system

Abstract

We provide the system. [Solution] A means of obtaining the user's biometric parameters, A means for generating an individualized motor plan based on the biological parameters using a generation program, A means for presenting the aforementioned movement plan through an augmented reality device, monitoring the user's physical movements according to the instructions, and providing appropriate modifications, Means for analyzing the results of the aforementioned physical movements and adjusting the aforementioned movement 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, with the increasing awareness of health, the demand for fitness has been increasing. However, there is a problem that general fitness programs are uniform and lack sufficient optimization according to the physical characteristics and health goals of individual users. In addition, for many people who cannot afford expensive personal training, the means to experience efficient and effective training are limited. Furthermore, there is a need for an attractive method for users to maintain motivation and continuously manage their health.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that acquires user attribute data and objective data, and generates personalized exercise plans based on this data using artificial intelligence. Furthermore, it aims to maintain user motivation by presenting the exercise plan to the user using a virtual environment device, monitoring movements during training, and providing appropriate feedback in real time. In addition, by providing a function to analyze training results and continuously adjust and optimize the exercise plan based on those results, it provides an effective fitness experience that meets the individual needs of the user.

[0006] A "user" refers to an individual who provides attribute data and purpose data when using a system.

[0007] "Attribute data" refers to data that includes information such as the user's physical characteristics, age, and gender.

[0008] "Purpose data" refers to data that indicates the health and fitness goals that the user intends to achieve.

[0009] "Generative artificial intelligence" refers to artificial intelligence technology used to generate optimal fitness plans based on data provided by users.

[0010] A "personalized exercise plan" refers to a training schedule and exercise content customized based on the user's attribute data and goal data.

[0011] A "virtual environment device" refers to a device or system used by a user to perform training in a virtual reality environment.

[0012] "Monitoring movements" refers to recording and evaluating the movements of the exercises performed by the user.

[0013] "Feedback" refers to the evaluation and guidance a user receives regarding their actions during training.

[0014] "Analyzing results" refers to analyzing data related to user training and extracting useful information from it. [Brief explanation of the drawing]

[0015] [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a 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.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] In order to implement the present invention, it is necessary to construct the following system. This system consists of a user, a terminal, and a server, and each element works in cooperation with one another.

[0037] First, the user enters their personal data (height, weight, age, etc.) and health goals (e.g., weight loss, muscle gain) into the device. The device receives this data and then transmits it to the server using a secure communication method.

[0038] Next, the server executes a generative artificial intelligence program based on the received user attribute data and health goals. This program designs an optimal exercise plan for the user, determining the content, frequency, and intensity of exercise according to the user's characteristics. This generated plan is then sent to the terminal by the server.

[0039] Next, the terminal prepares the VR environment based on the exercise plan received from the server. The user experiences the training through a VR headset or similar device using the virtual environment equipment. The terminal uses sensors to monitor the user's movements in real time and provide appropriate feedback. For example, if the user is not following the training instructions or has poor posture, a virtual instructor in the VR environment will give corrective instructions.

[0040] After the training session ends, the device sends the user's activity history and training results (e.g., calories burned, achievement of exercise schedule) back to the server. The server then analyzes these results and adjusts the exercise plan to reflect the findings in the next training session.

[0041] For example, if a 30-year-old user starts a program with the goal of losing 5 kilograms, the system will suggest a plan primarily focused on aerobic exercise, tailored to the user's basal metabolic rate and physical level. In the VR environment, simulations of running and arm exercises are displayed, and the system evaluates the user's movements in real time to provide an optimized fitness experience.

[0042] In this way, this system advances the personalization of fitness and provides users with a means to effectively and efficiently achieve their health goals.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users create an account using their device and enter their attribute data and health goals. The entered data includes height, weight, age, gender, and health goals (e.g., weight loss or muscle building).

[0046] Step 2:

[0047] The terminal receives the entered user data and verifies its format. It then sends the verified data to the server using a secure communication protocol.

[0048] Step 3:

[0049] The server stores the received user attribute data and health goals in a database and invokes a generative artificial intelligence (AI) based on this data. The AI ​​analyzes the data and generates a personalized exercise plan.

[0050] Step 4:

[0051] The server sends the generated exercise plan to the terminal and configures it to build a virtual environment that the user can use.

[0052] Step 5:

[0053] The device prepares a VR environment based on the exercise plan, allowing the user to begin training. Exercises are guided by a virtual instructor.

[0054] Step 6:

[0055] Users wear a VR headset and perform training in a virtual environment. During training, sensors monitor their movements and provide real-time feedback.

[0056] Step 7:

[0057] The device sends activity data collected during the training session to a server. This server records data such as calories burned and exercise achievement.

[0058] Step 8:

[0059] The server analyzes the collected data and adjusts the exercise plan for more effective planning of the next training session. As a result, a report is generated that allows the user to check their progress.

[0060] Step 9:

[0061] Users can check their training results on their device and receive advice on planning their next workout and areas for improvement. This allows users to continuously adjust their training towards their goals.

[0062] (Example 1)

[0063] 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."

[0064] In today's busy lifestyle, it's difficult for individuals to find the optimal fitness plan and efficiently achieve their health goals. Therefore, there's a need for a system that provides personalized exercise plans tailored to individual physical conditions and goals, and supports their implementation through a virtual environment, thereby effectively achieving health objectives.

[0065] 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.

[0066] In this invention, the server includes means for acquiring the user's biometric information and health goals, means for generating personalized exercise instructions based on the biometric information and health goals using an AI model generated by an information processing device, and means for presenting the exercise instructions through a virtual reality device, monitoring the user's actions, and providing feedback. This enables the provision of an optimal fitness plan tailored to the user's characteristics and real-time feedback according to the practice status.

[0067] A "user" is an individual who uses the system to achieve their own health goals.

[0068] "Biometric information" refers to data that indicates a user's physical attributes, including height, weight, and age.

[0069] "Health goals" refer to specific fitness and health-related objectives set by the user, such as weight loss or muscle gain.

[0070] An "information processing device" is a device that analyzes data and generates appropriate movement instructions.

[0071] A "generative AI model" is a program that includes artificial intelligence technology to design optimal exercise instructions for a user based on their biometric information and health goals.

[0072] A "virtual reality device" is a device used by users to experience exercise in a virtual environment, and includes headsets and the like.

[0073] "Exercise instructions" refer to fitness activity plans created by a generative AI model, including details such as content, frequency, and intensity.

[0074] "Feedback" refers to information provided to improve the accuracy and effectiveness of the user's exercise, and includes real-time guidance.

[0075] This invention is a system that generates personalized exercise plans and provides users with an optimal fitness experience. The system mainly consists of three elements: the user, the terminal, and the server, each of which works in cooperation with the others.

[0076] First, the user enters their biometric information (e.g., height, weight, age) and health goals (e.g., weight loss, muscle gain) into the device. The device then transmits this information to the server using a secure communication method (e.g., HTTPS).

[0077] Next, the server uses an information processing device to run a generative AI model, generating personalized exercise instructions based on the user's biometric information and health goals. This generative AI model considers past fitness data and the user's specific requests to determine the optimal exercise content, frequency, and intensity. For example, the AI ​​model might be given the prompt, "Generate an exercise plan for a 30-year-old male, 70kg, 180cm tall, aiming to lose 5kg."

[0078] Next, the terminal prepares a virtual reality environment for the user based on the exercise instructions received from the server. Using a virtual reality device (e.g., a VR headset), the user can then experience the training. The terminal uses sensors to monitor the user's movements in real time and verify whether the exercise is being performed as planned.

[0079] Feedback is provided after monitoring and analyzing the user's actions in detail. For example, if the user has poor posture or performs actions different from the instructions, a virtual instructor in the virtual reality environment will provide corrective instructions.

[0080] Finally, once the exercise session is complete, the device sends the user's exercise history and results to the server. The server uses this data to further improve exercise instructions for the next session.

[0081] In this way, this system provides users with a series of programs to effectively achieve their health goals, optimizing fitness according to their individual health status and objectives.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user enters their biometric information and health goals into the terminal. The terminal sends this input data to the server using a protocol. The input includes the user's height, weight, age, and health goals, and the output is data transmitted in a secure format. This data is prepared for further processing on the server.

[0085] Step 2:

[0086] The server receives biometric information and health goals transmitted from the terminal and uses an information processing device to activate a generating AI model. Based on the input data, the AI ​​model generates personalized exercise instructions. Here, prompts are also useful, and the AI ​​model is instructed in the form of, "Generate an exercise plan for a 30-year-old man aiming to lose 5 kg." As output, it generates an exercise plan optimized for the user.

[0087] Step 3:

[0088] Upon receiving exercise instructions from the server, the terminal sets up the virtual reality environment. Specifically, it starts the VR headset and loads the virtual training environment for the user. The input is the exercise plan data, and the output is the completion of the virtual environment for the user to experience.

[0089] Step 4:

[0090] The device monitors the user's movements through a virtual reality environment. It uses sensors to acquire movement data in real time and verifies whether the user is following the movement instructions. Input is movement data obtained from the sensors, and output is appropriate feedback based on that data. If posture is poor, a virtual instructor automatically provides corrective instructions.

[0091] Step 5:

[0092] The device sends the results of the training session to the server. Here, performance data such as exercise history and calories burned are uploaded to the server. The server uses the transmitted data to improve the exercise instructions for the next session. The input is the training result data, and the output is stored as data used to adjust the next exercise plan.

[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] Providing individualized rehabilitation exercise plans and ensuring a safe and effective fitness experience for the elderly and those requiring rehabilitation is a challenge. Traditional fitness plans often offer generic programs that fail to adequately address the individual conditions and goals of users. Therefore, there is a need for methods that allow users to perform rehabilitation and exercise at a more appropriate pace.

[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 means for acquiring the user's biometric parameters, means for generating an individualized exercise plan based on the biometric parameters using a generation program, means for presenting the exercise plan through an augmented reality device, monitoring the user's physical movements according to instructions, and providing appropriate modifications, and means for analyzing the results of the physical movements and adjusting the exercise plan. This enables the user to safely and effectively engage in a fitness experience tailored to their individual health goals.

[0098] "User biometric parameters" refer to individually measured information such as the user's age, weight, body composition, and health status.

[0099] A "generative program" is software or an algorithm used to design an optimal exercise plan based on biological parameters and the user's goals.

[0100] An "individualized exercise plan" refers to a fitness plan designed to suit each individual user, taking into account their characteristics and goals.

[0101] An "augmented reality device" is hardware that allows users to experience training in a virtual environment, and typically refers to a head-mounted display or similar device.

[0102] "Monitoring of physical movement" is the process of observing and recording a user's movements and actions in real time using sensors, cameras, and other devices.

[0103] "Adjusting the exercise plan" is the process of modifying and optimizing the content and intensity of exercises in accordance with the user's progress and changes in their physical condition.

[0104] In implementing this invention, the server receives the user's biometric parameters. The terminal transmits data to the server using a secure communication method based on the information entered by the user. Using the user's biometric parameters such as age, weight, and health status, the generating program designs an optimal exercise plan. A generating AI model is utilized, enabling the program to provide personalized exercise guidance.

[0105] The server sends the generated exercise plan back to the terminal, which then displays the plan on an augmented reality device. Specifically, a head-mounted display is used to allow the user to experience the exercise in a virtual environment. The terminal utilizes sensors to monitor the user's physical movements in real time and determine whether the exercises are being performed correctly. If necessary, a virtual instructor provides feedback and prompts corrections to the form.

[0106] When a user finishes an exercise session, the device resends the exercise history and performance data to the server. Based on this, the server analyzes the received data and refines the next exercise plan. As a specific example, the generative AI model proposes personalized exercises in program statements that are tailored to the user's daily progress and physical condition.

[0107] An example of a prompt statement is as follows:

[0108] "Please generate a safe rehabilitation exercise plan for seniors based on user data. The exercises should focus on improving balance and strengthening leg muscles, and be suitable for home training."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server receives biometric parameters and target data sent from the user via the terminal. These biometric parameters include information such as age, weight, and health status. Based on this data, the AI ​​model is prepared to design an exercise plan. This input prepares the system to generate an optimal exercise plan for the user.

[0112] Step 2:

[0113] The server uses a generative AI model to design a personalized exercise plan based on the received biometric parameters and target data. This AI model processes the parameters as input and determines the exercise content, frequency, and intensity according to the user's characteristics and goals. The resulting exercise plan is output. Specifically, the user selects from the various exercise combinations contained in the exercise plan.

[0114] Step 3:

[0115] The terminal receives the generated exercise plan and presents it to the user via an augmented reality device. A head-mounted display is used here, allowing the user to experience the exercise within a virtual environment. The output provides the user with instructions to begin the exercise. In this step, the virtual environment is visually and audibly set up and adjusted to allow the user to execute the prepared exercise plan.

[0116] Step 4:

[0117] The device uses sensors to monitor the user's physical movements in real time while they are exercising. If the user's movements deviate from the exercise plan, a virtual instructor provides corrective feedback to encourage improvement. This monitoring process takes data from the sensors as input and compares it to the exercise plan. Corrective instructions are generated as output.

[0118] Step 5:

[0119] After the exercise is completed, the device sends the user's exercise history and performance data to the server. This includes calories burned and the degree to which the exercise schedule was achieved. The server uses this data to analyze and further refine the next exercise plan. In this process, the input exercise history data is used as foundational data to adjust the next exercise plan. The generating AI model runs again, and an updated exercise plan is output.

[0120] 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.

[0121] This invention relates to a system that acquires user attribute data and objective data, and generates and optimizes personalized exercise plans using generative artificial intelligence and an emotion engine. The system consists of a user, a terminal, and a server.

[0122] First, the user uses their device to input their basic information (height, weight, age, gender, etc.) and health goals (e.g., weight loss, muscle gain). The device then sends this data to the server.

[0123] The server analyzes the received attribute and target data, and executes a generation artificial intelligence program to create an optimal exercise plan for the user. During this process, the emotion engine adjusts the plan, taking into account the user's current emotional state. The emotion engine infers emotions from sensor data and user input, and evaluates motivation and stress levels for exercise.

[0124] The generated exercise plan is sent to the device and set up so that the user can experience it in a virtual environment. The device uses a VR headset and other sensory feedback devices to provide the user with a realistic training experience. The device also feeds back the user's emotional changes during exercise to an emotion engine in real time, and instantly fine-tunes the intensity and content of the training based on that data.

[0125] As a concrete example, consider the case of a 30-year-old user who sets a goal of "losing 5 kilograms." The system tracks the user's emotional state, and if the stress level rises during exercise, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. On the other hand, if the user is positive and energetic about exercising, the system moderately increases the exercise intensity and provides a challenging plan.

[0126] After the session ends, the server analyzes the collected emotional data and exercise results to incorporate them into the next fitness plan for further optimization. A meal plan is also generated, providing comprehensive guidance to help users achieve their health goals more quickly.

[0127] This system allows users to have an efficient and personalized fitness experience that takes into account not only their physical but also their emotional state. This interactive and flexible training environment helps to achieve greater results and maintain motivation.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users create an account using their device and enter attribute data (e.g., height, weight, age) and goal data (e.g., weight loss, muscle gain). This data serves as the foundational information necessary for the user to achieve their fitness goals.

[0131] Step 2:

[0132] The terminal verifies attribute and objective data obtained from the user and transmits it to the server using a secure communication method. The transmitted data is integrated information including the user's characteristics and goals.

[0133] Step 3:

[0134] The server utilizes generative artificial intelligence to generate an optimized exercise plan for the user based on the received data. Here, the type, duration, and frequency of exercise are determined according to the user's physical characteristics and goals.

[0135] Step 4:

[0136] During the generation process, the server uses an emotion engine to analyze the user's emotional data and modifies the exercise plan based on this. If the user is in a high-stress state, it suggests exercises that promote relaxation; if the user is in a positive emotional state, it adds challenging exercises.

[0137] Step 5:

[0138] The server sends the final version of the plan to the device, which then prepares the training in the VR environment based on it. The user then puts on the VR headset and is ready to participate in the guided session.

[0139] Step 6:

[0140] The user begins training in a VR environment, and their movements are monitored in real time by the device. The device uses sensors to detect changes in the user's emotions along with their movements and sends the data to the emotion engine.

[0141] Step 7:

[0142] The device updates the exercise content and feedback as needed based on input from the emotion engine. For example, if the user starts to feel fatigued, measures such as slowing down the pace of the instructions are taken.

[0143] Step 8:

[0144] After the training session ends, the server analyzes the behavioral and emotional data reported from the terminal. Based on the results, it adjusts the next exercise plan and provides feedback to the user on their progress and areas for improvement.

[0145] Step 9:

[0146] Furthermore, the server also suggests meal plans that contribute to the user's health goals, supporting the overall fitness experience. These plans are regularly reviewed and continuously optimized by artificial intelligence.

[0147] (Example 2)

[0148] 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".

[0149] Traditional exercise plan generation systems typically generated exercise plans based on the user's physical attributes, but did not consider the user's emotional state. As a result, it was difficult to maintain user motivation and provide a personalized fitness experience. Furthermore, the lack of comprehensive coordination between exercise and meal plans limited the user's ability to achieve their health goals.

[0150] 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.

[0151] In this invention, the server includes means for acquiring user attribute information and purpose information, means for generating an individualized exercise plan based on the attribute information and purpose information using artificial intelligence, and means for evaluating the user's emotional state through emotion analysis and adjusting the exercise plan accordingly. This makes it possible to provide a flexible exercise plan that responds to the user's emotional state.

[0152] "User attribute information" refers to basic personal information about the user, such as height, weight, age, and gender.

[0153] "Purpose information" refers to information about the health and fitness goals that the user intends to achieve.

[0154] "Generative artificial intelligence" refers to a computer program that has the ability to automatically create specific plans and recommendations based on the user's attribute information and objective information.

[0155] An "exercise plan" refers to a schedule of specific exercises and workouts created based on the user's health goals.

[0156] "Emotional analysis" refers to the process of using sensor data and other input data to evaluate a user's emotional state and infer their mental state.

[0157] A "nutrition plan" refers to a set of dietary and nutritional guidance guidelines created to support users in achieving their health goals.

[0158] A "learning algorithm" refers to a computer algorithm that has the ability to learn from data and use the results to continuously improve and optimize its plans.

[0159] This invention is configured as a system that provides a personalized fitness experience for users. The system mainly consists of a user, a terminal, and a server.

[0160] First, the user uses their device to input basic attribute information (e.g., height, weight, age, gender) and health goals they wish to achieve (e.g., weight loss, muscle gain). The device then transmits this information to the server via a secure communication protocol.

[0161] The server uses a database management system and data cleansing algorithms to analyze the received data. The generated artificial intelligence model creates an optimal exercise plan from attribute information and objective information. In this process, the server uses an emotion analysis engine to evaluate the user's emotions. Specifically, it uses information obtained from sensors and input data, such as heart rate and voice data, to infer the user's current emotional state.

[0162] The generated exercise plan is organized in XML or JSON format and sent to the terminal. The terminal uses a VR headset and sensory feedback devices to create an environment where the user can experience exercise in a virtual environment. During this process, the terminal feeds back the user's real-time emotional changes to the server, and the exercise plan is adjusted as needed.

[0163] As a concrete example, consider a case where a 30-year-old user sets a goal of "losing 5 kilograms." In this case, the system tracks the user's emotional state during exercise, and if an increase in stress levels is detected, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. If a positive and energetic state is maintained, the system increases the exercise intensity and provides a more challenging plan.

[0164] An example of a prompt sentence for a generative AI model is, "How can we optimize an exercise plan for a 30-year-old user aiming to lose 5 kg, taking into account the emotional engine?" In this way, the system provides a personalized plan that takes into account the user's physical and emotional state, helping them achieve their health goals more effectively.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users input basic attribute information (height, weight, age, gender) and health goals (e.g., weight loss, muscle gain) using their own devices. Once this information is entered into the device, the device generates a data packet and sends it to the server after ensuring security. The input data is the basic information necessary to generate a personalized exercise plan for the user.

[0168] Step 2:

[0169] The server receives attribute and objective information sent from the terminal. It applies a data cleansing algorithm to process the data, such as filtering outliers, to prepare a clean dataset. Next, it launches a generative AI model, using the refined data as a prompt to generate the optimal movement plan. This process outputs an initial draft of the movement plan best suited to the user's objective.

[0170] Step 3:

[0171] The server uses an emotion analysis engine to evaluate the user's emotional state on the generated exercise plan. Input consists of emotional data from sensors and self-reported data. The emotion analysis analyzes heart rate and voice tone data to perform data calculations that estimate stress levels and motivation. Based on this, the exercise plan is adjusted in real time, and the optimal plan is maintained on the server.

[0172] Step 4:

[0173] The server sends a pre-configured exercise plan to the terminal. The terminal integrates it into a VR application and prepares it for the user. The user experiences the exercise using a VR headset and other sensory feedback devices. The output here is the exercise instructions the user receives in the virtual environment. The terminal provides real-time feedback of emotional and physiological data obtained from the user during the exercise.

[0174] Step 5:

[0175] The server uses real-time feedback data received from the terminal to re-evaluate the exercise plan and make modifications if necessary. This process stores a history in a database, which is referenced when generating the next exercise plan. As a result, it becomes possible to support users in achieving their long-term health goals more efficiently.

[0176] This series of processes allows users to enjoy a personalized fitness experience that meets their physical and emotional needs.

[0177] (Application Example 2)

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

[0179] In modern society, there is a demand for exercise plans tailored to individual needs. However, conventional systems have not adequately considered the emotional state of users when personalizing exercise plans, making it difficult to maintain motivation and improve exercise efficiency. It is necessary to address these challenges to provide an optimized exercise experience for each user and promote health.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for acquiring user attribute data and objective data, means for generating an individualized exercise plan based on the attribute data and objective data using generative artificial intelligence, and means for inferring the user's emotional state using an emotion analysis device and adjusting the exercise plan. This makes it possible to provide an optimal exercise plan that takes into account both the user's physical characteristics and emotional state.

[0182] "User attribute data" refers to basic information used to identify or specify an individual, such as a user's height, weight, age, and gender.

[0183] "Purpose data" refers to information that indicates the health goals or fitness objectives that the user intends to achieve.

[0184] "Generative artificial intelligence" is an artificial intelligence technology equipped with advanced algorithms for generating exercise plans based on acquired data.

[0185] An "exercise plan" is a detailed plan that outlines the schedule and content of fitness exercises tailored to the user's health goals.

[0186] An "emotion analysis device" is a device that determines the emotional state of a user from their facial expressions, voice, and behavior, and provides feedback based on that determination.

[0187] A "virtual environment device" is a device that uses virtual reality technology to provide users with an interactive and immersive training experience.

[0188] "Physical fitness equipment" refers to devices and machines that allow users to actually move their bodies and exercise.

[0189] "Emotional feedback" refers to feedback information that analyzes the user's emotional state and provides information and guidance accordingly.

[0190] In this invention, the system provides a personalized exercise plan based on the user's input of attribute data and goal data. Specifically, the user uses a terminal to send basic information and fitness goals to the server. The server uses artificial intelligence to analyze this data and generate an optimal exercise plan. The generated exercise plan is presented to the user through a virtual environment device or physical fitness equipment.

[0191] Emotion analysis devices identify emotional states from a user's facial expressions and voice, and provide feedback to the motor plan. This process utilizes technologies such as voice analysis and image analysis. Specifically, voice processing software and image processing libraries are used to analyze the user's voice tone and facial expressions.

[0192] For example, if a user complains of fatigue during exercise, the system can monitor the user's emotional state in real time and adjust the exercise intensity as needed. This allows the user to continue training at their own pace.

[0193] The generating AI model analyzes the user's past data to further optimize the exercise plan. This process runs on a cloud server, leveraging advanced computing power. A continuous data feedback loop is in place to enhance the user's exercise experience. An example of a prompt is: "A man in his 30s, 175cm tall and weighing 80kg, aims to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that supports him by increasing the exercise intensity."

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

[0195] Step 1:

[0196] The server receives user attribute data and purpose data from the terminal. This includes height, weight, age, gender, and fitness goals. Based on this input data, the server stores it in a database and prepares it for analysis.

[0197] Step 2:

[0198] The server uses generative artificial intelligence to analyze the received data and generate a personalized exercise plan. This analysis includes comparing and fitting the user's data to a dataset of existing exercise plans. The generated exercise plan is output as a series of steps of exercises to be performed within a virtual environment.

[0199] Step 3:

[0200] The server collects user emotional data using an emotion analysis device and performs real-time analysis. The input includes the user's facial expressions and voice tone, which are evaluated through facial expression analysis software and speech recognition technology. The analysis results are provided as feedback necessary for adjusting the user's exercise plan.

[0201] Step 4:

[0202] The terminal presents the user with an exercise plan sent from the server, using either a virtual environment device or physical fitness equipment. During this process, the user's movements are monitored in real time by sensors and sent to the server. The user then performs the exercises based on the presented plan, and the results are returned to the server as feedback.

[0203] Step 5:

[0204] The server continuously optimizes the exercise plan based on the results of the actions and emotional feedback. This optimization uses a generative AI model to analyze past data and incorporate the findings into the new exercise plan. The optimized plan is then sent back to the terminal and presented to the user.

[0205] An example of a prompt message would be: "A man in his 30s, 175cm tall and weighing 80kg, is aiming to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that increases the exercise intensity to support him."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] In order to implement the present invention, it is necessary to construct the following system. This system consists of a user, a terminal, and a server, and each element works in cooperation with one another.

[0223] First, the user enters their personal data (height, weight, age, etc.) and health goals (e.g., weight loss, muscle gain) into the device. The device receives this data and then transmits it to the server using a secure communication method.

[0224] Next, the server executes a generative artificial intelligence program based on the received user attribute data and health goals. This program designs an optimal exercise plan for the user, determining the content, frequency, and intensity of exercise according to the user's characteristics. This generated plan is then sent to the terminal by the server.

[0225] Next, the terminal prepares the VR environment based on the exercise plan received from the server. The user experiences the training through a VR headset or similar device using the virtual environment equipment. The terminal uses sensors to monitor the user's movements in real time and provide appropriate feedback. For example, if the user is not following the training instructions or has poor posture, a virtual instructor in the VR environment will give corrective instructions.

[0226] After the training session ends, the device sends the user's activity history and training results (e.g., calories burned, achievement of exercise schedule) back to the server. The server then analyzes these results and adjusts the exercise plan to reflect the findings in the next training session.

[0227] For example, if a 30-year-old user starts a program with the goal of losing 5 kilograms, the system will suggest a plan primarily focused on aerobic exercise, tailored to the user's basal metabolic rate and physical level. In the VR environment, simulations of running and arm exercises are displayed, and the system evaluates the user's movements in real time to provide an optimized fitness experience.

[0228] In this way, this system advances the personalization of fitness and provides users with a means to effectively and efficiently achieve their health goals.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] Users create an account using their device and enter their attribute data and health goals. The entered data includes height, weight, age, gender, and health goals (e.g., weight loss or muscle building).

[0232] Step 2:

[0233] The terminal receives the entered user data and verifies its format. It then sends the verified data to the server using a secure communication protocol.

[0234] Step 3:

[0235] The server stores the received user attribute data and health goals in a database and invokes a generative artificial intelligence (AI) based on this data. The AI ​​analyzes the data and generates a personalized exercise plan.

[0236] Step 4:

[0237] The server sends the generated exercise plan to the terminal and configures it to build a virtual environment that the user can use.

[0238] Step 5:

[0239] The device prepares a VR environment based on the exercise plan, allowing the user to begin training. Exercises are guided by a virtual instructor.

[0240] Step 6:

[0241] Users wear a VR headset and perform training in a virtual environment. During training, sensors monitor their movements and provide real-time feedback.

[0242] Step 7:

[0243] The device sends activity data collected during the training session to a server. This server records data such as calories burned and exercise achievement.

[0244] Step 8:

[0245] The server analyzes the collected data and adjusts the exercise plan for more effective planning of the next training session. As a result, a report is generated that allows the user to check their progress.

[0246] Step 9:

[0247] Users can check their training results on their device and receive advice on planning their next workout and areas for improvement. This allows users to continuously adjust their training towards their goals.

[0248] (Example 1)

[0249] 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".

[0250] In today's busy lifestyle, it's difficult for individuals to find the optimal fitness plan and efficiently achieve their health goals. Therefore, there's a need for a system that provides personalized exercise plans tailored to individual physical conditions and goals, and supports their implementation through a virtual environment, thereby effectively achieving health objectives.

[0251] 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.

[0252] In this invention, the server includes means for acquiring the user's biometric information and health goals, means for generating personalized exercise instructions based on the biometric information and health goals using an AI model generated by an information processing device, and means for presenting the exercise instructions through a virtual reality device, monitoring the user's actions, and providing feedback. This enables the provision of an optimal fitness plan tailored to the user's characteristics and real-time feedback according to the practice status.

[0253] A "user" is an individual who uses the system to achieve their own health goals.

[0254] "Biometric information" refers to data that indicates a user's physical attributes, including height, weight, and age.

[0255] "Health goals" refer to specific fitness and health-related objectives set by the user, such as weight loss or muscle gain.

[0256] An "information processing device" is a device that analyzes data and generates appropriate movement instructions.

[0257] A "generative AI model" is a program that includes artificial intelligence technology to design optimal exercise instructions for a user based on their biometric information and health goals.

[0258] A "virtual reality device" is a device used by users to experience exercise in a virtual environment, and includes headsets and the like.

[0259] "Exercise instructions" refer to fitness activity plans created by a generative AI model, including details such as content, frequency, and intensity.

[0260] "Feedback" refers to information provided to improve the accuracy and effectiveness of the user's exercise, and includes real-time guidance.

[0261] This invention is a system that generates personalized exercise plans and provides users with an optimal fitness experience. The system mainly consists of three elements: the user, the terminal, and the server, each of which works in cooperation with the others.

[0262] First, the user enters their biometric information (e.g., height, weight, age) and health goals (e.g., weight loss, muscle gain) into the device. The device then transmits this information to the server using a secure communication method (e.g., HTTPS).

[0263] Next, the server uses an information processing device to run a generative AI model, generating personalized exercise instructions based on the user's biometric information and health goals. This generative AI model considers past fitness data and the user's specific requests to determine the optimal exercise content, frequency, and intensity. For example, the AI ​​model might be given the prompt, "Generate an exercise plan for a 30-year-old male, 70kg, 180cm tall, aiming to lose 5kg."

[0264] Next, the terminal prepares a virtual reality environment for the user based on the exercise instructions received from the server. Using a virtual reality device (e.g., a VR headset), the user can then experience the training. The terminal uses sensors to monitor the user's movements in real time and verify whether the exercise is being performed as planned.

[0265] Feedback is provided after monitoring and analyzing the user's actions in detail. For example, if the user has poor posture or performs actions different from the instructions, a virtual instructor in the virtual reality environment will provide corrective instructions.

[0266] Finally, once the exercise session is complete, the device sends the user's exercise history and results to the server. The server uses this data to further improve exercise instructions for the next session.

[0267] In this way, this system provides users with a series of programs to effectively achieve their health goals, optimizing fitness according to their individual health status and objectives.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user enters their biometric information and health goals into the terminal. The terminal sends this input data to the server using a protocol. The input includes the user's height, weight, age, and health goals, and the output is data transmitted in a secure format. This data is prepared for further processing on the server.

[0271] Step 2:

[0272] The server receives biometric information and health goals transmitted from the terminal and uses an information processing device to activate a generating AI model. Based on the input data, the AI ​​model generates personalized exercise instructions. Here, prompts are also useful, and the AI ​​model is instructed in the form of, "Generate an exercise plan for a 30-year-old man aiming to lose 5 kg." As output, it generates an exercise plan optimized for the user.

[0273] Step 3:

[0274] Upon receiving exercise instructions from the server, the terminal sets up the virtual reality environment. Specifically, it starts the VR headset and loads the virtual training environment for the user. The input is the exercise plan data, and the output is the completion of the virtual environment for the user to experience.

[0275] Step 4:

[0276] The device monitors the user's movements through a virtual reality environment. It uses sensors to acquire movement data in real time and verifies whether the user is following the movement instructions. Input is movement data obtained from the sensors, and output is appropriate feedback based on that data. If posture is poor, a virtual instructor automatically provides corrective instructions.

[0277] Step 5:

[0278] The device sends the results of the training session to the server. Here, performance data such as exercise history and calories burned are uploaded to the server. The server uses the transmitted data to improve the exercise instructions for the next session. The input is the training result data, and the output is stored as data used to adjust the next exercise plan.

[0279] (Application Example 1)

[0280] 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."

[0281] Providing individualized rehabilitation exercise plans and ensuring a safe and effective fitness experience for the elderly and those requiring rehabilitation is a challenge. Traditional fitness plans often offer generic programs that fail to adequately address the individual conditions and goals of users. Therefore, there is a need for methods that allow users to perform rehabilitation and exercise at a more appropriate pace.

[0282] 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.

[0283] In this invention, the server includes means for acquiring the user's biological parameters, means for generating an individualized exercise plan based on the biological parameters using a generation program, means for presenting the exercise plan through an augmented reality device, monitoring the user's body movements according to instructions, and providing appropriate corrections, and means for analyzing the results of the body movements and adjusting the exercise plan. Thereby, the user can safely and effectively have a fitness experience according to individual health goals.

[0284] The "user's biological parameters" refers to individually measured information such as the user's age, weight, body composition, and health status.

[0285] The "generation program" is software or an algorithm for designing an optimal exercise plan based on biological parameters and the user's goals.

[0286] The "individualized exercise plan" means a fitness plan designed for each individual according to the user's characteristics and goals.

[0287] The "augmented reality device" is hardware that enables the user to experience training in a virtual environment, usually referring to a head-mounted display or the like.

[0288] The "monitoring of body movements" is a process of observing and recording the user's movements and actions in real time using sensors, cameras, or the like.

[0289] The "adjustment of the exercise plan" is a procedure for changing and optimizing the exercise content and intensity according to the user's progress and changes in physical condition.

[0290] In implementing this invention, the server receives the user's biometric parameters. The terminal transmits data to the server using a secure communication method based on the information entered by the user. Using the user's biometric parameters such as age, weight, and health status, the generating program designs an optimal exercise plan. A generating AI model is utilized, enabling the program to provide personalized exercise guidance.

[0291] The server sends the generated exercise plan back to the terminal, which then displays the plan on an augmented reality device. Specifically, a head-mounted display is used to allow the user to experience the exercise in a virtual environment. The terminal utilizes sensors to monitor the user's physical movements in real time and determine whether the exercises are being performed correctly. If necessary, a virtual instructor provides feedback and prompts corrections to the form.

[0292] When a user finishes an exercise session, the device resends the exercise history and performance data to the server. Based on this, the server analyzes the received data and refines the next exercise plan. As a specific example, the generative AI model proposes personalized exercises in program statements that are tailored to the user's daily progress and physical condition.

[0293] An example of a prompt statement is as follows:

[0294] "Please generate a safe rehabilitation exercise plan for seniors based on user data. The exercises should focus on improving balance and strengthening leg muscles, and be suitable for home training."

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] The server receives biometric parameters and target data sent from the user via the terminal. These biometric parameters include information such as age, weight, and health status. Based on this data, the AI ​​model is prepared to design an exercise plan. This input prepares the system to generate an optimal exercise plan for the user.

[0298] Step 2:

[0299] The server uses a generative AI model to design a personalized exercise plan based on the received biometric parameters and target data. This AI model processes the parameters as input and determines the exercise content, frequency, and intensity according to the user's characteristics and goals. The resulting exercise plan is output. Specifically, the user selects from the various exercise combinations contained in the exercise plan.

[0300] Step 3:

[0301] The terminal receives the generated exercise plan and presents it to the user via an augmented reality device. A head-mounted display is used here, allowing the user to experience the exercise within a virtual environment. The output provides the user with instructions to begin the exercise. In this step, the virtual environment is visually and audibly set up and adjusted to allow the user to execute the prepared exercise plan.

[0302] Step 4:

[0303] The device uses sensors to monitor the user's physical movements in real time while they are exercising. If the user's movements deviate from the exercise plan, a virtual instructor provides corrective feedback to encourage improvement. This monitoring process takes data from the sensors as input and compares it to the exercise plan. Corrective instructions are generated as output.

[0304] Step 5:

[0305] After the exercise is completed, the terminal sends the user's exercise history and achievement data to the server. This includes calorie consumption and the degree of achievement of the exercise schedule. Using this data, the server performs an analysis to further refine the next exercise plan. In this process, the input exercise history data is used as basic data for adjusting the next exercise plan. The generative AI model works again, and an updated exercise plan is output.

[0306] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0307] The present invention is a system that acquires the user's attribute data and target data and generates and optimizes an individualized exercise plan by utilizing a generative artificial intelligence and an emotion engine. The system is configured to include a user, a terminal, and a server.

[0308] First, the user uses the terminal to input their basic information (height, weight, age, gender, etc.) and health goals (e.g., weight loss, muscle strength improvement). The terminal sends this data to the server.

[0309] The server analyzes the received attribute data and target data, executes a generative artificial intelligence program, and generates an exercise plan optimal for the user. At this time, the emotion engine adjusts the plan in consideration of the user's current emotional state. The emotion engine infers emotions from sensor data and the user's input, and evaluates motivation for exercise and stress indices.

[0310] The generated exercise plan is sent to the terminal and is set so that the user can experience it in a virtual environment. The terminal uses a VR headset or other sensory feedback device to provide the user with a realistic training experience. Also, the terminal provides real-time feedback on the user's emotional changes during exercise to the emotion engine, and finely adjusts the intensity and content of the training instantaneously based on that data.

[0311] As a concrete example, consider the case of a 30-year-old user who sets a goal of "losing 5 kilograms." The system tracks the user's emotional state, and if the stress level rises during exercise, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. On the other hand, if the user is positive and energetic about exercising, the system moderately increases the exercise intensity and provides a challenging plan.

[0312] After the session ends, the server analyzes the collected emotional data and exercise results to incorporate them into the next fitness plan for further optimization. A meal plan is also generated, providing comprehensive guidance to help users achieve their health goals more quickly.

[0313] This system allows users to have an efficient and personalized fitness experience that takes into account not only their physical but also their emotional state. This interactive and flexible training environment helps to achieve greater results and maintain motivation.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] Users create an account using their device and enter attribute data (e.g., height, weight, age) and goal data (e.g., weight loss, muscle gain). This data serves as the foundational information necessary for the user to achieve their fitness goals.

[0317] Step 2:

[0318] The terminal verifies attribute and objective data obtained from the user and transmits it to the server using a secure communication method. The transmitted data is integrated information including the user's characteristics and goals.

[0319] Step 3:

[0320] The server utilizes generative artificial intelligence to generate an optimized exercise plan for the user based on the received data. Here, the type, duration, and frequency of exercise are determined according to the user's physical characteristics and goals.

[0321] Step 4:

[0322] During the generation process, the server uses an emotion engine to analyze the user's emotional data and modifies the exercise plan based on this. If the user is in a high-stress state, it suggests exercises that promote relaxation; if the user is in a positive emotional state, it adds challenging exercises.

[0323] Step 5:

[0324] The server sends the final version of the plan to the device, which then prepares the training in the VR environment based on it. The user then puts on the VR headset and is ready to participate in the guided session.

[0325] Step 6:

[0326] The user begins training in a VR environment, and their movements are monitored in real time by the device. The device uses sensors to detect changes in the user's emotions along with their movements and sends the data to the emotion engine.

[0327] Step 7:

[0328] The device updates the exercise content and feedback as needed based on input from the emotion engine. For example, if the user starts to feel fatigued, measures such as slowing down the pace of the instructions are taken.

[0329] Step 8:

[0330] After the training session ends, the server analyzes the behavioral and emotional data reported from the terminal. Based on the results, it adjusts the next exercise plan and provides feedback to the user on their progress and areas for improvement.

[0331] Step 9:

[0332] Furthermore, the server also suggests meal plans that contribute to the user's health goals, supporting the overall fitness experience. These plans are regularly reviewed and continuously optimized by artificial intelligence.

[0333] (Example 2)

[0334] 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".

[0335] Traditional exercise plan generation systems typically generated exercise plans based on the user's physical attributes, but did not consider the user's emotional state. As a result, it was difficult to maintain user motivation and provide a personalized fitness experience. Furthermore, the lack of comprehensive coordination between exercise and meal plans limited the user's ability to achieve their health goals.

[0336] 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.

[0337] In this invention, the server includes means for acquiring user attribute information and purpose information, means for generating an individualized exercise plan based on the attribute information and purpose information using artificial intelligence, and means for evaluating the user's emotional state through emotion analysis and adjusting the exercise plan accordingly. This makes it possible to provide a flexible exercise plan that responds to the user's emotional state.

[0338] "User attribute information" refers to basic personal information about the user, such as height, weight, age, and gender.

[0339] "Purpose information" refers to information about the health and fitness goals that the user intends to achieve.

[0340] "Generative artificial intelligence" refers to a computer program that has the ability to automatically create specific plans and recommendations based on the user's attribute information and objective information.

[0341] An "exercise plan" refers to a schedule of specific exercises and workouts created based on the user's health goals.

[0342] "Emotional analysis" refers to the process of using sensor data and other input data to evaluate a user's emotional state and infer their mental state.

[0343] A "nutrition plan" refers to a set of dietary and nutritional guidance guidelines created to support users in achieving their health goals.

[0344] A "learning algorithm" refers to a computer algorithm that has the ability to learn from data and use the results to continuously improve and optimize its plans.

[0345] This invention is configured as a system that provides a personalized fitness experience for users. The system mainly consists of a user, a terminal, and a server.

[0346] First, the user uses their device to input basic attribute information (e.g., height, weight, age, gender) and health goals they wish to achieve (e.g., weight loss, muscle gain). The device then transmits this information to the server via a secure communication protocol.

[0347] The server uses a database management system and data cleansing algorithms to analyze the received data. The generated artificial intelligence model creates an optimal exercise plan from attribute information and objective information. In this process, the server uses an emotion analysis engine to evaluate the user's emotions. Specifically, it uses information obtained from sensors and input data, such as heart rate and voice data, to infer the user's current emotional state.

[0348] The generated exercise plan is organized in XML or JSON format and sent to the terminal. The terminal uses a VR headset and sensory feedback devices to create an environment where the user can experience exercise in a virtual environment. During this process, the terminal feeds back the user's real-time emotional changes to the server, and the exercise plan is adjusted as needed.

[0349] As a concrete example, consider a case where a 30-year-old user sets a goal of "losing 5 kilograms." In this case, the system tracks the user's emotional state during exercise, and if an increase in stress levels is detected, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. If a positive and energetic state is maintained, the system increases the exercise intensity and provides a more challenging plan.

[0350] An example of a prompt sentence for a generative AI model is, "How can we optimize an exercise plan for a 30-year-old user aiming to lose 5 kg, taking into account the emotional engine?" In this way, the system provides a personalized plan that takes into account the user's physical and emotional state, helping them achieve their health goals more effectively.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] Users input basic attribute information (height, weight, age, gender) and health goals (e.g., weight loss, muscle gain) using their own devices. Once this information is entered into the device, the device generates a data packet and sends it to the server after ensuring security. The input data is the basic information necessary to generate a personalized exercise plan for the user.

[0354] Step 2:

[0355] The server receives attribute and objective information sent from the terminal. It applies a data cleansing algorithm to process the data, such as filtering outliers, to prepare a clean dataset. Next, it launches a generative AI model, using the refined data as a prompt to generate the optimal movement plan. This process outputs an initial draft of the movement plan best suited to the user's objective.

[0356] Step 3:

[0357] The server uses an emotion analysis engine to evaluate the user's emotional state on the generated exercise plan. Input consists of emotional data from sensors and self-reported data. The emotion analysis analyzes heart rate and voice tone data to perform data calculations that estimate stress levels and motivation. Based on this, the exercise plan is adjusted in real time, and the optimal plan is maintained on the server.

[0358] Step 4:

[0359] The server sends a pre-configured exercise plan to the terminal. The terminal integrates it into a VR application and prepares it for the user. The user experiences the exercise using a VR headset and other sensory feedback devices. The output here is the exercise instructions the user receives in the virtual environment. The terminal provides real-time feedback of emotional and physiological data obtained from the user during the exercise.

[0360] Step 5:

[0361] The server uses real-time feedback data received from the terminal to re-evaluate the exercise plan and make modifications if necessary. This process stores a history in a database, which is referenced when generating the next exercise plan. As a result, it becomes possible to support users in achieving their long-term health goals more efficiently.

[0362] This series of processes allows users to enjoy a personalized fitness experience that meets their physical and emotional needs.

[0363] (Application Example 2)

[0364] 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."

[0365] In modern society, there is a demand for exercise plans tailored to individual needs. However, conventional systems have not adequately considered the emotional state of users when personalizing exercise plans, making it difficult to maintain motivation and improve exercise efficiency. It is necessary to address these challenges to provide an optimized exercise experience for each user and promote health.

[0366] 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.

[0367] In this invention, the server includes means for acquiring user attribute data and objective data, means for generating an individualized exercise plan based on the attribute data and objective data using generative artificial intelligence, and means for inferring the user's emotional state using an emotion analysis device and adjusting the exercise plan. This makes it possible to provide an optimal exercise plan that takes into account both the user's physical characteristics and emotional state.

[0368] "User attribute data" refers to basic information used to identify or specify an individual, such as a user's height, weight, age, and gender.

[0369] "Purpose data" refers to information that indicates the health goals or fitness objectives that the user intends to achieve.

[0370] "Generative artificial intelligence" is an artificial intelligence technology equipped with advanced algorithms for generating exercise plans based on acquired data.

[0371] An "exercise plan" is a detailed plan that outlines the schedule and content of fitness exercises tailored to the user's health goals.

[0372] An "emotion analysis device" is a device that determines the emotional state of a user from their facial expressions, voice, and behavior, and provides feedback based on that determination.

[0373] A "virtual environment device" is a device that uses virtual reality technology to provide users with an interactive and immersive training experience.

[0374] "Physical fitness equipment" refers to devices and machines that allow users to actually move their bodies and exercise.

[0375] "Emotional feedback" refers to feedback information that analyzes the user's emotional state and provides information and guidance accordingly.

[0376] In this invention, the system provides a personalized exercise plan based on the user's input of attribute data and goal data. Specifically, the user uses a terminal to send basic information and fitness goals to the server. The server uses artificial intelligence to analyze this data and generate an optimal exercise plan. The generated exercise plan is presented to the user through a virtual environment device or physical fitness equipment.

[0377] Emotion analysis devices identify emotional states from a user's facial expressions and voice, and provide feedback to the motor plan. This process utilizes technologies such as voice analysis and image analysis. Specifically, voice processing software and image processing libraries are used to analyze the user's voice tone and facial expressions.

[0378] For example, if a user complains of fatigue during exercise, the system can monitor the user's emotional state in real time and adjust the exercise intensity as needed. This allows the user to continue training at their own pace.

[0379] The generating AI model analyzes the user's past data to further optimize the exercise plan. This process runs on a cloud server, leveraging advanced computing power. A continuous data feedback loop is in place to enhance the user's exercise experience. An example of a prompt is: "A man in his 30s, 175cm tall and weighing 80kg, aims to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that supports him by increasing the exercise intensity."

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server receives user attribute data and purpose data from the terminal. This includes height, weight, age, gender, and fitness goals. Based on this input data, the server stores it in a database and prepares it for analysis.

[0383] Step 2:

[0384] The server uses generative artificial intelligence to analyze the received data and generate a personalized exercise plan. This analysis includes comparing and fitting the user's data to a dataset of existing exercise plans. The generated exercise plan is output as a series of steps of exercises to be performed within a virtual environment.

[0385] Step 3:

[0386] The server collects user emotional data using an emotion analysis device and performs real-time analysis. The input includes the user's facial expressions and voice tone, which are evaluated through facial expression analysis software and speech recognition technology. The analysis results are provided as feedback necessary for adjusting the user's exercise plan.

[0387] Step 4:

[0388] The terminal presents the user with an exercise plan sent from the server, using either a virtual environment device or physical fitness equipment. During this process, the user's movements are monitored in real time by sensors and sent to the server. The user then performs the exercises based on the presented plan, and the results are returned to the server as feedback.

[0389] Step 5:

[0390] The server continuously optimizes the exercise plan based on the results of the actions and emotional feedback. This optimization uses a generative AI model to analyze past data and incorporate the findings into the new exercise plan. The optimized plan is then sent back to the terminal and presented to the user.

[0391] An example of a prompt message would be: "A man in his 30s, 175cm tall and weighing 80kg, is aiming to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that increases the exercise intensity to support him."

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] In order to implement the present invention, it is necessary to construct the following system. This system consists of a user, a terminal, and a server, and each element works in cooperation with one another.

[0409] First, the user enters their personal data (height, weight, age, etc.) and health goals (e.g., weight loss, muscle gain) into the device. The device receives this data and then transmits it to the server using a secure communication method.

[0410] Next, the server executes a generative artificial intelligence program based on the received user attribute data and health goals. This program designs an optimal exercise plan for the user, determining the content, frequency, and intensity of exercise according to the user's characteristics. This generated plan is then sent to the terminal by the server.

[0411] Next, the terminal prepares the VR environment based on the exercise plan received from the server. The user experiences the training through a VR headset or similar device using the virtual environment equipment. The terminal uses sensors to monitor the user's movements in real time and provide appropriate feedback. For example, if the user is not following the training instructions or has poor posture, a virtual instructor in the VR environment will give corrective instructions.

[0412] After the training session ends, the device sends the user's activity history and training results (e.g., calories burned, achievement of exercise schedule) back to the server. The server then analyzes these results and adjusts the exercise plan to reflect the findings in the next training session.

[0413] For example, if a 30-year-old user starts a program with the goal of losing 5 kilograms, the system will suggest a plan primarily focused on aerobic exercise, tailored to the user's basal metabolic rate and physical level. In the VR environment, simulations of running and arm exercises are displayed, and the system evaluates the user's movements in real time to provide an optimized fitness experience.

[0414] In this way, this system advances the personalization of fitness and provides users with a means to effectively and efficiently achieve their health goals.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] Users create an account using their device and enter their attribute data and health goals. The entered data includes height, weight, age, gender, and health goals (e.g., weight loss or muscle building).

[0418] Step 2:

[0419] The terminal receives the entered user data and verifies its format. It then sends the verified data to the server using a secure communication protocol.

[0420] Step 3:

[0421] The server stores the received user attribute data and health goals in a database and invokes a generative artificial intelligence (AI) based on this data. The AI ​​analyzes the data and generates a personalized exercise plan.

[0422] Step 4:

[0423] The server sends the generated exercise plan to the terminal and configures it to build a virtual environment that the user can use.

[0424] Step 5:

[0425] The device prepares a VR environment based on the exercise plan, allowing the user to begin training. Exercises are guided by a virtual instructor.

[0426] Step 6:

[0427] Users wear a VR headset and perform training in a virtual environment. During training, sensors monitor their movements and provide real-time feedback.

[0428] Step 7:

[0429] The device sends activity data collected during the training session to a server. This server records data such as calories burned and exercise achievement.

[0430] Step 8:

[0431] The server analyzes the collected data and adjusts the exercise plan for more effective planning of the next training session. As a result, a report is generated that allows the user to check their progress.

[0432] Step 9:

[0433] Users can check their training results on their device and receive advice on planning their next workout and areas for improvement. This allows users to continuously adjust their training towards their goals.

[0434] (Example 1)

[0435] 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."

[0436] In today's busy lifestyle, it's difficult for individuals to find the optimal fitness plan and efficiently achieve their health goals. Therefore, there's a need for a system that provides personalized exercise plans tailored to individual physical conditions and goals, and supports their implementation through a virtual environment, thereby effectively achieving health objectives.

[0437] 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.

[0438] In this invention, the server includes means for acquiring the user's biometric information and health goals, means for generating personalized exercise instructions based on the biometric information and health goals using an AI model generated by an information processing device, and means for presenting the exercise instructions through a virtual reality device, monitoring the user's actions, and providing feedback. This enables the provision of an optimal fitness plan tailored to the user's characteristics and real-time feedback according to the practice status.

[0439] A "user" is an individual who uses the system to achieve their own health goals.

[0440] "Biometric information" refers to data that indicates a user's physical attributes, including height, weight, and age.

[0441] "Health goals" refer to specific fitness and health-related objectives set by the user, such as weight loss or muscle gain.

[0442] An "information processing device" is a device that analyzes data and generates appropriate movement instructions.

[0443] A "generative AI model" is a program that includes artificial intelligence technology to design optimal exercise instructions for a user based on their biometric information and health goals.

[0444] A "virtual reality device" is a device used by users to experience exercise in a virtual environment, and includes headsets and the like.

[0445] "Exercise instructions" refer to fitness activity plans created by a generative AI model, including details such as content, frequency, and intensity.

[0446] "Feedback" refers to information provided to improve the accuracy and effectiveness of the user's exercise, and includes real-time guidance.

[0447] This invention is a system that generates personalized exercise plans and provides users with an optimal fitness experience. The system mainly consists of three elements: the user, the terminal, and the server, each of which works in cooperation with the others.

[0448] First, the user enters their biometric information (e.g., height, weight, age) and health goals (e.g., weight loss, muscle gain) into the device. The device then transmits this information to the server using a secure communication method (e.g., HTTPS).

[0449] Next, the server uses an information processing device to run a generative AI model, generating personalized exercise instructions based on the user's biometric information and health goals. This generative AI model considers past fitness data and the user's specific requests to determine the optimal exercise content, frequency, and intensity. For example, the AI ​​model might be given the prompt, "Generate an exercise plan for a 30-year-old male, 70kg, 180cm tall, aiming to lose 5kg."

[0450] Next, the terminal prepares a virtual reality environment for the user based on the exercise instructions received from the server. Using a virtual reality device (e.g., a VR headset), the user can then experience the training. The terminal uses sensors to monitor the user's movements in real time and verify whether the exercise is being performed as planned.

[0451] Feedback is provided after monitoring and analyzing the user's actions in detail. For example, if the user has poor posture or performs actions different from the instructions, a virtual instructor in the virtual reality environment will provide corrective instructions.

[0452] Finally, once the exercise session is complete, the device sends the user's exercise history and results to the server. The server uses this data to further improve exercise instructions for the next session.

[0453] In this way, this system provides users with a series of programs to effectively achieve their health goals, optimizing fitness according to their individual health status and objectives.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] The user enters their biometric information and health goals into the terminal. The terminal sends this input data to the server using a protocol. The input includes the user's height, weight, age, and health goals, and the output is data transmitted in a secure format. This data is prepared for further processing on the server.

[0457] Step 2:

[0458] The server receives biometric information and health goals transmitted from the terminal and uses an information processing device to activate a generating AI model. Based on the input data, the AI ​​model generates personalized exercise instructions. Here, prompts are also useful, and the AI ​​model is instructed in the form of, "Generate an exercise plan for a 30-year-old man aiming to lose 5 kg." As output, it generates an exercise plan optimized for the user.

[0459] Step 3:

[0460] Upon receiving exercise instructions from the server, the terminal sets up the virtual reality environment. Specifically, it starts the VR headset and loads the virtual training environment for the user. The input is the exercise plan data, and the output is the completion of the virtual environment for the user to experience.

[0461] Step 4:

[0462] The device monitors the user's movements through a virtual reality environment. It uses sensors to acquire movement data in real time and verifies whether the user is following the movement instructions. Input is movement data obtained from the sensors, and output is appropriate feedback based on that data. If posture is poor, a virtual instructor automatically provides corrective instructions.

[0463] Step 5:

[0464] The device sends the results of the training session to the server. Here, performance data such as exercise history and calories burned are uploaded to the server. The server uses the transmitted data to improve the exercise instructions for the next session. The input is the training result data, and the output is stored as data used to adjust the next exercise plan.

[0465] (Application Example 1)

[0466] 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."

[0467] Providing individualized rehabilitation exercise plans and ensuring a safe and effective fitness experience for the elderly and those requiring rehabilitation is a challenge. Traditional fitness plans often offer generic programs that fail to adequately address the individual conditions and goals of users. Therefore, there is a need for methods that allow users to perform rehabilitation and exercise at a more appropriate pace.

[0468] 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.

[0469] In this invention, the server includes means for acquiring the user's biometric parameters, means for generating an individualized exercise plan based on the biometric parameters using a generation program, means for presenting the exercise plan through an augmented reality device, monitoring the user's physical movements according to instructions, and providing appropriate modifications, and means for analyzing the results of the physical movements and adjusting the exercise plan. This enables the user to safely and effectively engage in a fitness experience tailored to their individual health goals.

[0470] "User biometric parameters" refer to individually measured information such as the user's age, weight, body composition, and health status.

[0471] A "generative program" is software or an algorithm used to design an optimal exercise plan based on biological parameters and the user's goals.

[0472] An "individualized exercise plan" refers to a fitness plan designed to suit each individual user, taking into account their characteristics and goals.

[0473] An "augmented reality device" is hardware that allows users to experience training in a virtual environment, and typically refers to a head-mounted display or similar device.

[0474] "Monitoring of physical movement" is the process of observing and recording a user's movements and actions in real time using sensors, cameras, and other devices.

[0475] "Adjusting the exercise plan" is the process of modifying and optimizing the content and intensity of exercises in accordance with the user's progress and changes in their physical condition.

[0476] In implementing this invention, the server receives the user's biometric parameters. The terminal transmits data to the server using a secure communication method based on the information entered by the user. Using the user's biometric parameters such as age, weight, and health status, the generating program designs an optimal exercise plan. A generating AI model is utilized, enabling the program to provide personalized exercise guidance.

[0477] The server sends the generated exercise plan back to the terminal, which then displays the plan on an augmented reality device. Specifically, a head-mounted display is used to allow the user to experience the exercise in a virtual environment. The terminal utilizes sensors to monitor the user's physical movements in real time and determine whether the exercises are being performed correctly. If necessary, a virtual instructor provides feedback and prompts corrections to the form.

[0478] When a user finishes an exercise session, the device resends the exercise history and performance data to the server. Based on this, the server analyzes the received data and refines the next exercise plan. As a specific example, the generative AI model proposes personalized exercises in program statements that are tailored to the user's daily progress and physical condition.

[0479] An example of a prompt statement is as follows:

[0480] "Please generate a safe rehabilitation exercise plan for seniors based on user data. The exercises should focus on improving balance and strengthening leg muscles, and be suitable for home training."

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The server receives biometric parameters and target data sent from the user via the terminal. These biometric parameters include information such as age, weight, and health status. Based on this data, the AI ​​model is prepared to design an exercise plan. This input prepares the system to generate an optimal exercise plan for the user.

[0484] Step 2:

[0485] The server uses a generative AI model to design a personalized exercise plan based on the received biometric parameters and target data. This AI model processes the parameters as input and determines the exercise content, frequency, and intensity according to the user's characteristics and goals. The resulting exercise plan is output. Specifically, the user selects from the various exercise combinations contained in the exercise plan.

[0486] Step 3:

[0487] The terminal receives the generated exercise plan and presents it to the user via an augmented reality device. A head-mounted display is used here, allowing the user to experience the exercise within a virtual environment. The output provides the user with instructions to begin the exercise. In this step, the virtual environment is visually and audibly set up and adjusted to allow the user to execute the prepared exercise plan.

[0488] Step 4:

[0489] The device uses sensors to monitor the user's physical movements in real time while they are exercising. If the user's movements deviate from the exercise plan, a virtual instructor provides corrective feedback to encourage improvement. This monitoring process takes data from the sensors as input and compares it to the exercise plan. Corrective instructions are generated as output.

[0490] Step 5:

[0491] After the exercise is completed, the device sends the user's exercise history and performance data to the server. This includes calories burned and the degree to which the exercise schedule was achieved. The server uses this data to analyze and further refine the next exercise plan. In this process, the input exercise history data is used as foundational data to adjust the next exercise plan. The generating AI model runs again, and an updated exercise plan is output.

[0492] 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.

[0493] This invention relates to a system that acquires user attribute data and objective data, and generates and optimizes personalized exercise plans using generative artificial intelligence and an emotion engine. The system consists of a user, a terminal, and a server.

[0494] First, the user uses their device to input their basic information (height, weight, age, gender, etc.) and health goals (e.g., weight loss, muscle gain). The device then sends this data to the server.

[0495] The server analyzes the received attribute and target data, and executes a generation artificial intelligence program to create an optimal exercise plan for the user. During this process, the emotion engine adjusts the plan, taking into account the user's current emotional state. The emotion engine infers emotions from sensor data and user input, and evaluates motivation and stress levels for exercise.

[0496] The generated exercise plan is sent to the device and set up so that the user can experience it in a virtual environment. The device uses a VR headset and other sensory feedback devices to provide the user with a realistic training experience. The device also feeds back the user's emotional changes during exercise to an emotion engine in real time, and instantly fine-tunes the intensity and content of the training based on that data.

[0497] As a concrete example, consider the case of a 30-year-old user who sets a goal of "losing 5 kilograms." The system tracks the user's emotional state, and if the stress level rises during exercise, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. On the other hand, if the user is positive and energetic about exercising, the system moderately increases the exercise intensity and provides a challenging plan.

[0498] After the session ends, the server analyzes the collected emotional data and exercise results to incorporate them into the next fitness plan for further optimization. A meal plan is also generated, providing comprehensive guidance to help users achieve their health goals more quickly.

[0499] This system allows users to have an efficient and personalized fitness experience that takes into account not only their physical but also their emotional state. This interactive and flexible training environment helps to achieve greater results and maintain motivation.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] Users create an account using their device and enter attribute data (e.g., height, weight, age) and goal data (e.g., weight loss, muscle gain). This data serves as the foundational information necessary for the user to achieve their fitness goals.

[0503] Step 2:

[0504] The terminal verifies attribute and objective data obtained from the user and transmits it to the server using a secure communication method. The transmitted data is integrated information including the user's characteristics and goals.

[0505] Step 3:

[0506] The server utilizes generative artificial intelligence to generate an optimized exercise plan for the user based on the received data. Here, the type, duration, and frequency of exercise are determined according to the user's physical characteristics and goals.

[0507] Step 4:

[0508] During the generation process, the server uses an emotion engine to analyze the user's emotional data and modifies the exercise plan based on this. If the user is in a high-stress state, it suggests exercises that promote relaxation; if the user is in a positive emotional state, it adds challenging exercises.

[0509] Step 5:

[0510] The server sends the final version of the plan to the device, which then prepares the training in the VR environment based on it. The user then puts on the VR headset and is ready to participate in the guided session.

[0511] Step 6:

[0512] The user begins training in a VR environment, and their movements are monitored in real time by the device. The device uses sensors to detect changes in the user's emotions along with their movements and sends the data to the emotion engine.

[0513] Step 7:

[0514] The device updates the exercise content and feedback as needed based on input from the emotion engine. For example, if the user starts to feel fatigued, measures such as slowing down the pace of the instructions are taken.

[0515] Step 8:

[0516] After the training session ends, the server analyzes the behavioral and emotional data reported from the terminal. Based on the results, it adjusts the next exercise plan and provides feedback to the user on their progress and areas for improvement.

[0517] Step 9:

[0518] Furthermore, the server also suggests meal plans that contribute to the user's health goals, supporting the overall fitness experience. These plans are regularly reviewed and continuously optimized by artificial intelligence.

[0519] (Example 2)

[0520] 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."

[0521] Traditional exercise plan generation systems typically generated exercise plans based on the user's physical attributes, but did not consider the user's emotional state. As a result, it was difficult to maintain user motivation and provide a personalized fitness experience. Furthermore, the lack of comprehensive coordination between exercise and meal plans limited the user's ability to achieve their health goals.

[0522] 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.

[0523] In this invention, the server includes means for acquiring user attribute information and purpose information, means for generating an individualized exercise plan based on the attribute information and purpose information using artificial intelligence, and means for evaluating the user's emotional state through emotion analysis and adjusting the exercise plan accordingly. This makes it possible to provide a flexible exercise plan that responds to the user's emotional state.

[0524] "User attribute information" refers to basic personal information about the user, such as height, weight, age, and gender.

[0525] "Purpose information" refers to information about the health and fitness goals that the user intends to achieve.

[0526] "Generative artificial intelligence" refers to a computer program that has the ability to automatically create specific plans and recommendations based on the user's attribute information and objective information.

[0527] An "exercise plan" refers to a schedule of specific exercises and workouts created based on the user's health goals.

[0528] "Emotional analysis" refers to the process of using sensor data and other input data to evaluate a user's emotional state and infer their mental state.

[0529] A "nutrition plan" refers to a set of dietary and nutritional guidance guidelines created to support users in achieving their health goals.

[0530] A "learning algorithm" refers to a computer algorithm that has the ability to learn from data and use the results to continuously improve and optimize its plans.

[0531] This invention is configured as a system that provides a personalized fitness experience for users. The system mainly consists of a user, a terminal, and a server.

[0532] First, the user uses their device to input basic attribute information (e.g., height, weight, age, gender) and health goals they wish to achieve (e.g., weight loss, muscle gain). The device then transmits this information to the server via a secure communication protocol.

[0533] The server uses a database management system and data cleansing algorithms to analyze the received data. The generated artificial intelligence model creates an optimal exercise plan from attribute information and objective information. In this process, the server uses an emotion analysis engine to evaluate the user's emotions. Specifically, it uses information obtained from sensors and input data, such as heart rate and voice data, to infer the user's current emotional state.

[0534] The generated exercise plan is organized in XML or JSON format and sent to the terminal. The terminal uses a VR headset and sensory feedback devices to create an environment where the user can experience exercise in a virtual environment. During this process, the terminal feeds back the user's real-time emotional changes to the server, and the exercise plan is adjusted as needed.

[0535] As a concrete example, consider a case where a 30-year-old user sets a goal of "losing 5 kilograms." In this case, the system tracks the user's emotional state during exercise, and if an increase in stress levels is detected, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. If a positive and energetic state is maintained, the system increases the exercise intensity and provides a more challenging plan.

[0536] An example of a prompt sentence for a generative AI model is, "How can we optimize an exercise plan for a 30-year-old user aiming to lose 5 kg, taking into account the emotional engine?" In this way, the system provides a personalized plan that takes into account the user's physical and emotional state, helping them achieve their health goals more effectively.

[0537] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0538] Step 1:

[0539] Users input basic attribute information (height, weight, age, gender) and health goals (e.g., weight loss, muscle gain) using their own devices. Once this information is entered into the device, the device generates a data packet and sends it to the server after ensuring security. The input data is the basic information necessary to generate a personalized exercise plan for the user.

[0540] Step 2:

[0541] The server receives attribute and objective information sent from the terminal. It applies a data cleansing algorithm to process the data, such as filtering outliers, to prepare a clean dataset. Next, it launches a generative AI model, using the refined data as a prompt to generate the optimal movement plan. This process outputs an initial draft of the movement plan best suited to the user's objective.

[0542] Step 3:

[0543] The server uses an emotion analysis engine to evaluate the user's emotional state on the generated exercise plan. Input consists of emotional data from sensors and self-reported data. The emotion analysis analyzes heart rate and voice tone data to perform data calculations that estimate stress levels and motivation. Based on this, the exercise plan is adjusted in real time, and the optimal plan is maintained on the server.

[0544] Step 4:

[0545] The server sends a pre-configured exercise plan to the terminal. The terminal integrates it into a VR application and prepares it for the user. The user experiences the exercise using a VR headset and other sensory feedback devices. The output here is the exercise instructions the user receives in the virtual environment. The terminal provides real-time feedback of emotional and physiological data obtained from the user during the exercise.

[0546] Step 5:

[0547] The server uses real-time feedback data received from the terminal to re-evaluate the exercise plan and make modifications if necessary. This process stores a history in a database, which is referenced when generating the next exercise plan. As a result, it becomes possible to support users in achieving their long-term health goals more efficiently.

[0548] This series of processes allows users to enjoy a personalized fitness experience that meets their physical and emotional needs.

[0549] (Application Example 2)

[0550] 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."

[0551] In modern society, there is a demand for exercise plans tailored to individual needs. However, conventional systems have not adequately considered the emotional state of users when personalizing exercise plans, making it difficult to maintain motivation and improve exercise efficiency. It is necessary to address these challenges to provide an optimized exercise experience for each user and promote health.

[0552] 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.

[0553] In this invention, the server includes means for acquiring user attribute data and objective data, means for generating an individualized exercise plan based on the attribute data and objective data using generative artificial intelligence, and means for inferring the user's emotional state using an emotion analysis device and adjusting the exercise plan. This makes it possible to provide an optimal exercise plan that takes into account both the user's physical characteristics and emotional state.

[0554] "User attribute data" refers to basic information used to identify or specify an individual, such as a user's height, weight, age, and gender.

[0555] "Purpose data" refers to information that indicates the health goals or fitness objectives that the user intends to achieve.

[0556] "Generative artificial intelligence" is an artificial intelligence technology equipped with advanced algorithms for generating exercise plans based on acquired data.

[0557] An "exercise plan" is a detailed plan that outlines the schedule and content of fitness exercises tailored to the user's health goals.

[0558] An "emotion analysis device" is a device that determines the emotional state of a user from their facial expressions, voice, and behavior, and provides feedback based on that determination.

[0559] A "virtual environment device" is a device that uses virtual reality technology to provide users with an interactive and immersive training experience.

[0560] "Physical fitness equipment" refers to devices and machines that allow users to actually move their bodies and exercise.

[0561] "Emotional feedback" refers to feedback information that analyzes the user's emotional state and provides information and guidance accordingly.

[0562] In this invention, the system provides a personalized exercise plan based on the user's input of attribute data and goal data. Specifically, the user uses a terminal to send basic information and fitness goals to the server. The server uses artificial intelligence to analyze this data and generate an optimal exercise plan. The generated exercise plan is presented to the user through a virtual environment device or physical fitness equipment.

[0563] Emotion analysis devices identify emotional states from a user's facial expressions and voice, and provide feedback to the motor plan. This process utilizes technologies such as voice analysis and image analysis. Specifically, voice processing software and image processing libraries are used to analyze the user's voice tone and facial expressions.

[0564] For example, if a user complains of fatigue during exercise, the system can monitor the user's emotional state in real time and adjust the exercise intensity as needed. This allows the user to continue training at their own pace.

[0565] The generating AI model analyzes the user's past data to further optimize the exercise plan. This process runs on a cloud server, leveraging advanced computing power. A continuous data feedback loop is in place to enhance the user's exercise experience. An example of a prompt is: "A man in his 30s, 175cm tall and weighing 80kg, aims to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that supports him by increasing the exercise intensity."

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The server receives user attribute data and purpose data from the terminal. This includes height, weight, age, gender, and fitness goals. Based on this input data, the server stores it in a database and prepares it for analysis.

[0569] Step 2:

[0570] The server uses generative artificial intelligence to analyze the received data and generate a personalized exercise plan. This analysis includes comparing and fitting the user's data to a dataset of existing exercise plans. The generated exercise plan is output as a series of steps of exercises to be performed within a virtual environment.

[0571] Step 3:

[0572] The server collects user emotional data using an emotion analysis device and performs real-time analysis. The input includes the user's facial expressions and voice tone, which are evaluated through facial expression analysis software and speech recognition technology. The analysis results are provided as feedback necessary for adjusting the user's exercise plan.

[0573] Step 4:

[0574] The terminal presents the user with an exercise plan sent from the server, using either a virtual environment device or physical fitness equipment. During this process, the user's movements are monitored in real time by sensors and sent to the server. The user then performs the exercises based on the presented plan, and the results are returned to the server as feedback.

[0575] Step 5:

[0576] The server continuously optimizes the exercise plan based on the results of the actions and emotional feedback. This optimization uses a generative AI model to analyze past data and incorporate the findings into the new exercise plan. The optimized plan is then sent back to the terminal and presented to the user.

[0577] An example of a prompt message would be: "A man in his 30s, 175cm tall and weighing 80kg, is aiming to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that increases the exercise intensity to support him."

[0578] 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.

[0579] 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.

[0580] 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.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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".

[0595] In order to implement the present invention, it is necessary to construct the following system. This system consists of a user, a terminal, and a server, and each element works in cooperation with one another.

[0596] First, the user enters their personal data (height, weight, age, etc.) and health goals (e.g., weight loss, muscle gain) into the device. The device receives this data and then transmits it to the server using a secure communication method.

[0597] Next, the server executes a generative artificial intelligence program based on the received user attribute data and health goals. This program designs an optimal exercise plan for the user, determining the content, frequency, and intensity of exercise according to the user's characteristics. This generated plan is then sent to the terminal by the server.

[0598] Next, the terminal prepares the VR environment based on the exercise plan received from the server. The user experiences the training through a VR headset or similar device using the virtual environment equipment. The terminal uses sensors to monitor the user's movements in real time and provide appropriate feedback. For example, if the user is not following the training instructions or has poor posture, a virtual instructor in the VR environment will give corrective instructions.

[0599] After the training session ends, the device sends the user's activity history and training results (e.g., calories burned, achievement of exercise schedule) back to the server. The server then analyzes these results and adjusts the exercise plan to reflect the findings in the next training session.

[0600] For example, if a 30-year-old user starts a program with the goal of losing 5 kilograms, the system will suggest a plan primarily focused on aerobic exercise, tailored to the user's basal metabolic rate and physical level. In the VR environment, simulations of running and arm exercises are displayed, and the system evaluates the user's movements in real time to provide an optimized fitness experience.

[0601] In this way, this system advances the personalization of fitness and provides users with a means to effectively and efficiently achieve their health goals.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] Users create an account using their device and enter their attribute data and health goals. The entered data includes height, weight, age, gender, and health goals (e.g., weight loss or muscle building).

[0605] Step 2:

[0606] The terminal receives the entered user data and verifies its format. It then sends the verified data to the server using a secure communication protocol.

[0607] Step 3:

[0608] The server stores the received user attribute data and health goals in a database and invokes a generative artificial intelligence (AI) based on this data. The AI ​​analyzes the data and generates a personalized exercise plan.

[0609] Step 4:

[0610] The server sends the generated exercise plan to the terminal and configures it to build a virtual environment that the user can use.

[0611] Step 5:

[0612] The device prepares a VR environment based on the exercise plan, allowing the user to begin training. Exercises are guided by a virtual instructor.

[0613] Step 6:

[0614] Users wear a VR headset and perform training in a virtual environment. During training, sensors monitor their movements and provide real-time feedback.

[0615] Step 7:

[0616] The device sends activity data collected during the training session to a server. This server records data such as calories burned and exercise achievement.

[0617] Step 8:

[0618] The server analyzes the collected data and adjusts the exercise plan for more effective planning of the next training session. As a result, a report is generated that allows the user to check their progress.

[0619] Step 9:

[0620] Users can check their training results on their device and receive advice on planning their next workout and areas for improvement. This allows users to continuously adjust their training towards their goals.

[0621] (Example 1)

[0622] 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".

[0623] In today's busy lifestyle, it's difficult for individuals to find the optimal fitness plan and efficiently achieve their health goals. Therefore, there's a need for a system that provides personalized exercise plans tailored to individual physical conditions and goals, and supports their implementation through a virtual environment, thereby effectively achieving health objectives.

[0624] 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.

[0625] In this invention, the server includes means for acquiring the user's biometric information and health goals, means for generating personalized exercise instructions based on the biometric information and health goals using an AI model generated by an information processing device, and means for presenting the exercise instructions through a virtual reality device, monitoring the user's actions, and providing feedback. This enables the provision of an optimal fitness plan tailored to the user's characteristics and real-time feedback according to the practice status.

[0626] A "user" is an individual who uses the system to achieve their own health goals.

[0627] "Biometric information" refers to data that indicates a user's physical attributes, including height, weight, and age.

[0628] "Health goals" refer to specific fitness and health-related objectives set by the user, such as weight loss or muscle gain.

[0629] An "information processing device" is a device that analyzes data and generates appropriate movement instructions.

[0630] A "generative AI model" is a program that includes artificial intelligence technology to design optimal exercise instructions for a user based on their biometric information and health goals.

[0631] A "virtual reality device" is a device used by users to experience exercise in a virtual environment, and includes headsets and the like.

[0632] "Exercise instructions" refer to fitness activity plans created by a generative AI model, including details such as content, frequency, and intensity.

[0633] "Feedback" refers to information provided to improve the accuracy and effectiveness of the user's exercise, and includes real-time guidance.

[0634] This invention is a system that generates personalized exercise plans and provides users with an optimal fitness experience. The system mainly consists of three elements: the user, the terminal, and the server, each of which works in cooperation with the others.

[0635] First, the user enters their biometric information (e.g., height, weight, age) and health goals (e.g., weight loss, muscle gain) into the device. The device then transmits this information to the server using a secure communication method (e.g., HTTPS).

[0636] Next, the server uses an information processing device to run a generative AI model, generating personalized exercise instructions based on the user's biometric information and health goals. This generative AI model considers past fitness data and the user's specific requests to determine the optimal exercise content, frequency, and intensity. For example, the AI ​​model might be given the prompt, "Generate an exercise plan for a 30-year-old male, 70kg, 180cm tall, aiming to lose 5kg."

[0637] Next, the terminal prepares a virtual reality environment for the user based on the exercise instructions received from the server. Using a virtual reality device (e.g., a VR headset), the user can then experience the training. The terminal uses sensors to monitor the user's movements in real time and verify whether the exercise is being performed as planned.

[0638] Feedback is provided after monitoring and analyzing the user's actions in detail. For example, if the user has poor posture or performs actions different from the instructions, a virtual instructor in the virtual reality environment will provide corrective instructions.

[0639] Finally, once the exercise session is complete, the device sends the user's exercise history and results to the server. The server uses this data to further improve exercise instructions for the next session.

[0640] In this way, this system provides users with a series of programs to effectively achieve their health goals, optimizing fitness according to their individual health status and objectives.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The user enters their biometric information and health goals into the terminal. The terminal sends this input data to the server using a protocol. The input includes the user's height, weight, age, and health goals, and the output is data transmitted in a secure format. This data is prepared for further processing on the server.

[0644] Step 2:

[0645] The server receives biometric information and health goals transmitted from the terminal and uses an information processing device to activate a generating AI model. Based on the input data, the AI ​​model generates personalized exercise instructions. Here, prompts are also useful, and the AI ​​model is instructed in the form of, "Generate an exercise plan for a 30-year-old man aiming to lose 5 kg." As output, it generates an exercise plan optimized for the user.

[0646] Step 3:

[0647] Upon receiving exercise instructions from the server, the terminal sets up the virtual reality environment. Specifically, it starts the VR headset and loads the virtual training environment for the user. The input is the exercise plan data, and the output is the completion of the virtual environment for the user to experience.

[0648] Step 4:

[0649] The device monitors the user's movements through a virtual reality environment. It uses sensors to acquire movement data in real time and verifies whether the user is following the movement instructions. Input is movement data obtained from the sensors, and output is appropriate feedback based on that data. If posture is poor, a virtual instructor automatically provides corrective instructions.

[0650] Step 5:

[0651] The device sends the results of the training session to the server. Here, performance data such as exercise history and calories burned are uploaded to the server. The server uses the transmitted data to improve the exercise instructions for the next session. The input is the training result data, and the output is stored as data used to adjust the next exercise plan.

[0652] (Application Example 1)

[0653] 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".

[0654] Providing individualized rehabilitation exercise plans and ensuring a safe and effective fitness experience for the elderly and those requiring rehabilitation is a challenge. Traditional fitness plans often offer generic programs that fail to adequately address the individual conditions and goals of users. Therefore, there is a need for methods that allow users to perform rehabilitation and exercise at a more appropriate pace.

[0655] 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.

[0656] In this invention, the server includes means for acquiring the user's biometric parameters, means for generating an individualized exercise plan based on the biometric parameters using a generation program, means for presenting the exercise plan through an augmented reality device, monitoring the user's physical movements according to instructions, and providing appropriate modifications, and means for analyzing the results of the physical movements and adjusting the exercise plan. This enables the user to safely and effectively engage in a fitness experience tailored to their individual health goals.

[0657] "User biometric parameters" refer to individually measured information such as the user's age, weight, body composition, and health status.

[0658] A "generative program" is software or an algorithm used to design an optimal exercise plan based on biological parameters and the user's goals.

[0659] An "individualized exercise plan" refers to a fitness plan designed to suit each individual user, taking into account their characteristics and goals.

[0660] An "augmented reality device" is hardware that allows users to experience training in a virtual environment, and typically refers to a head-mounted display or similar device.

[0661] "Monitoring of physical movement" is the process of observing and recording a user's movements and actions in real time using sensors, cameras, and other devices.

[0662] "Adjusting the exercise plan" is the process of modifying and optimizing the content and intensity of exercises in accordance with the user's progress and changes in their physical condition.

[0663] In implementing this invention, the server receives the user's biometric parameters. The terminal transmits data to the server using a secure communication method based on the information entered by the user. Using the user's biometric parameters such as age, weight, and health status, the generating program designs an optimal exercise plan. A generating AI model is utilized, enabling the program to provide personalized exercise guidance.

[0664] The server sends the generated exercise plan back to the terminal, which then displays the plan on an augmented reality device. Specifically, a head-mounted display is used to allow the user to experience the exercise in a virtual environment. The terminal utilizes sensors to monitor the user's physical movements in real time and determine whether the exercises are being performed correctly. If necessary, a virtual instructor provides feedback and prompts corrections to the form.

[0665] When a user finishes an exercise session, the device resends the exercise history and performance data to the server. Based on this, the server analyzes the received data and refines the next exercise plan. As a specific example, the generative AI model proposes personalized exercises in program statements that are tailored to the user's daily progress and physical condition.

[0666] An example of a prompt statement is as follows:

[0667] "Please generate a safe rehabilitation exercise plan for seniors based on user data. The exercises should focus on improving balance and strengthening leg muscles, and be suitable for home training."

[0668] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0669] Step 1:

[0670] The server receives biometric parameters and target data sent from the user via the terminal. These biometric parameters include information such as age, weight, and health status. Based on this data, the AI ​​model is prepared to design an exercise plan. This input prepares the system to generate an optimal exercise plan for the user.

[0671] Step 2:

[0672] The server uses a generative AI model to design a personalized exercise plan based on the received biometric parameters and target data. This AI model processes the parameters as input and determines the exercise content, frequency, and intensity according to the user's characteristics and goals. The resulting exercise plan is output. Specifically, the user selects from the various exercise combinations contained in the exercise plan.

[0673] Step 3:

[0674] The terminal receives the generated exercise plan and presents it to the user via an augmented reality device. A head-mounted display is used here, allowing the user to experience the exercise within a virtual environment. The output provides the user with instructions to begin the exercise. In this step, the virtual environment is visually and audibly set up and adjusted to allow the user to execute the prepared exercise plan.

[0675] Step 4:

[0676] The device uses sensors to monitor the user's physical movements in real time while they are exercising. If the user's movements deviate from the exercise plan, a virtual instructor provides corrective feedback to encourage improvement. This monitoring process takes data from the sensors as input and compares it to the exercise plan. Corrective instructions are generated as output.

[0677] Step 5:

[0678] After the exercise is completed, the device sends the user's exercise history and performance data to the server. This includes calories burned and the degree to which the exercise schedule was achieved. The server uses this data to analyze and further refine the next exercise plan. In this process, the input exercise history data is used as foundational data to adjust the next exercise plan. The generating AI model runs again, and an updated exercise plan is output.

[0679] 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.

[0680] This invention relates to a system that acquires user attribute data and objective data, and generates and optimizes personalized exercise plans using generative artificial intelligence and an emotion engine. The system consists of a user, a terminal, and a server.

[0681] First, the user uses their device to input their basic information (height, weight, age, gender, etc.) and health goals (e.g., weight loss, muscle gain). The device then sends this data to the server.

[0682] The server analyzes the received attribute and target data, and executes a generation artificial intelligence program to create an optimal exercise plan for the user. During this process, the emotion engine adjusts the plan, taking into account the user's current emotional state. The emotion engine infers emotions from sensor data and user input, and evaluates motivation and stress levels for exercise.

[0683] The generated exercise plan is sent to the device and set up so that the user can experience it in a virtual environment. The device uses a VR headset and other sensory feedback devices to provide the user with a realistic training experience. The device also feeds back the user's emotional changes during exercise to an emotion engine in real time, and instantly fine-tunes the intensity and content of the training based on that data.

[0684] As a concrete example, consider the case of a 30-year-old user who sets a goal of "losing 5 kilograms." The system tracks the user's emotional state, and if the stress level rises during exercise, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. On the other hand, if the user is positive and energetic about exercising, the system moderately increases the exercise intensity and provides a challenging plan.

[0685] After the session ends, the server analyzes the collected emotional data and exercise results to incorporate them into the next fitness plan for further optimization. A meal plan is also generated, providing comprehensive guidance to help users achieve their health goals more quickly.

[0686] This system allows users to have an efficient and personalized fitness experience that takes into account not only their physical but also their emotional state. This interactive and flexible training environment helps to achieve greater results and maintain motivation.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] Users create an account using their device and enter attribute data (e.g., height, weight, age) and goal data (e.g., weight loss, muscle gain). This data serves as the foundational information necessary for the user to achieve their fitness goals.

[0690] Step 2:

[0691] The terminal verifies attribute and objective data obtained from the user and transmits it to the server using a secure communication method. The transmitted data is integrated information including the user's characteristics and goals.

[0692] Step 3:

[0693] The server utilizes generative artificial intelligence to generate an optimized exercise plan for the user based on the received data. Here, the type, duration, and frequency of exercise are determined according to the user's physical characteristics and goals.

[0694] Step 4:

[0695] During the generation process, the server uses an emotion engine to analyze the user's emotional data and modifies the exercise plan based on this. If the user is in a high-stress state, it suggests exercises that promote relaxation; if the user is in a positive emotional state, it adds challenging exercises.

[0696] Step 5:

[0697] The server sends the final version of the plan to the device, which then prepares the training in the VR environment based on it. The user then puts on the VR headset and is ready to participate in the guided session.

[0698] Step 6:

[0699] The user begins training in a VR environment, and their movements are monitored in real time by the device. The device uses sensors to detect changes in the user's emotions along with their movements and sends the data to the emotion engine.

[0700] Step 7:

[0701] The device updates the exercise content and feedback as needed based on input from the emotion engine. For example, if the user starts to feel fatigued, measures such as slowing down the pace of the instructions are taken.

[0702] Step 8:

[0703] After the training session ends, the server analyzes the behavioral and emotional data reported from the terminal. Based on the results, it adjusts the next exercise plan and provides feedback to the user on their progress and areas for improvement.

[0704] Step 9:

[0705] Furthermore, the server also suggests meal plans that contribute to the user's health goals, supporting the overall fitness experience. These plans are regularly reviewed and continuously optimized by artificial intelligence.

[0706] (Example 2)

[0707] 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".

[0708] Traditional exercise plan generation systems typically generated exercise plans based on the user's physical attributes, but did not consider the user's emotional state. As a result, it was difficult to maintain user motivation and provide a personalized fitness experience. Furthermore, the lack of comprehensive coordination between exercise and meal plans limited the user's ability to achieve their health goals.

[0709] 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.

[0710] In this invention, the server includes means for acquiring user attribute information and purpose information, means for generating an individualized exercise plan based on the attribute information and purpose information using artificial intelligence, and means for evaluating the user's emotional state through emotion analysis and adjusting the exercise plan accordingly. This makes it possible to provide a flexible exercise plan that responds to the user's emotional state.

[0711] "User attribute information" refers to basic personal information about the user, such as height, weight, age, and gender.

[0712] "Purpose information" refers to information about the health and fitness goals that the user intends to achieve.

[0713] "Generative artificial intelligence" refers to a computer program that has the ability to automatically create specific plans and recommendations based on the user's attribute information and objective information.

[0714] An "exercise plan" refers to a schedule of specific exercises and workouts created based on the user's health goals.

[0715] "Emotional analysis" refers to the process of using sensor data and other input data to evaluate a user's emotional state and infer their mental state.

[0716] A "nutrition plan" refers to a set of dietary and nutritional guidance guidelines created to support users in achieving their health goals.

[0717] A "learning algorithm" refers to a computer algorithm that has the ability to learn from data and use the results to continuously improve and optimize its plans.

[0718] This invention is configured as a system that provides a personalized fitness experience for users. The system mainly consists of a user, a terminal, and a server.

[0719] First, the user uses their device to input basic attribute information (e.g., height, weight, age, gender) and health goals they wish to achieve (e.g., weight loss, muscle gain). The device then transmits this information to the server via a secure communication protocol.

[0720] The server uses a database management system and data cleansing algorithms to analyze the received data. The generated artificial intelligence model creates an optimal exercise plan from attribute information and objective information. In this process, the server uses an emotion analysis engine to evaluate the user's emotions. Specifically, it uses information obtained from sensors and input data, such as heart rate and voice data, to infer the user's current emotional state.

[0721] The generated exercise plan is organized in XML or JSON format and sent to the terminal. The terminal uses a VR headset and sensory feedback devices to create an environment where the user can experience exercise in a virtual environment. During this process, the terminal feeds back the user's real-time emotional changes to the server, and the exercise plan is adjusted as needed.

[0722] As a concrete example, consider a case where a 30-year-old user sets a goal of "losing 5 kilograms." In this case, the system tracks the user's emotional state during exercise, and if an increase in stress levels is detected, it either lowers the exercise intensity or switches to an exercise mode that promotes relaxation. If a positive and energetic state is maintained, the system increases the exercise intensity and provides a more challenging plan.

[0723] An example of a prompt sentence for a generative AI model is, "How can we optimize an exercise plan for a 30-year-old user aiming to lose 5 kg, taking into account the emotional engine?" In this way, the system provides a personalized plan that takes into account the user's physical and emotional state, helping them achieve their health goals more effectively.

[0724] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0725] Step 1:

[0726] Users input basic attribute information (height, weight, age, gender) and health goals (e.g., weight loss, muscle gain) using their own devices. Once this information is entered into the device, the device generates a data packet and sends it to the server after ensuring security. The input data is the basic information necessary to generate a personalized exercise plan for the user.

[0727] Step 2:

[0728] The server receives attribute and objective information sent from the terminal. It applies a data cleansing algorithm to process the data, such as filtering outliers, to prepare a clean dataset. Next, it launches a generative AI model, using the refined data as a prompt to generate the optimal movement plan. This process outputs an initial draft of the movement plan best suited to the user's objective.

[0729] Step 3:

[0730] The server uses an emotion analysis engine to evaluate the user's emotional state on the generated exercise plan. Input consists of emotional data from sensors and self-reported data. The emotion analysis analyzes heart rate and voice tone data to perform data calculations that estimate stress levels and motivation. Based on this, the exercise plan is adjusted in real time, and the optimal plan is maintained on the server.

[0731] Step 4:

[0732] The server sends a pre-configured exercise plan to the terminal. The terminal integrates it into a VR application and prepares it for the user. The user experiences the exercise using a VR headset and other sensory feedback devices. The output here is the exercise instructions the user receives in the virtual environment. The terminal provides real-time feedback of emotional and physiological data obtained from the user during the exercise.

[0733] Step 5:

[0734] The server uses real-time feedback data received from the terminal to re-evaluate the exercise plan and make modifications if necessary. This process stores a history in a database, which is referenced when generating the next exercise plan. As a result, it becomes possible to support users in achieving their long-term health goals more efficiently.

[0735] This series of processes allows users to enjoy a personalized fitness experience that meets their physical and emotional needs.

[0736] (Application Example 2)

[0737] 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".

[0738] In modern society, there is a demand for exercise plans tailored to individual needs. However, conventional systems have not adequately considered the emotional state of users when personalizing exercise plans, making it difficult to maintain motivation and improve exercise efficiency. It is necessary to address these challenges to provide an optimized exercise experience for each user and promote health.

[0739] 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.

[0740] In this invention, the server includes means for acquiring user attribute data and objective data, means for generating an individualized exercise plan based on the attribute data and objective data using generative artificial intelligence, and means for inferring the user's emotional state using an emotion analysis device and adjusting the exercise plan. This makes it possible to provide an optimal exercise plan that takes into account both the user's physical characteristics and emotional state.

[0741] "User attribute data" refers to basic information used to identify or specify an individual, such as a user's height, weight, age, and gender.

[0742] "Purpose data" refers to information that indicates the health goals or fitness objectives that the user intends to achieve.

[0743] "Generative artificial intelligence" is an artificial intelligence technology equipped with advanced algorithms for generating exercise plans based on acquired data.

[0744] An "exercise plan" is a detailed plan that outlines the schedule and content of fitness exercises tailored to the user's health goals.

[0745] An "emotion analysis device" is a device that determines the emotional state of a user from their facial expressions, voice, and behavior, and provides feedback based on that determination.

[0746] A "virtual environment device" is a device that uses virtual reality technology to provide users with an interactive and immersive training experience.

[0747] "Physical fitness equipment" refers to devices and machines that allow users to actually move their bodies and exercise.

[0748] "Emotional feedback" refers to feedback information that analyzes the user's emotional state and provides information and guidance accordingly.

[0749] In this invention, the system provides a personalized exercise plan based on the user's input of attribute data and goal data. Specifically, the user uses a terminal to send basic information and fitness goals to the server. The server uses artificial intelligence to analyze this data and generate an optimal exercise plan. The generated exercise plan is presented to the user through a virtual environment device or physical fitness equipment.

[0750] Emotion analysis devices identify emotional states from a user's facial expressions and voice, and provide feedback to the motor plan. This process utilizes technologies such as voice analysis and image analysis. Specifically, voice processing software and image processing libraries are used to analyze the user's voice tone and facial expressions.

[0751] For example, if a user complains of fatigue during exercise, the system can monitor the user's emotional state in real time and adjust the exercise intensity as needed. This allows the user to continue training at their own pace.

[0752] The generating AI model analyzes the user's past data to further optimize the exercise plan. This process runs on a cloud server, leveraging advanced computing power. A continuous data feedback loop is in place to enhance the user's exercise experience. An example of a prompt is: "A man in his 30s, 175cm tall and weighing 80kg, aims to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that supports him by increasing the exercise intensity."

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The server receives user attribute data and purpose data from the terminal. This includes height, weight, age, gender, and fitness goals. Based on this input data, the server stores it in a database and prepares it for analysis.

[0756] Step 2:

[0757] The server uses generative artificial intelligence to analyze the received data and generate a personalized exercise plan. This analysis includes comparing and fitting the user's data to a dataset of existing exercise plans. The generated exercise plan is output as a series of steps of exercises to be performed within a virtual environment.

[0758] Step 3:

[0759] The server collects user emotional data using an emotion analysis device and performs real-time analysis. The input includes the user's facial expressions and voice tone, which are evaluated through facial expression analysis software and speech recognition technology. The analysis results are provided as feedback necessary for adjusting the user's exercise plan.

[0760] Step 4:

[0761] The terminal presents the user with an exercise plan sent from the server, using either a virtual environment device or physical fitness equipment. During this process, the user's movements are monitored in real time by sensors and sent to the server. The user then performs the exercises based on the presented plan, and the results are returned to the server as feedback.

[0762] Step 5:

[0763] The server continuously optimizes the exercise plan based on the results of the actions and emotional feedback. This optimization uses a generative AI model to analyze past data and incorporate the findings into the new exercise plan. The optimized plan is then sent back to the terminal and presented to the user.

[0764] An example of a prompt message would be: "A man in his 30s, 175cm tall and weighing 80kg, is aiming to lose 5kg. The user appears energetic and is eager to run today. Please generate a fitness plan that increases the exercise intensity to support him."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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."

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] The following is further disclosed regarding the embodiments described above.

[0787] (Claim 1)

[0788] Means for obtaining user attribute data and target data,

[0789] A means for generating an individualized exercise plan based on attribute data and objective data using generative artificial intelligence,

[0790] A means for presenting the exercise plan through a virtual environment device, monitoring the user's actions according to the instructions, and providing feedback as appropriate,

[0791] Means for analyzing the results of the aforementioned actions and adjusting the aforementioned movement plan,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, wherein the means generates and presents a meal plan based on the generated exercise plan.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the means accumulates the user's movement history and continuously optimizes the exercise plan using artificial intelligence.

[0797] "Example 1"

[0798] (Claim 1)

[0799] Means for acquiring the user's biometric information and health goals,

[0800] A means for generating personalized exercise instructions based on the biometric information and health goals using an AI model generated by an information processing device,

[0801] A means for presenting the aforementioned movement instructions through a virtual reality device, monitoring the performer's actions, and providing feedback,

[0802] A means for analyzing the results of the aforementioned actions and improving the aforementioned motor instructions,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein the means generates and presents nutritional instructions based on the generated exercise instructions.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the means store a record of the user's actions and continuously improves the exercise instructions using an information processing device.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of obtaining the user's biometric parameters,

[0811] A means for generating an individualized motor plan based on the biological parameters using a generation program,

[0812] A means for presenting the aforementioned movement plan through an augmented reality device, monitoring the user's physical movements according to the instructions, and providing appropriate modifications,

[0813] Means for analyzing the results of the aforementioned physical movements and adjusting the aforementioned movement plan,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, wherein the means generates and presents a nutrition plan based on the generated exercise plan.

[0817] (Claim 3)

[0818] The system according to claim 1, wherein the means accumulates the user's physical movement history and continuously optimizes the exercise plan using artificial intelligence.

[0819] "Example 2 of combining an emotion engine"

[0820] (Claim 1)

[0821] Means for obtaining user attribute information and purpose information,

[0822] A means for generating an individualized motion plan based on attribute information and objective information using artificial intelligence,

[0823] A means for evaluating the user's emotional state through emotion analysis and adjusting the exercise plan based on that,

[0824] A means for presenting the exercise plan through a virtual environment device, monitoring the user's actions based on the instructions, and providing feedback as appropriate,

[0825] A means of analyzing the results of the aforementioned actions and feeding them back into the next plan,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the means generates and presents a nutrition plan based on the generated exercise plan.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the means accumulates the user's behavior history and continuously optimizes the movement plan using a learning algorithm.

[0831] "Application example 2 when combining with an emotional engine"

[0832] (Claim 1)

[0833] Means for obtaining user attribute data and target data,

[0834] A means for generating an individualized exercise plan based on attribute data and objective data using generative artificial intelligence,

[0835] A means of using an emotion analysis device to estimate the user's emotional state and adjust the exercise plan accordingly,

[0836] A means for presenting the exercise plan through a virtual environment device and a physical fitness device, monitoring the user's movements according to the instructions, and providing real-time feedback as needed,

[0837] Means for analyzing the results of the aforementioned actions and emotional feedback, and for continuously optimizing the aforementioned movement plan,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the means generates and presents a meal plan based on the generated exercise plan and the user's emotional state.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the means accumulates the user's movement history and emotional changes, and continuously optimizes the exercise plan using generative artificial intelligence. [Explanation of symbols]

[0843] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining the user's biometric parameters, A means for generating an individualized motor plan based on the biological parameters using a generation program, A means for presenting the aforementioned movement plan through an augmented reality device, monitoring the user's physical movements according to the instructions, and providing appropriate modifications, Means for analyzing the results of the aforementioned physical movements and adjusting the aforementioned movement plan, A system that includes this.

2. The system according to claim 1, wherein the means generates and presents a nutrition plan based on the generated exercise plan.

3. The system according to claim 1, wherein the means accumulates the user's physical movement history and continuously optimizes the exercise plan using artificial intelligence.

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