, it provides feedback to the user through the device and instructs them to correct their exercise form.

[0274] In this way, the server and terminal work together to provide the user with an optimized exercise experience, ensuring user safety while supporting effective training.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] Users enter basic information using a terminal. This information includes a wide range of details such as age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to the server. The server receives this data and stores the user's individual profile in a database.

[0278] Step 2:

[0279] The server generates an exercise plan using an AI model generated based on the user's profile information. Based on the input information, the generative AI model calculates the optimal exercise type, frequency, and intensity using the prompt sentence "Please create an exercise plan based on the user's profile." As output, an individualized exercise plan is generated.

[0280] Step 3:

[0281] The server sends the generated exercise plan to the terminal. Based on the received exercise plan, the terminal activates the hologram assistant. The hologram assistant uses 3D video to visually convey to the user the detailed implementation method of the exercise. As a result, the user can easily understand the correct exercise form.

[0282] Step 4:

[0283] The user performs the exercise according to the guidance of the hologram assistant. The terminal detects the user's movements in real time through the installed sensors. The input data from the sensors is temporarily processed by the terminal and sent to the server as exercise data.

[0284] Step 5:

[0285] The server analyzes the received exercise data and evaluates whether the exercise is being performed correctly. At this time, an AI algorithm is used to determine the accuracy, and feedback is generated if improvement is needed. The feedback is sent to the terminal in real time and provided to the user.

[0286] Step 6:

[0287] The user corrects the exercise form based on the feedback from the server. The terminal further displays motivation information according to the user's goal achievement status to support continuous training. This information includes content that appreciates the user's efforts and messages that prompt the setting of the next goal.

[0288] (Application Example 1)

[0289] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0290] In modern society, many individuals want to exercise efficiently and safely at home, but maintaining correct form and consistently training is difficult. Furthermore, they lack the feedback and motivation tailored to their individual fitness levels, resulting in insufficient solutions for their exercise needs. Therefore, it is necessary to provide a system that enables users to exercise appropriately by offering personalized exercise plans and providing visual, real-time exercise guidance and feedback.

[0291] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance via holograms based on the generated exercise plan, and a detection means for detecting the user's physical movements and analyzing the progress of the exercise in real time. This enables the user to effectively and safely execute the exercise plan at home, maintain proper form while receiving real-time feedback, and train continuously.

[0293] The "calculation means" refers to a function that automatically generates an individualized exercise plan based on the user's input information.

[0294] A "display means" is a device that provides the generated exercise plan visually using a hologram and guides the user through exercise.

[0295] "Detection means" refers to a function that uses sensors to detect the user's physical movements and analyzes their progress in real time.

[0296] The "output mechanism" is a mechanism for providing users with real-time guidance on correcting their exercise form based on the analysis results.

[0297] A "data processing system" is a system that continuously learns the user's exercise history and executes a process to optimize the next exercise plan.

[0298] This invention provides a system that allows users to exercise safely and effectively at home. Specifically, a server uses an AI algorithm to automatically generate a personalized exercise plan from the user's input information. First, the user inputs information such as age, gender, fitness level, injury history, and fitness goals into a terminal. The terminal sends this information to the server, which then generates an exercise plan optimized for the user based on the received information.

[0299] The generated plan is sent to the terminal and presented to the user as a 3D hologram. This allows the user to visually understand the correct form of the exercise. The terminal is equipped with sensors to detect the user's body movements in real time. This data is sent to a server, where an AI algorithm analyzes the progress of the exercise. The server determines whether the exercise is being performed correctly and, if necessary, provides guidance to correct the exercise form to the user via the terminal.

[0300] Furthermore, the server stores the user's exercise history and processes the data to optimize the next exercise plan. The hardware used includes a physical assistant robot with holographic display capabilities and Microsoft's skeletal tracking sensors. The software utilizes AI algorithms such as TensorFlow and PyTorch, and a real-time feedback system using OpenCV.

[0301] As a specific example, when a user wants to start a beginner-level fitness program, the system proposes a short-duration and low-intensity exercise session for first-time users. Movements are detected in real time, and specific feedback such as "Stretch your back a little more" is provided through the terminal.

[0302] As an example of a prompt sentence for the generative AI model, there is "A 47-year-old male who has had knee surgery in the past and has a medium physical strength level. Please propose an exercise plan to steadily improve physical strength without overexertion." In this way, the user can effectively achieve fitness goals through a dedicated exercise experience.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The user uses the terminal to input basic information (age, gender, physical strength level, injury history, fitness goal). This input information is sent from the terminal to the server and prepared as data necessary for processing by the AI algorithm. As a result, a user profile is generated.

[0306] Step 2:

[0307] The server executes the AI algorithm based on the user profile and automatically generates an individualized exercise plan. At this time, the type, frequency, and intensity of the exercise are determined based on the input information. As output, the generated exercise plan is obtained.

[0308] Step 3:

[0309] The terminal that has received the exercise plan activates the hologram display and presents how to perform the exercise using 3D images in front of the user. Based on the input exercise plan, the content is visually displayed.

[0310] Step 4:

[0311] Sensors built into the device detect the user's body movements in real time. Based on the input data from the sensors, the server analyzes the user's movement progress and evaluates the accuracy of their posture and movements. This analysis determines whether the user's movements are being performed appropriately.

[0312] Step 5:

[0313] Based on the analysis results, the server provides real-time feedback on how to correct or improve exercise form. This feedback is transmitted to the user via the terminal, and specific instructions such as "Raise your arms a little higher" are output.

[0314] Step 6:

[0315] The server learns the user's exercise history and processes the data to optimize the next exercise plan for better suitability. Here, past exercise data is used as input, and the generating AI model is ready to output an improved exercise plan for future training.

[0316] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0317] This invention adds emotion recognition functionality to a system that allows users to safely continue exercising at home, further personalizing and improving the traditional fitness experience. This system provides an automatically generated exercise plan based on the user's personal information, real-time feedback, and also recognizes the user's emotional state, reflecting that information in the exercise and feedback.

[0318] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device sends this information to the server to create a personalized profile for the user.

[0319] The server uses an AI algorithm to generate a personalized exercise plan based on user information. The generated plan is sent to the terminal, which then activates a hologram assistant. The hologram assistant shows the correct form for each exercise and provides visual guidance.

[0320] This system also incorporates an emotion engine. The device detects the user's emotional state in real time through facial recognition technology and voice analysis. This emotional data is sent to a server and used to inform exercise plans and feedback.

[0321] For example, if the system detects that a user is experiencing stress, the emotion engine will temporarily reduce the intensity of the exercise and recommend relaxation-focused exercises. Furthermore, if a decrease in motivation is detected, the system will provide encouraging words and feedback tailored to the user's preferences through a hologram assistant.

[0322] The device detects the user's movements in real time and sends motion and emotional data collected by sensors to a server. The server analyzes this data and generates accurate form and emotionally responsive feedback. Emotion-based feedback personalizes the user's fitness experience and maximizes the effectiveness of their workout.

[0323] This system allows users to exercise according to their physical and psychological needs, enhancing fitness while reducing the risk of injury.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0327] Step 2:

[0328] The terminal sends the input information to the server and prepares the data for creating the user's individual profile.

[0329] Step 3:

[0330] The server uses AI algorithms to automatically generate personalized exercise plans based on the user's profile.

[0331] Step 4:

[0332] The server sends the generated motion plan to the terminal, and after the terminal receives the plan, it activates the hologram assistant.

[0333] Step 5:

[0334] The hologram assistant is displayed to the user, visually guiding them through the correct form and procedure of exercise.

[0335] Step 6:

[0336] The device uses facial recognition sensors and voice analysis functions to detect the user's emotional state in real time.

[0337] Step 7:

[0338] The device sends detected emotion data to the server, which then dynamically adjusts the exercise plan and feedback content based on this data.

[0339] Step 8:

[0340] The server generates and sends feedback that reflects emotional data to the terminal. The feedback includes content tailored to the user's emotional state.

[0341] Step 9:

[0342] The device provides users with emotionally responsive feedback through a holographic assistant, increasing their motivation to exercise.

[0343] Step 10:

[0344] User data regarding exercise and emotions is regularly saved to the server and used to plan future exercise sessions.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0347] Conventional fitness systems can adapt to some extent to a user's physical condition, but they often fail to adequately address the user's emotional state, resulting in uniform exercise plans and feedback. This has made it difficult for users to receive appropriate guidance and feedback that responds to changes in stress and motivation. Therefore, the present invention aims to provide a personalized fitness experience that takes the user's emotional state into account, thereby enhancing the effectiveness of exercise while maintaining motivation for continuous exercise.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes information processing means for automatically generating an individualized exercise plan based on user input information, display means for visually providing exercise guidance based on the generated exercise plan, detection means for detecting the user's exercise and analyzing the progress of the exercise, output means for adjusting feedback based on the analysis results and the user's emotional state, and emotion analysis means for recognizing the user's emotions and reflecting that information in the exercise plan. This enables the provision of appropriate fitness guidance and feedback that takes the user's emotional state into consideration in real time, optimizing the effectiveness of exercise and improving motivation.

[0350] "Information processing means" refers to a device or system that processes data corresponding to a specific purpose based on user input information and automatically generates an individualized exercise plan.

[0351] "Display means" refers to a device or interface that visually outputs information based on the generated exercise plan and provides exercise guidance to the user.

[0352] A "detection means" is a device or system that collects the user's movements and actions in real time using sensors and analyzes their progress.

[0353] "Output means" refers to a device or function that provides the user with adjusted feedback based on the analyzed results and the user's emotional state.

[0354] An "emotion analysis tool" is a device or system that recognizes a user's emotional state from their facial expressions, voice, etc., and uses that information to inform their movement plan.

[0355] This invention is a system that allows users to exercise safely at home and incorporates an emotion recognition function. The system generates an individualized exercise plan based on user information and analyzes emotional states in real time, reflecting this in the feedback to provide a more personalized fitness experience.

[0356] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device then sends this information to the server.

[0357] The server uses an AI algorithm to generate an individualized exercise plan based on the received user information. This generated AI model automatically creates an optimal plan that matches the user's needs. This exercise plan is sent to the device, which then activates a hologram assistant based on the plan.

[0358] The hologram assistant visually demonstrates the correct exercise form to the user and provides exercise guidance. The device also uses emotion analysis technology, including facial recognition and voice analysis, to detect the user's emotional state. This emotion data is transmitted to a server in real time.

[0359] The server integrates and analyzes behavioral and emotional data to generate feedback that takes the user's emotional state into account. For example, if the user is stressed, the emotional engine will recommend exercises with reduced intensity. If motivation is low, it can also provide encouraging messages or feedback based on the user's preferences.

[0360] This system allows users to engage in exercise that meets their physical and mental needs, maximizing fitness benefits and maintaining sustained motivation.

[0361] Examples of prompts include "Generate an exercise plan based on the user profile" and "Provide feedback tailored to specific emotional states."

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] Users enter their profile information on their home device. This information includes age, gender, fitness level, injury history, and fitness goals. The device receives this information as input data and sends it to the server. The data is encrypted and transmitted securely.

[0365] Step 2:

[0366] The server supplies the received user information as input to the AI ​​algorithm. This algorithm uses a generative AI model to automatically generate an individualized exercise plan based on the user's needs and goals. The generated exercise plan is sent to the terminal as output. High-precision personalization is achieved by utilizing the AI ​​model.

[0367] Step 3:

[0368] The device activates a hologram assistant based on the received exercise plan. The hologram assistant provides visual guidance, showing the user the correct form for the exercises according to the plan. This is important as part of visual feedback.

[0369] Step 4:

[0370] The device detects the user's emotional state in real time by capturing the user's face with a camera and picking up their voice with a microphone. The data obtained through facial recognition and voice analysis is used as input data for emotional state detection. The detected emotional data is sent to a server.

[0371] Step 5:

[0372] The server analyzes user motion data (obtained from sensors) and emotional data as input. Through this data processing, it generates output data that adjusts the exercise plan and feedback, taking the user's emotional state into account.

[0373] Step 6:

[0374] The device provides real-time feedback to the user based on analytical data sent from the server. This feedback includes encouraging messages tailored to the user's emotional state, adjustments to exercise intensity, and specific guidance to improve the user experience. In this step, the feedback content is customized to enhance user motivation and reduce stress.

[0375] (Application Example 2)

[0376] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0377] In recent years, with the rise of health consciousness, home exercise habits have gained attention. However, if exercise plans do not adapt to the user's physical condition or emotions, it can lead to decreased motivation and an increased risk of injury. Furthermore, conventional fitness systems lack the ability to dynamically reflect the user's emotions when suggesting exercises, making it difficult to provide a personalized exercise experience.

[0378] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0379] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance based on the generated exercise plan, a detection means for detecting the user's movements and analyzing the progress of the exercise, an analysis means for recognizing the user's emotional state and reflecting that information in the exercise plan, and an output means for providing real-time feedback adjusted based on the user's emotions. This enables the provision of an exercise plan and feedback adapted to the user's physical and emotional state, resulting in an individualized fitness experience.

[0380] A "user" is an individual who uses the system to experience fitness.

[0381] "Input information" refers to data provided by the user, such as age, gender, fitness level, injury history, and fitness goals.

[0382] A "personalized exercise plan" is a program that suggests a specific exercise menu based on the user's input information.

[0383] "Calculation means" refers to a computing device or program designed to generate an optimal motion plan from user input information.

[0384] "Display means" refers to a device or method for visually providing exercise instructions based on a generated exercise plan.

[0385] "Detection means" refers to a device or technology for understanding a user's physical movements in real time and evaluating their progress and accuracy.

[0386] "Analysis means" refers to a system that analyzes data obtained from detection means and the user's emotional state to generate appropriate exercise plans and feedback.

[0387] "Emotional state" refers to the psychological condition inferred from the user's facial expressions, voice, and other factors.

[0388] "Real-time feedback" refers to guidance and encouragement provided in immediate response to a user's exercise.

[0389] "Output means" refers to a device or program that provides information or instructions to the user based on the results of the analysis.

[0390] The system for implementing this invention provides a method for creating an individualized exercise plan based on information input by the user and adjusting it according to the user's emotional state.

[0391] Users enter personal information such as age, gender, fitness level, injury status, and fitness goals into a terminal. This information is sent to a server, which uses AI algorithms to perform calculations and generate a personalized exercise plan. This generation process utilizes AI frameworks known as data analysis technologies. Specific examples include libraries such as TensorFlow and PyTorch.

[0392] The generated motion plan is visually presented to the user through holograms or displays. Projector devices or AR-enabled displays can be used to provide visual information.

[0393] The user's actual movements are monitored in real time using detection methods that utilize cameras and sensors. Cameras can include depth sensors such as Intel RealSense. Furthermore, tools such as OpenCV and the Emotion API are used to analyze the user's facial expressions and voice to determine their emotional state.

[0394] Based on this detection data and user sentiment information, the server adjusts the exercise plan and real-time feedback, providing it to the user through output devices. The feedback includes voice guidance and encouragement using speech synthesis technology such as Google Text-to-Speech.

[0395] For example, if a user notices they are feeling a bit down today, the system might suggest a gentle yoga routine. The prompt for the generating AI model might look something like, "What kind of exercise have you been doing lately? Today, let's make time to relax and pamper yourself."

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] Users use a device to input information such as their age, gender, fitness level, injury history, and fitness goals. This information is retrieved by the device and sent to the server. It is crucial that the input information is accurately transmitted to the server.

[0399] Step 2:

[0400] The server generates a personalized exercise plan using an AI algorithm based on the received user information. During this process, it performs calculations based on the input information and extracts the optimal exercises from a database. The calculation results are output as a plan and sent back to the terminal.

[0401] Step 3:

[0402] The terminal receives the personalized exercise plan transmitted from the server and presents it visually to the user using a display device. At this time, a projector device or AR display is used to visually show the exercise content.

[0403] Step 4:

[0404] The user's movements are detected in real time by cameras and sensors connected to the device. This allows the device to input the user's movement data and understand their actual movement patterns. Emotion recognition is also performed simultaneously, analyzing the user's emotional state from their facial expressions and voice.

[0405] Step 5:

[0406] The server receives exercise and emotional data transmitted from the terminal and analyzes them using AI. This allows it to evaluate whether the exercise is being performed as planned and what the emotional state is. By synthesizing the data, it monitors the progress of the exercise and changes in emotions.

[0407] Step 6:

[0408] Based on the analysis results, the server generates real-time feedback adapted to the user's emotional state. This feedback is output as voice from the device using speech synthesis technology. Specifically, it outputs encouraging words when motivation is low.

[0409] Step 7:

[0410] Users receive real-time feedback to adjust their exercise routine and form, thus continuing their fitness experience. The personalized experience is provided through feedback that takes into account the user's emotions and exercise progress.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] This invention provides a system that allows users to exercise safely and effectively at home. This system automatically generates an exercise plan tailored to the user's characteristics and provides visual guidance, making it adaptable to a wide range of users.

[0428] First, the user enters their basic information into the system using a terminal. This information is extensive and includes age, gender, current fitness level, past injury history, and specific fitness goals. The terminal sends this information to the server, which then creates an individual profile for the user.

[0429] The server generates an optimized exercise plan for the user based on the information it receives. An AI algorithm is involved in this process, determining the type of exercise, frequency, and intensity that matches the user's level and goals. For example, a beginner user might be offered short sessions that include low-intensity strength training.

[0430] The generated exercise plan is sent to the device, which then activates a hologram assistant based on it. The hologram assistant appears as a 3D image in front of the user, providing detailed instructions on how to perform the exercises. This makes it easier for the user to visually understand the correct form of the exercises.

[0431] The device also features sensors that detect user movements in real time. This allows it to collect data on the exercises the user is actually performing and send it to a server. The server analyzes this data to determine if the exercises are being performed correctly. If necessary, it provides feedback to the user via the device, instructing them on areas that need correction.

[0432] Furthermore, the server tracks the user's exercise progress and provides motivational information through the device. This information encourages users to continue exercising, gives them a sense of accomplishment, and helps them set their next goals. For example, a message such as, "You're making good progress today; let's try a little harder next time," might be displayed.

[0433] Thus, the system of the present invention provides an exercise experience tailored to each individual user, minimizing the risk of injury and supporting sustained exercise.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0437] Step 2:

[0438] The terminal sends the entered user information to the server to prepare data for creating the user's individual profile.

[0439] Step 3:

[0440] Based on the received user information, the server automatically generates an optimized exercise plan for the user using an AI algorithm. This plan includes the type of exercise, the number of repetitions, and the intensity level.

[0441] Step 4:

[0442] The server sends the generated motion plan to the terminal. The terminal receives this plan and prepares to activate the hologram assistant.

[0443] Step 5:

[0444] The device activates a hologram assistant, which displays a 3D image in front of the user. The hologram assistant visually demonstrates the correct form and procedure for the exercises to the user.

[0445] Step 6:

[0446] The device uses sensors to detect the user's movements in real time and collects movement data. This allows the system to understand how the user is exercising.

[0447] Step 7:

[0448] The device sends the collected motion data to the server. The server analyzes the data and determines whether the user's movements are correct.

[0449] Step 8:

[0450] The server generates feedback based on the results of the motion analysis, including advice for form correction and motivation as needed.

[0451] Step 9:

[0452] The device receives feedback from the server and provides it to the user. This allows the user to understand their exercise progress and make necessary adjustments immediately.

[0453] Step 10:

[0454] The server saves user progress information and incorporates it into future exercise plans. It also provides motivational information to help users evaluate their ability to continue exercising.

[0455] (Example 1)

[0456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] Conventional exercise instruction systems have struggled to generate personalized exercise plans for each user and provide accurate, real-time feedback. Therefore, they have been unable to create an environment that allows users to continue exercising safely and effectively. Furthermore, they lacked effective means of motivating users to exercise.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0459] In this invention, the server includes numerical analysis means for automatically generating an individualized exercise plan using a generation AI model based on user input information, stereoscopic image display means for visually providing exercise guidance based on the generated exercise plan, and motion detection means for detecting the user's movements and analyzing the progress of the exercise. This makes it possible to provide an exercise plan optimized for the user and to provide accurate feedback and motivational information in real time.

[0460] The "numerical analysis means" is a mechanism for automatically generating personalized exercise plans using an AI model based on user input information.

[0461] A "stereoscopic image display means" is a system that displays 3D images to visually provide exercise guidance to the user based on the generated exercise plan.

[0462] A "motion detection means" is a system that senses the user's physical movements in real time and analyzes the collected motion data.

[0463] An "information output method" is a method for providing users with analysis results and motivational information, and for conveying exercise feedback in real time.

[0464] This invention provides a system that allows users to receive personalized exercise guidance at home and exercise effectively and safely. The system primarily operates through the cooperation of a server and a terminal.

[0465] Users first enter their basic information using a terminal. This includes age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to a server and stored in a dedicated database.

[0466] The server uses the user's profile data to generate an optimal exercise plan through a generative AI model. The program uses an AI algorithm to determine the type, frequency, and intensity of exercise that matches each user's level and goals. An example of a prompt used in this process is, "Please create an exercise plan based on the user's profile."

[0467] The exercise plan generated on the server is sent to the terminal, which then activates a hologram assistant based on it. This hologram assistant appears before the user as a 3D image and provides detailed instructions on the exercise. This makes it easier for the user to visually learn the correct exercise form.

[0468] Furthermore, the device is equipped with sensors that detect user movements in real time. This allows data on the user's actual exercises to be transmitted to a server. The server analyzes this data and evaluates whether the movements are being performed correctly. If necessary, it provides feedback to the user through the device and instructs them to correct their exercise form.

[0469] In this way, the server and terminal work together to provide the user with an optimized exercise experience, ensuring user safety while supporting effective training.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] Users enter basic information using a terminal. This information includes a wide range of details such as age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to the server. The server receives this data and stores the user's individual profile in a database.

[0473] Step 2:

[0474] The server generates an exercise plan using a generative AI model based on the user's profile information. Based on the input information, the generative AI model calculates the optimal exercise type, frequency, and intensity using the prompt message "Create an exercise plan based on the user's profile." As output, a personalized exercise plan is generated.

[0475] Step 3:

[0476] The server sends the generated exercise plan to the terminal. Based on the received exercise plan, the terminal activates a hologram assistant. The hologram assistant uses 3D images to visually explain to the user detailed instructions on how to perform the exercises. This makes it easy for the user to understand the correct exercise form.

[0477] Step 4:

[0478] The user performs exercises following the guidance of a hologram assistant. The device detects the user's movements in real time through its built-in sensors. The input data from the sensors is temporarily processed by the device and sent to the server as exercise data.

[0479] Step 5:

[0480] The server analyzes the received exercise data and evaluates whether the exercise is being performed correctly. Using an AI algorithm, it determines accuracy and generates feedback if improvement is needed. This feedback is sent to the device in real time and provided to the user.

[0481] Step 6:

[0482] The user modifies their exercise form based on feedback from the server. The device also displays motivational information tailored to the user's progress toward their goals, supporting continued training. This information includes messages praising the user's efforts and encouraging them to set new goals.

[0483] (Application Example 1)

[0484] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] In modern society, many individuals want to exercise efficiently and safely at home, but maintaining correct form and consistently training is difficult. Furthermore, they lack the feedback and motivation tailored to their individual fitness levels, resulting in insufficient solutions for their exercise needs. Therefore, it is necessary to provide a system that enables users to exercise appropriately by offering personalized exercise plans and providing visual, real-time exercise guidance and feedback.

[0486] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0487] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance via holograms based on the generated exercise plan, and a detection means for detecting the user's physical movements and analyzing the progress of the exercise in real time. This enables the user to effectively and safely execute the exercise plan at home, maintain proper form while receiving real-time feedback, and train continuously.

[0488] The "calculation means" refers to a function that automatically generates an individualized exercise plan based on the user's input information.

[0489] A "display means" is a device that provides the generated exercise plan visually using a hologram and guides the user through exercise.

[0490] "Detection means" refers to a function that uses sensors to detect the user's physical movements and analyzes their progress in real time.

[0491] The "output mechanism" is a mechanism for providing users with real-time guidance on correcting their exercise form based on the analysis results.

[0492] A "data processing system" is a system that continuously learns the user's exercise history and executes a process to optimize the next exercise plan.

[0493] This invention provides a system that allows users to exercise safely and effectively at home. Specifically, a server uses an AI algorithm to automatically generate a personalized exercise plan from the user's input information. First, the user inputs information such as age, gender, fitness level, injury history, and fitness goals into a terminal. The terminal sends this information to the server, which then generates an exercise plan optimized for the user based on the received information.

[0494] The generated plan is sent to the terminal and presented to the user as a 3D hologram. This allows the user to visually understand the correct form of the exercise. The terminal is equipped with sensors to detect the user's body movements in real time. This data is sent to a server, where an AI algorithm analyzes the progress of the exercise. The server determines whether the exercise is being performed correctly and, if necessary, provides guidance to correct the exercise form to the user via the terminal.

[0495] Furthermore, the server stores the user's exercise history and processes the data to optimize the next exercise plan. The hardware used includes a physical assistant robot with holographic display capabilities and Microsoft's skeletal tracking sensors. The software utilizes AI algorithms such as TensorFlow and PyTorch, and a real-time feedback system using OpenCV.

[0496] For example, if a user wants to start a beginner-level fitness program, the system suggests a short, low-intensity exercise session for first-time users. Movements are detected in real time, and specific feedback such as "straighten your back a little more" is provided through the device.

[0497] An example of a prompt for a generating AI model is: "I am a 47-year-old male with a history of knee surgery and an intermediate fitness level. Please suggest an exercise plan that will allow me to improve my fitness steadily and without overexertion." In this way, users can effectively achieve their fitness goals through a personalized exercise experience.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The user enters basic information (age, gender, fitness level, injury history, fitness goals) using their device. This information is sent from the device to the server and prepared as data necessary for processing by the AI ​​algorithm. As a result, a user profile is generated.

[0501] Step 2:

[0502] The server executes an AI algorithm based on the user profile to automatically generate a personalized exercise plan. The type, frequency, and intensity of the exercise are determined based on the input information. The generated exercise plan is provided as output.

[0503] Step 3:

[0504] The device that receives the exercise plan activates a hologram display and presents the exercise method using 3D images in front of the user. The content is displayed visually based on the entered exercise plan.

[0505] Step 4:

[0506] Sensors built into the device detect the user's body movements in real time. Based on the input data from the sensors, the server analyzes the user's movement progress and evaluates the accuracy of their posture and movements. This analysis determines whether the user's movements are being performed appropriately.

[0507] Step 5:

[0508] Based on the analysis results, the server provides real-time feedback on how to correct or improve exercise form. This feedback is transmitted to the user via the terminal, and specific instructions such as "Raise your arms a little higher" are output.

[0509] Step 6:

[0510] The server learns the user's exercise history and processes the data to optimize the next exercise plan for better suitability. Here, past exercise data is used as input, and the generating AI model is ready to output an improved exercise plan for future training.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] This invention adds emotion recognition functionality to a system that allows users to safely continue exercising at home, further personalizing and improving the traditional fitness experience. This system provides an automatically generated exercise plan based on the user's personal information, real-time feedback, and also recognizes the user's emotional state, reflecting that information in the exercise and feedback.

[0513] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device sends this information to the server to create a personalized profile for the user.

[0514] The server uses an AI algorithm to generate a personalized exercise plan based on user information. The generated plan is sent to the terminal, which then activates a hologram assistant. The hologram assistant shows the correct form for each exercise and provides visual guidance.

[0515] This system also incorporates an emotion engine. The device detects the user's emotional state in real time through facial recognition technology and voice analysis. This emotional data is sent to a server and used to inform exercise plans and feedback.

[0516] For example, if the system detects that a user is experiencing stress, the emotion engine will temporarily reduce the intensity of the exercise and recommend relaxation-focused exercises. Furthermore, if a decrease in motivation is detected, the system will provide encouraging words and feedback tailored to the user's preferences through a hologram assistant.

[0517] The device detects the user's movements in real time and sends motion and emotional data collected by sensors to a server. The server analyzes this data and generates accurate form and emotionally responsive feedback. Emotion-based feedback personalizes the user's fitness experience and maximizes the effectiveness of their workout.

[0518] This system allows users to exercise according to their physical and psychological needs, enhancing fitness while reducing the risk of injury.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0522] Step 2:

[0523] The terminal sends the input information to the server and prepares the data for creating the user's individual profile.

[0524] Step 3:

[0525] The server uses AI algorithms to automatically generate personalized exercise plans based on the user's profile.

[0526] Step 4:

[0527] The server sends the generated motion plan to the terminal, and after the terminal receives the plan, it activates the hologram assistant.

[0528] Step 5:

[0529] The hologram assistant is displayed to the user, visually guiding them through the correct form and procedure of exercise.

[0530] Step 6:

[0531] The device uses facial recognition sensors and voice analysis functions to detect the user's emotional state in real time.

[0532] Step 7:

[0533] The device sends detected emotion data to the server, which then dynamically adjusts the exercise plan and feedback content based on this data.

[0534] Step 8:

[0535] The server generates and sends feedback that reflects emotional data to the terminal. The feedback includes content tailored to the user's emotional state.

[0536] Step 9:

[0537] The device provides users with emotionally responsive feedback through a holographic assistant, increasing their motivation to exercise.

[0538] Step 10:

[0539] User data regarding exercise and emotions is regularly saved to the server and used to plan future exercise sessions.

[0540] (Example 2)

[0541] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] Conventional fitness systems can adapt to some extent to a user's physical condition, but they often fail to adequately address the user's emotional state, resulting in uniform exercise plans and feedback. This has made it difficult for users to receive appropriate guidance and feedback that responds to changes in stress and motivation. Therefore, the present invention aims to provide a personalized fitness experience that takes the user's emotional state into account, thereby enhancing the effectiveness of exercise while maintaining motivation for continuous exercise.

[0543] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0544] In this invention, the server includes information processing means for automatically generating an individualized exercise plan based on user input information, display means for visually providing exercise guidance based on the generated exercise plan, detection means for detecting the user's exercise and analyzing the progress of the exercise, output means for adjusting feedback based on the analysis results and the user's emotional state, and emotion analysis means for recognizing the user's emotions and reflecting that information in the exercise plan. This enables the provision of appropriate fitness guidance and feedback that takes the user's emotional state into consideration in real time, optimizing the effectiveness of exercise and improving motivation.

[0545] "Information processing means" refers to a device or system that processes data corresponding to a specific purpose based on user input information and automatically generates an individualized exercise plan.

[0546] "Display means" refers to a device or interface that visually outputs information based on the generated exercise plan and provides exercise guidance to the user.

[0547] A "detection means" is a device or system that collects the user's movements and actions in real time using sensors and analyzes their progress.

[0548] "Output means" refers to a device or function that provides the user with adjusted feedback based on the analyzed results and the user's emotional state.

[0549] An "emotion analysis tool" is a device or system that recognizes a user's emotional state from their facial expressions, voice, etc., and uses that information to inform their movement plan.

[0550] This invention is a system that allows users to exercise safely at home and incorporates an emotion recognition function. The system generates an individualized exercise plan based on user information and analyzes emotional states in real time, reflecting this in the feedback to provide a more personalized fitness experience.

[0551] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device then sends this information to the server.

[0552] The server uses an AI algorithm to generate an individualized exercise plan based on the received user information. This generated AI model automatically creates an optimal plan that matches the user's needs. This exercise plan is sent to the device, which then activates a hologram assistant based on the plan.

[0553] The hologram assistant visually demonstrates the correct exercise form to the user and provides exercise guidance. The device also uses emotion analysis technology, including facial recognition and voice analysis, to detect the user's emotional state. This emotion data is transmitted to a server in real time.

[0554] The server integrates and analyzes behavioral and emotional data to generate feedback that takes the user's emotional state into account. For example, if the user is stressed, the emotional engine will recommend exercises with reduced intensity. If motivation is low, it can also provide encouraging messages or feedback based on the user's preferences.

[0555] This system allows users to engage in exercise that meets their physical and mental needs, maximizing fitness benefits and maintaining sustained motivation.

[0556] Examples of prompts include "Generate an exercise plan based on the user profile" and "Provide feedback tailored to specific emotional states."

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] Users enter their profile information on their home device. This information includes age, gender, fitness level, injury history, and fitness goals. The device receives this information as input data and sends it to the server. The data is encrypted and transmitted securely.

[0560] Step 2:

[0561] The server supplies the received user information as input to the AI ​​algorithm. This algorithm uses a generative AI model to automatically generate an individualized exercise plan based on the user's needs and goals. The generated exercise plan is sent to the terminal as output. High-precision personalization is achieved by utilizing the AI ​​model.

[0562] Step 3:

[0563] The device activates a hologram assistant based on the received exercise plan. The hologram assistant provides visual guidance, showing the user the correct form for the exercises according to the plan. This is important as part of visual feedback.

[0564] Step 4:

[0565] The device detects the user's emotional state in real time by capturing the user's face with a camera and picking up their voice with a microphone. The data obtained through facial recognition and voice analysis is used as input data for emotional state detection. The detected emotional data is sent to a server.

[0566] Step 5:

[0567] The server analyzes user motion data (obtained from sensors) and emotional data as input. Through this data processing, it generates output data that adjusts the exercise plan and feedback, taking the user's emotional state into account.

[0568] Step 6:

[0569] The device provides real-time feedback to the user based on analytical data sent from the server. This feedback includes encouraging messages tailored to the user's emotional state, adjustments to exercise intensity, and specific guidance to improve the user experience. In this step, the feedback content is customized to enhance user motivation and reduce stress.

[0570] (Application Example 2)

[0571] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0572] In recent years, with the rise of health consciousness, home exercise habits have gained attention. However, if exercise plans do not adapt to the user's physical condition or emotions, it can lead to decreased motivation and an increased risk of injury. Furthermore, conventional fitness systems lack the ability to dynamically reflect the user's emotions when suggesting exercises, making it difficult to provide a personalized exercise experience.

[0573] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0574] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance based on the generated exercise plan, a detection means for detecting the user's movements and analyzing the progress of the exercise, an analysis means for recognizing the user's emotional state and reflecting that information in the exercise plan, and an output means for providing real-time feedback adjusted based on the user's emotions. This enables the provision of an exercise plan and feedback adapted to the user's physical and emotional state, resulting in an individualized fitness experience.

[0575] A "user" is an individual who uses the system to experience fitness.

[0576] "Input information" refers to data provided by the user, such as age, gender, fitness level, injury history, and fitness goals.

[0577] A "personalized exercise plan" is a program that suggests a specific exercise menu based on the user's input information.

[0578] "Calculation means" refers to a computing device or program designed to generate an optimal motion plan from user input information.

[0579] "Display means" refers to a device or method for visually providing exercise instructions based on a generated exercise plan.

[0580] "Detection means" refers to a device or technology for understanding a user's physical movements in real time and evaluating their progress and accuracy.

[0581] "Analysis means" refers to a system that analyzes data obtained from detection means and the user's emotional state to generate appropriate exercise plans and feedback.

[0582] "Emotional state" refers to the psychological condition inferred from the user's facial expressions, voice, and other factors.

[0583] "Real-time feedback" refers to guidance and encouragement provided in immediate response to a user's exercise.

[0584] "Output means" refers to a device or program that provides information or instructions to the user based on the results of the analysis.

[0585] The system for implementing this invention provides a method for creating an individualized exercise plan based on information input by the user and adjusting it according to the user's emotional state.

[0586] Users enter personal information such as age, gender, fitness level, injury status, and fitness goals into a terminal. This information is sent to a server, which uses AI algorithms to perform calculations and generate a personalized exercise plan. This generation process utilizes AI frameworks known as data analysis technologies. Specific examples include libraries such as TensorFlow and PyTorch.

[0587] The generated motion plan is visually presented to the user through holograms or displays. Projector devices or AR-enabled displays can be used to provide visual information.

[0588] The user's actual movements are monitored in real time using detection methods that utilize cameras and sensors. Cameras can include depth sensors such as Intel RealSense. Furthermore, tools such as OpenCV and the Emotion API are used to analyze the user's facial expressions and voice to determine their emotional state.

[0589] Based on this detection data and user sentiment information, the server adjusts the exercise plan and real-time feedback, providing it to the user through output devices. The feedback includes voice guidance and encouragement using speech synthesis technology such as Google Text-to-Speech.

[0590] For example, if a user notices they are feeling a bit down today, the system might suggest a gentle yoga routine. The prompt for the generating AI model might look something like, "What kind of exercise have you been doing lately? Today, let's make time to relax and pamper yourself."

[0591] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0592] Step 1:

[0593] Users use a device to input information such as their age, gender, fitness level, injury history, and fitness goals. This information is retrieved by the device and sent to the server. It is crucial that the input information is accurately transmitted to the server.

[0594] Step 2:

[0595] The server generates a personalized exercise plan using an AI algorithm based on the received user information. During this process, it performs calculations based on the input information and extracts the optimal exercises from a database. The calculation results are output as a plan and sent back to the terminal.

[0596] Step 3:

[0597] The terminal receives the personalized exercise plan transmitted from the server and presents it visually to the user using a display device. At this time, a projector device or AR display is used to visually show the exercise content.

[0598] Step 4:

[0599] The user's movements are detected in real time by cameras and sensors connected to the device. This allows the device to input the user's movement data and understand their actual movement patterns. Emotion recognition is also performed simultaneously, analyzing the user's emotional state from their facial expressions and voice.

[0600] Step 5:

[0601] The server receives exercise and emotional data transmitted from the terminal and analyzes them using AI. This allows it to evaluate whether the exercise is being performed as planned and what the emotional state is. By synthesizing the data, it monitors the progress of the exercise and changes in emotions.

[0602] Step 6:

[0603] Based on the analysis results, the server generates real-time feedback adapted to the user's emotional state. This feedback is output as voice from the device using speech synthesis technology. Specifically, it outputs encouraging words when motivation is low.

[0604] Step 7:

[0605] Users receive real-time feedback to adjust their exercise routine and form, thus continuing their fitness experience. The personalized experience is provided through feedback that takes into account the user's emotions and exercise progress.

[0606] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0607] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0608] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0612] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0613] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0614] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0615] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0616] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0617] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0618] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0619] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0620] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0621] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0622] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0623] This invention provides a system that allows users to exercise safely and effectively at home. This system automatically generates an exercise plan tailored to the user's characteristics and provides visual guidance, making it adaptable to a wide range of users.

[0624] First, the user enters their basic information into the system using a terminal. This information is extensive and includes age, gender, current fitness level, past injury history, and specific fitness goals. The terminal sends this information to the server, which then creates an individual profile for the user.

[0625] The server generates an optimized exercise plan for the user based on the information it receives. An AI algorithm is involved in this process, determining the type of exercise, frequency, and intensity that matches the user's level and goals. For example, a beginner user might be offered short sessions that include low-intensity strength training.

[0626] The generated exercise plan is sent to the device, which then activates a hologram assistant based on it. The hologram assistant appears as a 3D image in front of the user, providing detailed instructions on how to perform the exercises. This makes it easier for the user to visually understand the correct form of the exercises.

[0627] The device also features sensors that detect user movements in real time. This allows it to collect data on the exercises the user is actually performing and send it to a server. The server analyzes this data to determine if the exercises are being performed correctly. If necessary, it provides feedback to the user via the device, instructing them on areas that need correction.

[0628] Furthermore, the server tracks the user's exercise progress and provides motivational information through the device. This information encourages users to continue exercising, gives them a sense of accomplishment, and helps them set their next goals. For example, a message such as, "You're making good progress today; let's try a little harder next time," might be displayed.

[0629] Thus, the system of the present invention provides an exercise experience tailored to each individual user, minimizing the risk of injury and supporting sustained exercise.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0633] Step 2:

[0634] The terminal sends the entered user information to the server to prepare data for creating the user's individual profile.

[0635] Step 3:

[0636] Based on the received user information, the server automatically generates an optimized exercise plan for the user using an AI algorithm. This plan includes the type of exercise, the number of repetitions, and the intensity level.

[0637] Step 4:

[0638] The server sends the generated motion plan to the terminal. The terminal receives this plan and prepares to activate the hologram assistant.

[0639] Step 5:

[0640] The device activates a hologram assistant, which displays a 3D image in front of the user. The hologram assistant visually demonstrates the correct form and procedure for the exercises to the user.

[0641] Step 6:

[0642] The device uses sensors to detect the user's movements in real time and collects movement data. This allows the system to understand how the user is exercising.

[0643] Step 7:

[0644] The device sends the collected motion data to the server. The server analyzes the data and determines whether the user's movements are correct.

[0645] Step 8:

[0646] The server generates feedback based on the results of the motion analysis, including advice for form correction and motivation as needed.

[0647] Step 9:

[0648] The device receives feedback from the server and provides it to the user. This allows the user to understand their exercise progress and make necessary adjustments immediately.

[0649] Step 10:

[0650] The server saves user progress information and incorporates it into future exercise plans. It also provides motivational information to help users evaluate their ability to continue exercising.

[0651] (Example 1)

[0652] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] Conventional exercise instruction systems have struggled to generate personalized exercise plans for each user and provide accurate, real-time feedback. Therefore, they have been unable to create an environment that allows users to continue exercising safely and effectively. Furthermore, they lacked effective means of motivating users to exercise.

[0654] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0655] In this invention, the server includes numerical analysis means for automatically generating an individualized exercise plan using a generation AI model based on user input information, stereoscopic image display means for visually providing exercise guidance based on the generated exercise plan, and motion detection means for detecting the user's movements and analyzing the progress of the exercise. This makes it possible to provide an exercise plan optimized for the user and to provide accurate feedback and motivational information in real time.

[0656] The "numerical analysis means" is a mechanism for automatically generating personalized exercise plans using an AI model based on user input information.

[0657] A "stereoscopic image display means" is a system that displays 3D images to visually provide exercise guidance to the user based on the generated exercise plan.

[0658] A "motion detection means" is a system that senses the user's physical movements in real time and analyzes the collected motion data.

[0659] An "information output method" is a method for providing users with analysis results and motivational information, and for conveying exercise feedback in real time.

[0660] This invention provides a system that allows users to receive personalized exercise guidance at home and exercise effectively and safely. The system primarily operates through the cooperation of a server and a terminal.

[0661] Users first enter their basic information using a terminal. This includes age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to a server and stored in a dedicated database.

[0662] The server uses the user's profile data to generate an optimal exercise plan through a generative AI model. The program uses an AI algorithm to determine the type, frequency, and intensity of exercise that matches each user's level and goals. An example of a prompt used in this process is, "Please create an exercise plan based on the user's profile."

[0663] The exercise plan generated on the server is sent to the terminal, which then activates a hologram assistant based on it. This hologram assistant appears before the user as a 3D image and provides detailed instructions on the exercise. This makes it easier for the user to visually learn the correct exercise form.

[0664] Furthermore, the device is equipped with sensors that detect user movements in real time. This allows data on the user's actual exercises to be transmitted to a server. The server analyzes this data and evaluates whether the movements are being performed correctly. If necessary, it provides feedback to the user through the device and instructs them to correct their exercise form.

[0665] In this way, the server and terminal work together to provide the user with an optimized exercise experience, ensuring user safety while supporting effective training.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] Users enter basic information using a terminal. This information includes a wide range of details such as age, gender, fitness level, injury history, and fitness goals. This information is sent from the terminal to the server. The server receives this data and stores the user's individual profile in a database.

[0669] Step 2:

[0670] The server generates an exercise plan using a generative AI model based on the user's profile information. Based on the input information, the generative AI model calculates the optimal exercise type, frequency, and intensity using the prompt message "Create an exercise plan based on the user's profile." As output, a personalized exercise plan is generated.

[0671] Step 3:

[0672] The server sends the generated exercise plan to the terminal. Based on the received exercise plan, the terminal activates a hologram assistant. The hologram assistant uses 3D images to visually explain to the user detailed instructions on how to perform the exercises. This makes it easy for the user to understand the correct exercise form.

[0673] Step 4:

[0674] The user performs exercises following the guidance of a hologram assistant. The device detects the user's movements in real time through its built-in sensors. The input data from the sensors is temporarily processed by the device and sent to the server as exercise data.

[0675] Step 5:

[0676] The server analyzes the received exercise data and evaluates whether the exercise is being performed correctly. Using an AI algorithm, it determines accuracy and generates feedback if improvement is needed. This feedback is sent to the device in real time and provided to the user.

[0677] Step 6:

[0678] The user modifies their exercise form based on feedback from the server. The device also displays motivational information tailored to the user's progress toward their goals, supporting continued training. This information includes messages praising the user's efforts and encouraging them to set new goals.

[0679] (Application Example 1)

[0680] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] In modern society, many individuals want to exercise efficiently and safely at home, but maintaining correct form and consistently training is difficult. Furthermore, they lack the feedback and motivation tailored to their individual fitness levels, resulting in insufficient solutions for their exercise needs. Therefore, it is necessary to provide a system that enables users to exercise appropriately by offering personalized exercise plans and providing visual, real-time exercise guidance and feedback.

[0682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0683] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance via holograms based on the generated exercise plan, and a detection means for detecting the user's physical movements and analyzing the progress of the exercise in real time. This enables the user to effectively and safely execute the exercise plan at home, maintain proper form while receiving real-time feedback, and train continuously.

[0684] The "calculation means" refers to a function that automatically generates an individualized exercise plan based on the user's input information.

[0685] A "display means" is a device that provides the generated exercise plan visually using a hologram and guides the user through exercise.

[0686] "Detection means" refers to a function that uses sensors to detect the user's physical movements and analyzes their progress in real time.

[0687] The "output mechanism" is a mechanism for providing users with real-time guidance on correcting their exercise form based on the analysis results.

[0688] A "data processing system" is a system that continuously learns the user's exercise history and executes a process to optimize the next exercise plan.

[0689] This invention provides a system that allows users to exercise safely and effectively at home. Specifically, a server uses an AI algorithm to automatically generate a personalized exercise plan from the user's input information. First, the user inputs information such as age, gender, fitness level, injury history, and fitness goals into a terminal. The terminal sends this information to the server, which then generates an exercise plan optimized for the user based on the received information.

[0690] The generated plan is sent to the terminal and presented to the user as a 3D hologram. This allows the user to visually understand the correct form of the exercise. The terminal is equipped with sensors to detect the user's body movements in real time. This data is sent to a server, where an AI algorithm analyzes the progress of the exercise. The server determines whether the exercise is being performed correctly and, if necessary, provides guidance to correct the exercise form to the user via the terminal.

[0691] Furthermore, the server stores the user's exercise history and processes the data to optimize the next exercise plan. The hardware used includes a physical assistant robot with holographic display capabilities and Microsoft's skeletal tracking sensors. The software utilizes AI algorithms such as TensorFlow and PyTorch, and a real-time feedback system using OpenCV.

[0692] For example, if a user wants to start a beginner-level fitness program, the system suggests a short, low-intensity exercise session for first-time users. Movements are detected in real time, and specific feedback such as "straighten your back a little more" is provided through the device.

[0693] An example of a prompt for a generating AI model is: "I am a 47-year-old male with a history of knee surgery and an intermediate fitness level. Please suggest an exercise plan that will allow me to improve my fitness steadily and without overexertion." In this way, users can effectively achieve their fitness goals through a personalized exercise experience.

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The user enters basic information (age, gender, fitness level, injury history, fitness goals) using their device. This information is sent from the device to the server and prepared as data necessary for processing by the AI ​​algorithm. As a result, a user profile is generated.

[0697] Step 2:

[0698] The server executes an AI algorithm based on the user profile to automatically generate a personalized exercise plan. The type, frequency, and intensity of the exercise are determined based on the input information. The generated exercise plan is provided as output.

[0699] Step 3:

[0700] The device that receives the exercise plan activates a hologram display and presents the exercise method using 3D images in front of the user. The content is displayed visually based on the entered exercise plan.

[0701] Step 4:

[0702] Sensors built into the device detect the user's body movements in real time. Based on the input data from the sensors, the server analyzes the user's movement progress and evaluates the accuracy of their posture and movements. This analysis determines whether the user's movements are being performed appropriately.

[0703] Step 5:

[0704] Based on the analysis results, the server provides real-time feedback on how to correct or improve exercise form. This feedback is transmitted to the user via the terminal, and specific instructions such as "Raise your arms a little higher" are output.

[0705] Step 6:

[0706] The server learns the user's exercise history and processes the data to optimize the next exercise plan for better suitability. Here, past exercise data is used as input, and the generating AI model is ready to output an improved exercise plan for future training.

[0707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0708] This invention adds emotion recognition functionality to a system that allows users to safely continue exercising at home, further personalizing and improving the traditional fitness experience. This system provides an automatically generated exercise plan based on the user's personal information, real-time feedback, and also recognizes the user's emotional state, reflecting that information in the exercise and feedback.

[0709] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device sends this information to the server to create a personalized profile for the user.

[0710] The server uses an AI algorithm to generate a personalized exercise plan based on user information. The generated plan is sent to the terminal, which then activates a hologram assistant. The hologram assistant shows the correct form for each exercise and provides visual guidance.

[0711] This system also incorporates an emotion engine. The device detects the user's emotional state in real time through facial recognition technology and voice analysis. This emotional data is sent to a server and used to inform exercise plans and feedback.

[0712] For example, if the system detects that a user is experiencing stress, the emotion engine will temporarily reduce the intensity of the exercise and recommend relaxation-focused exercises. Furthermore, if a decrease in motivation is detected, the system will provide encouraging words and feedback tailored to the user's preferences through a hologram assistant.

[0713] The device detects the user's movements in real time and sends motion and emotional data collected by sensors to a server. The server analyzes this data and generates accurate form and emotionally responsive feedback. Emotion-based feedback personalizes the user's fitness experience and maximizes the effectiveness of their workout.

[0714] This system allows users to exercise according to their physical and psychological needs, enhancing fitness while reducing the risk of injury.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] Users enter their basic information into the device. This information includes age, gender, fitness level, injury history, and fitness goals.

[0718] Step 2:

[0719] The terminal sends the input information to the server and prepares the data for creating the user's individual profile.

[0720] Step 3:

[0721] The server uses AI algorithms to automatically generate personalized exercise plans based on the user's profile.

[0722] Step 4:

[0723] The server sends the generated motion plan to the terminal, and after the terminal receives the plan, it activates the hologram assistant.

[0724] Step 5:

[0725] The hologram assistant is displayed to the user, visually guiding them through the correct form and procedure of exercise.

[0726] Step 6:

[0727] The device uses facial recognition sensors and voice analysis functions to detect the user's emotional state in real time.

[0728] Step 7:

[0729] The device sends detected emotion data to the server, which then dynamically adjusts the exercise plan and feedback content based on this data.

[0730] Step 8:

[0731] The server generates and sends feedback that reflects emotional data to the terminal. The feedback includes content tailored to the user's emotional state.

[0732] Step 9:

[0733] The device provides users with emotionally responsive feedback through a holographic assistant, increasing their motivation to exercise.

[0734] Step 10:

[0735] User data regarding exercise and emotions is regularly saved to the server and used to plan future exercise sessions.

[0736] (Example 2)

[0737] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0738] Conventional fitness systems can adapt to some extent to a user's physical condition, but they often fail to adequately address the user's emotional state, resulting in uniform exercise plans and feedback. This has made it difficult for users to receive appropriate guidance and feedback that responds to changes in stress and motivation. Therefore, the present invention aims to provide a personalized fitness experience that takes the user's emotional state into account, thereby enhancing the effectiveness of exercise while maintaining motivation for continuous exercise.

[0739] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0740] In this invention, the server includes information processing means for automatically generating an individualized exercise plan based on user input information, display means for visually providing exercise guidance based on the generated exercise plan, detection means for detecting the user's exercise and analyzing the progress of the exercise, output means for adjusting feedback based on the analysis results and the user's emotional state, and emotion analysis means for recognizing the user's emotions and reflecting that information in the exercise plan. This enables the provision of appropriate fitness guidance and feedback that takes the user's emotional state into consideration in real time, optimizing the effectiveness of exercise and improving motivation.

[0741] "Information processing means" refers to a device or system that processes data corresponding to a specific purpose based on user input information and automatically generates an individualized exercise plan.

[0742] "Display means" refers to a device or interface that visually outputs information based on the generated exercise plan and provides exercise guidance to the user.

[0743] A "detection means" is a device or system that collects the user's movements and actions in real time using sensors and analyzes their progress.

[0744] "Output means" refers to a device or function that provides the user with adjusted feedback based on the analyzed results and the user's emotional state.

[0745] An "emotion analysis tool" is a device or system that recognizes a user's emotional state from their facial expressions, voice, etc., and uses that information to inform their movement plan.

[0746] This invention is a system that allows users to exercise safely at home and incorporates an emotion recognition function. The system generates an individualized exercise plan based on user information and analyzes emotional states in real time, reflecting this in the feedback to provide a more personalized fitness experience.

[0747] Users enter their profile information using their device. This information includes age, gender, fitness level, injury history, and fitness goals. The device then sends this information to the server.

[0748] The server uses an AI algorithm to generate an individualized exercise plan based on the received user information. This generated AI model automatically creates an optimal plan that matches the user's needs. This exercise plan is sent to the device, which then activates a hologram assistant based on the plan.

[0749] The hologram assistant visually demonstrates the correct exercise form to the user and provides exercise guidance. The device also uses emotion analysis technology, including facial recognition and voice analysis, to detect the user's emotional state. This emotion data is transmitted to a server in real time.

[0750] The server integrates and analyzes behavioral and emotional data to generate feedback that takes the user's emotional state into account. For example, if the user is stressed, the emotional engine will recommend exercises with reduced intensity. If motivation is low, it can also provide encouraging messages or feedback based on the user's preferences.

[0751] This system allows users to engage in exercise that meets their physical and mental needs, maximizing fitness benefits and maintaining sustained motivation.

[0752] Examples of prompts include "Generate an exercise plan based on the user profile" and "Provide feedback tailored to specific emotional states."

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] Users enter their profile information on their home device. This information includes age, gender, fitness level, injury history, and fitness goals. The device receives this information as input data and sends it to the server. The data is encrypted and transmitted securely.

[0756] Step 2:

[0757] The server supplies the received user information as input to the AI ​​algorithm. This algorithm uses a generative AI model to automatically generate an individualized exercise plan based on the user's needs and goals. The generated exercise plan is sent to the terminal as output. High-precision personalization is achieved by utilizing the AI ​​model.

[0758] Step 3:

[0759] The device activates a hologram assistant based on the received exercise plan. The hologram assistant provides visual guidance, showing the user the correct form for the exercises according to the plan. This is important as part of visual feedback.

[0760] Step 4:

[0761] The device detects the user's emotional state in real time by capturing the user's face with a camera and picking up their voice with a microphone. The data obtained through facial recognition and voice analysis is used as input data for emotional state detection. The detected emotional data is sent to a server.

[0762] Step 5:

[0763] The server analyzes user motion data (obtained from sensors) and emotional data as input. Through this data processing, it generates output data that adjusts the exercise plan and feedback, taking the user's emotional state into account.

[0764] Step 6:

[0765] The device provides real-time feedback to the user based on analytical data sent from the server. This feedback includes encouraging messages tailored to the user's emotional state, adjustments to exercise intensity, and specific guidance to improve the user experience. In this step, the feedback content is customized to enhance user motivation and reduce stress.

[0766] (Application Example 2)

[0767] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0768] In recent years, with the rise of health consciousness, home exercise habits have gained attention. However, if exercise plans do not adapt to the user's physical condition or emotions, it can lead to decreased motivation and an increased risk of injury. Furthermore, conventional fitness systems lack the ability to dynamically reflect the user's emotions when suggesting exercises, making it difficult to provide a personalized exercise experience.

[0769] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0770] In this invention, the server includes a calculation means for automatically generating an individualized exercise plan based on user input information, a display means for visually providing exercise guidance based on the generated exercise plan, a detection means for detecting the user's movements and analyzing the progress of the exercise, an analysis means for recognizing the user's emotional state and reflecting that information in the exercise plan, and an output means for providing real-time feedback adjusted based on the user's emotions. This enables the provision of an exercise plan and feedback adapted to the user's physical and emotional state, resulting in an individualized fitness experience.

[0771] A "user" is an individual who uses the system to experience fitness.

[0772] "Input information" refers to data provided by the user, such as age, gender, fitness level, injury history, and fitness goals.

[0773] A "personalized exercise plan" is a program that suggests a specific exercise menu based on the user's input information.

[0774] "Calculation means" refers to a computing device or program designed to generate an optimal motion plan from user input information.

[0775] "Display means" refers to a device or method for visually providing exercise instructions based on a generated exercise plan.

[0776] "Detection means" refers to a device or technology for understanding a user's physical movements in real time and evaluating their progress and accuracy.

[0777] "Analysis means" refers to a system that analyzes data obtained from detection means and the user's emotional state to generate appropriate exercise plans and feedback.

[0778] "Emotional state" refers to the psychological condition inferred from the user's facial expressions, voice, and other factors.

[0779] "Real-time feedback" refers to guidance and encouragement provided in immediate response to a user's exercise.

[0780] "Output means" refers to a device or program that provides information or instructions to the user based on the results of the analysis.

[0781] The system for implementing this invention provides a method for creating an individualized exercise plan based on information input by the user and adjusting it according to the user's emotional state.

[0782] Users enter personal information such as age, gender, fitness level, injury status, and fitness goals into a terminal. This information is sent to a server, which uses AI algorithms to perform calculations and generate a personalized exercise plan. This generation process utilizes AI frameworks known as data analysis technologies. Specific examples include libraries such as TensorFlow and PyTorch.

[0783] The generated motion plan is visually presented to the user through holograms or displays. Projector devices or AR-enabled displays can be used to provide visual information.

[0784] The user's actual movements are monitored in real time using detection methods that utilize cameras and sensors. Cameras can include depth sensors such as Intel RealSense. Furthermore, tools such as OpenCV and the Emotion API are used to analyze the user's facial expressions and voice to determine their emotional state.

[0785] Based on this detection data and user sentiment information, the server adjusts the exercise plan and real-time feedback, providing it to the user through output devices. The feedback includes voice guidance and encouragement using speech synthesis technology such as Google Text-to-Speech.

[0786] For example, if a user notices they are feeling a bit down today, the system might suggest a gentle yoga routine. The prompt for the generating AI model might look something like, "What kind of exercise have you been doing lately? Today, let's make time to relax and pamper yourself."

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] Users use a device to input information such as their age, gender, fitness level, injury history, and fitness goals. This information is retrieved by the device and sent to the server. It is crucial that the input information is accurately transmitted to the server.

[0790] Step 2:

[0791] The server generates a personalized exercise plan using an AI algorithm based on the received user information. During this process, it performs calculations based on the input information and extracts the optimal exercises from a database. The calculation results are output as a plan and sent back to the terminal.

[0792] Step 3:

[0793] The terminal receives the personalized exercise plan transmitted from the server and presents it visually to the user using a display device. At this time, a projector device or AR display is used to visually show the exercise content.

[0794] Step 4:

[0795] The user's movements are detected in real time by cameras and sensors connected to the device. This allows the device to input the user's movement data and understand their actual movement patterns. Emotion recognition is also performed simultaneously, analyzing the user's emotional state from their facial expressions and voice.

[0796] Step 5:

[0797] The server receives exercise and emotional data transmitted from the terminal and analyzes them using AI. This allows it to evaluate whether the exercise is being performed as planned and what the emotional state is. By synthesizing the data, it monitors the progress of the exercise and changes in emotions.

[0798] Step 6:

[0799] Based on the analysis results, the server generates real-time feedback adapted to the user's emotional state. This feedback is output as voice from the device using speech synthesis technology. Specifically, it outputs encouraging words when motivation is low.

[0800] Step 7:

[0801] Users receive real-time feedback to adjust their exercise routine and form, thus continuing their fitness experience. The personalized experience is provided through feedback that takes into account the user's emotions and exercise progress.

[0802] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0803] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0804] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0805] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0806] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0807] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0808] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0809] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0810] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0811] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0812] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0813] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0814] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0815] 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.

[0816] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0817] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0818] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0819] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0820] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0821] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0822] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A calculation means for automatically generating an individualized exercise plan based on user input information,

[0826] A display means that visually provides exercise guidance based on the generated exercise plan,

[0827] A detection means for detecting the user's movements and analyzing the progress of those movements,

[0828] An output means that provides real-time feedback to the user based on the analysis results,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein a detection means detects the user's body movements in real time, and an analysis means evaluates the accuracy of the movements based on those movements.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the output means provides motivational information based on the user's goal achievement status to support the user's continued participation.

[0834] "Example 1"

[0835] (Claim 1)

[0836] Numerical analysis means that automatically generates an individualized exercise plan using an AI model based on user input information,

[0837] A 3D image display means that provides exercise guidance visually based on the generated exercise plan,

[0838] A motion detection means that detects the user's movements and analyzes the progress of those movements,

[0839] An information output means that provides real-time feedback to the user based on the analysis results,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, wherein a motion detection means detects the user's physical movements in real time, and a numerical analysis means evaluates the accuracy of the movements based on those movements.

[0843] (Claim 3)

[0844] The system according to claim 1, wherein the information output means provides motivational information based on the user's goal achievement status to support the user's continued participation.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] A calculation means for automatically generating an individualized exercise plan based on user input information,

[0848] A display means that visually provides exercise guidance using holograms based on the generated exercise plan,

[0849] A detection means that detects the user's body movements and analyzes the progress of the exercise in real time,

[0850] An output means that provides real-time corrections to the user's exercise form based on the analysis results,

[0851] A data processing means for learning the user's exercise history and optimizing the next exercise plan,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein a detection means detects the user's body movements in real time, and an AI algorithm uses the detection data to evaluate the accuracy of the movements.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the output means generates motivational information in natural language based on the user's goal achievement status, thereby supporting the user's continued participation.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] Information processing means that automatically generates an individualized exercise plan based on user input information,

[0860] A display means that visually provides exercise guidance based on the generated exercise plan,

[0861] A detection means for detecting the user's movements and analyzing the progress of those movements,

[0862] An output means that adjusts the feedback based on the analysis results and the user's emotional state,

[0863] A means of emotion analysis that recognizes the user's emotions and reflects that information in the exercise plan,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, wherein a detection means detects the user's physical movements in real time, and an analysis means evaluates the accuracy of the movements and the emotional state based on those movements.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the output means provides motivational information based on the user's goal achievement status and emotional state, thereby supporting the user's continued participation.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] A calculation means that automatically generates an individualized movement plan based on user input information,

[0872] A display means that visually provides exercise guidance based on the generated exercise plan,

[0873] A detection means for detecting the user's movements and analyzing the progress of those movements,

[0874] An analytical tool that recognizes the user's emotional state and reflects that information in the exercise plan,

[0875] An output means that provides real-time feedback adjusted based on the user's emotions,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, wherein a detection means detects the user's body movements in real time, and an analysis means evaluates the accuracy of the movements based on those movements.

[0879] (Claim 3)

[0880] The system according to claim 1, wherein the output means provides motivational information based on the user's goal achievement status and emotional state, thereby supporting the user's continued participation. [Explanation of symbols]

[0881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A calculation means for automatically generating an individualized exercise plan based on user input information, A display means that visually provides exercise guidance using holograms based on the generated exercise plan, A detection means that detects the user's body movements and analyzes the progress of the exercise in real time, An output means that provides real-time corrections to the user's exercise form based on the analysis results, A data processing means for learning the user's exercise history and optimizing the next exercise plan, A system that includes this.

2. The system according to claim 1, wherein a detection means detects the user's body movements in real time, and an AI algorithm uses the detection data to evaluate the accuracy of the movements.

3. The system according to claim 1, wherein the output means generates motivational information in natural language based on the user's goal achievement status, thereby supporting the user's continued participation.

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