system

Abstract

The system according to this embodiment aims to efficiently provide individualized training plans and nutritional guidance. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a feedback unit. The collection unit collects information about the user's goals, health status, and lifestyle. The generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. The feedback unit provides real-time feedback based on the plans generated by the generation unit.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] z In the prior art, a lot of time and specialized knowledge are required to provide individual training plans and nutritional guidance, which is difficult to implement for people with busy lives.

[0005] [The system according to the embodiment aims to efficiently provide individual training plans and nutritional guidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a data generation unit, and a feedback unit. The data collection unit collects information about the user's goals, health status, and lifestyle. The data generation unit analyzes the information collected by the data collection unit and generates individual training plans and nutritional guidance. The feedback unit provides real-time feedback based on the plans generated by the data generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide individualized training plans and nutritional guidance. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The home fitness AI system according to an embodiment of the present invention is a system targeted at people who are highly interested in health and fitness and who seek personalized training and nutritional guidance. This system provides accurate advice from AI, especially to people who lead busy lives or lack knowledge about training. Specifically, it consists of the following steps: First, the user inputs information about their goals for home fitness, current health status, and lifestyle. Next, the AI ​​analyzes this information and generates a personalized training plan and nutritional guidance. The generated plan is adjusted to the user's schedule and fitness level. Furthermore, as the user performs training, the AI ​​provides real-time feedback, correcting form and monitoring training progress. This allows the user to train effectively at home. For example, if the user's goal is weight loss, the AI ​​analyzes the user's diet and exercise level and provides a plan to manage calorie intake and expenditure. For users aiming to increase muscle strength, it provides appropriate training menus and nutritional advice. Furthermore, for users aiming to reduce stress, it provides relaxation exercises and mental health care advice. This system allows users to easily perform fitness at home and maximize the effects of health management and training. Furthermore, because the AI ​​provides personalized advice tailored to individual needs, it is suitable for fitness beginners and people with busy lifestyles. This allows home fitness AI systems to maximize the user's health management and training effectiveness.

[0029] The home fitness AI system according to the embodiment comprises a collection unit, a generation unit, and a feedback unit. The collection unit collects information about the user's goals, health status, and lifestyle. For example, the collection unit stores information about the user's goals, health status, and lifestyle in a database. The collection unit can collect specific details as the user's goals, such as weight loss, muscle building, and maintaining health. The collection unit can collect specific indicators as the user's health status, such as blood pressure, heart rate, and body fat percentage. The collection unit can collect specific elements as the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. The generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. For example, the generation unit analyzes the collected information using data analysis methods and algorithms. The generation unit can generate specific details as a training plan tailored to the user's needs, such as the type, frequency, and intensity of exercise. The generation unit can generate specific details as nutritional guidance tailored to the user's needs, such as dietary balance, calorie intake, and nutrient distribution. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can perform generation using an AI model that takes information collected by the collection unit as input and outputs individual training plans and nutritional guidance. The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, the feedback unit monitors form corrections and training progress when the user is performing training. When the user is performing training, the feedback unit can provide, for example, points for posture adjustments and movement corrections as form corrections. When the user is performing training, the feedback unit can provide, for example, methods for monitoring achievement and progress as training progress. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI.For example, the feedback unit can take the plan generated by the generation unit as input and provide feedback using an AI model that provides real-time feedback. As a result, the home fitness AI system according to the embodiment can provide individualized training plans and nutritional guidance tailored to the user's goals, health condition, and lifestyle, and provide real-time feedback, enabling effective training.

[0030] The data collection unit collects information about the user's goals, health status, and lifestyle. Specifically, it stores the user's input information about their goals, health status, and lifestyle in a database. The data collection unit can collect specific details about the user's goals, such as weight loss, muscle building, and maintaining health. This includes detailed goals such as the weight, muscle mass, and health indicators the user wants to achieve. The data collection unit can collect specific indicators about the user's health status, such as blood pressure, heart rate, and body fat percentage. This includes the results of regular health checkups and health data that the user measures daily. The data collection unit can collect specific elements about the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. This includes the content and calories of the food the user eats daily, the type and frequency of exercise, and the duration and quality of sleep. The data collection unit centrally manages this information and builds a customized database for each user. Furthermore, the data collection unit regularly updates user information and maintains the latest data, ensuring that the generation and feedback units can always process information based on accurate data. The data collection unit can collect not only user input data but also data from wearable devices and smartphone apps. This allows for a more detailed and accurate understanding of the user's health status and lifestyle. For example, it can collect data such as heart rate, steps taken, and calories burned in real time from wearable devices, and collect meal records and exercise logs from smartphone apps. As a result, the data collection unit can comprehensively collect information on the user's health status and lifestyle, improving the accuracy and effectiveness of the entire system.

[0031] The generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. Specifically, it analyzes the collected information using data analysis methods and algorithms. The generation unit can generate training plans tailored to the user's needs, including specific details such as the type, frequency, and intensity of exercise. This includes designing an optimal exercise program based on the user's goals, health condition, and lifestyle. For example, it can generate a plan centered on aerobic exercise for users aiming to lose weight, and a plan centered on strength training for users aiming to increase muscle mass. The generation unit can also generate nutritional guidance tailored to the user's needs, including specific details such as meal balance, calorie intake, and nutrient distribution. This includes designing a meal plan tailored to the user's goals and health condition. For example, it can generate a low-calorie, nutritionally balanced meal plan for users aiming to lose weight, and a high-protein, energy-supply-appropriate meal plan for users aiming to increase muscle mass. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use an AI model that takes information collected by the collection unit as input and outputs individual training plans and nutritional guidance. The AI ​​model uses an algorithm to generate the optimal plan for the user's needs based on past data and statistical information. This allows the generation unit to provide customized training plans and nutritional guidance for each user, supporting effective training. Furthermore, the generation unit can continuously improve the training plans and nutritional guidance based on user feedback. This allows the generation unit to always provide the optimal plan based on the latest information, supporting the user in achieving their goals.

[0032] The feedback unit provides real-time feedback based on the plan generated by the generation unit. Specifically, it monitors the user's form and training progress as they perform their workouts. When the user performs their workouts, the feedback unit can provide, for example, corrections to posture and points for correcting movement as part of form corrections. This includes specific advice and instructions to help the user perform the workout with the correct form. For example, it can provide real-time instructions on fine details such as knee position, back angle, and arm movement when performing squats. When the user performs their workouts, the feedback unit can provide methods for monitoring achievement and progress as part of training progress. This includes specific indicators for evaluating progress towards user-set goals and training results. For example, it can monitor the number of repetitions and sets, weight used, and calories burned in real time and provide feedback to the user. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can use an AI model that takes the plan generated by the generation unit as input and provides real-time feedback. The AI ​​model uses algorithms to analyze user training data and provide optimal feedback. This allows the feedback system to support users in effectively training and achieving their goals. Furthermore, the feedback system can continuously improve training plans and feedback content based on user feedback. This ensures that the feedback system always provides the most up-to-date and optimal support, helping users achieve their goals.

[0033] The data collection unit can collect information about the user's goals, current health status, and lifestyle. For example, the data collection unit can store the user's entered information about their goals, health status, and lifestyle in a database. The data collection unit can collect specific details about the user's goals, such as weight loss, muscle building, and maintaining health. The data collection unit can collect specific indicators about the user's health status, such as blood pressure, heart rate, and body fat percentage. The data collection unit can collect specific elements about the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. By collecting information about the user's goals, current health status, and lifestyle, the data collection unit can obtain basic data for providing individualized training plans and nutritional guidance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the information entered by the user into a generating AI and have the generating AI perform the information collection.

[0034] The generation unit can analyze the collected information and generate training plans and nutritional guidance tailored to the user's needs. The generation unit analyzes the collected information using, for example, data analysis methods and algorithms. The generation unit can generate specific details for training plans tailored to the user's needs, such as the type of exercise, frequency, and intensity. The generation unit can generate specific details for nutritional guidance tailored to the user's needs, such as the balance of meals, calorie intake, and nutrient distribution. In this way, the generation unit can provide advice tailored to individual needs by analyzing the collected information and generating training plans and nutritional guidance tailored to the user's needs. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the information collected by the collection unit into a generation AI and have the generation AI execute the generation of training plans and nutritional guidance.

[0035] The feedback unit can provide real-time feedback to users as they perform training. For example, the feedback unit can monitor form corrections and training progress as the user performs training. As a form correction, the feedback unit can provide points for adjusting posture and correcting movements as the user performs training. As a training progress, the feedback unit can provide methods for monitoring achievement and progress as the user performs training. In this way, the feedback unit can maximize the effectiveness of training by providing real-time feedback to users as they perform training. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the plan generated by the generation unit into the generation AI and have the generation AI perform the process of providing real-time feedback.

[0036] The generation unit can adjust the plan to match the user's schedule and fitness level. For example, the generation unit adjusts the plan considering the user's schedule, such as daily appointments and training times. The generation unit also adjusts the plan considering specific indicators such as endurance, strength, and flexibility as part of the user's fitness level. In this way, the generation unit can provide training that is not overly strenuous by adjusting the plan to match the user's schedule and fitness level. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input information about the user's schedule and fitness level into a generation AI and have the generation AI perform the plan adjustments.

[0037] The feedback unit can monitor form corrections and training progress. For example, when a user is performing training, the feedback unit provides points for correcting posture and movement as form corrections. For example, when a user is performing training, the feedback unit provides methods for monitoring achievement and progress as training progress. In this way, by correcting form and monitoring training progress, the feedback unit can ensure that training is performed with correct form and effective training is achieved. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's training data into a generating AI and have the generating AI perform form corrections and monitor training progress.

[0038] The data collection unit can analyze the user's past health data and select the optimal information collection method. For example, the data collection unit can identify a tendency for health to deteriorate during specific time periods from the user's past health data and focus on collecting information during those times. Based on the user's past health data, the data collection unit can analyze the impact of specific diets or exercises on health and adjust the information collection method accordingly. The data collection unit can analyze the user's past health data and adjust the timing of information collection according to seasonal changes in health. In this way, the data collection unit can select the optimal information collection method and perform effective information collection by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the information collection method.

[0039] The data collection unit can filter information based on the user's current activity level and dietary content. For example, if the user is at a high activity level, the data collection unit will prioritize collecting information related to post-exercise recovery. After the user consumes a particular meal, the data collection unit can analyze the impact of that meal on their health and collect relevant information. If the user is at a low activity level, the data collection unit can collect information related to light exercise or stretching to improve their health. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current activity level and dietary content. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's activity level and dietary content into a generating AI and have the generating AI perform the information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit can collect training and nutrition information suitable for the climate and environment of that region. If the user is traveling, the data collection unit can collect information about fitness facilities and health foods available at the travel destination. If the user is at home, the data collection unit can prioritize the collection of information about training and nutrition guidance that can be done at home. In this way, the data collection unit can provide information that is suitable for the user's environment by considering the user's geographical location when collecting information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze training and meal content shared by the user on social media and collect relevant information. The data collection unit can analyze posts from fitness influencers followed by the user and collect relevant information. The data collection unit can collect relevant information based on health topics the user has shown interest in on social media. In this way, the data collection unit can collect information based on the user's interests and concerns by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0042] The generation unit can adjust the level of detail of the plan based on the user's health goals during generation. For example, if the user's goal is weight loss, the generation unit can provide a detailed plan focused on calorie management and fat burning. If the user's goal is muscle building, the generation unit can provide a detailed plan focused on strength training and nutrition. If the user's goal is stress reduction, the generation unit can provide a detailed plan focused on relaxation exercises and mental health care. By adjusting the level of detail of the plan based on the user's health goals, the generation unit can provide specific advice to help the user achieve their goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health goal information into a generation AI and have the generation AI adjust the level of detail of the plan.

[0043] The generation unit can apply different generation algorithms depending on the user's health condition during generation. For example, if the user is in good health, the generation unit can apply a normal training plan generation algorithm. If the user's health condition is deteriorating, the generation unit can apply a training plan generation algorithm that prioritizes recovery. If the user has a specific health problem, the generation unit can apply a training plan generation algorithm that addresses that problem. In this way, by applying different generation algorithms according to the user's health condition, the generation unit can provide the optimal training plan and nutritional guidance for the user's condition. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about the user's health condition into a generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine the priority of plans based on the user's daily rhythm during generation. For example, if the user is a morning person, the generation unit can provide a plan that prioritizes morning training. If the user is a night owl, the generation unit can provide a plan that prioritizes evening training. The generation unit can provide plans that adjust the timing of training to match the user's daily rhythm. In this way, by determining the priority of plans based on the user's daily rhythm, the generation unit can provide training plans that are tailored to the user's lifestyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about the user's daily rhythm into a generation AI and have the generation AI perform the determination of plan priorities.

[0045] The generation unit can adjust the order of the plan based on the user's relevant health data during generation. For example, the generation unit can provide an optimal training order based on the user's past training data. The generation unit can analyze the user's health data and adjust the training order. The generation unit can adjust the training order according to the user's fitness level. In this way, the generation unit can provide a training plan that is optimal for the user's condition by adjusting the order of the plan based on the user's relevant health data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's relevant health data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0046] The feedback unit can provide optimal feedback by referring to the user's training history during the feedback process. For example, the feedback unit can evaluate the user's progress based on their past training history and suggest the next steps. The feedback unit can analyze the effectiveness of a particular training session from the user's training history and provide feedback on areas for improvement. The feedback unit can refer to the user's training history and provide feedback to maintain motivation. In this way, the feedback unit can provide optimal feedback according to the user's progress by referring to their training history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's training history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0047] The feedback unit can adjust the intensity of the feedback according to the user's fitness level. For example, if the user's fitness level is high, the feedback unit can provide challenging feedback. If the user's fitness level is low, the feedback unit can provide gentle feedback. The feedback unit can adjust the content of the feedback according to the user's fitness level. In this way, the feedback unit can provide the optimal feedback for the user by adjusting the intensity of the feedback according to the user's fitness level. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input information about the user's fitness level into a generating AI and have the generating AI adjust the intensity of the feedback.

[0048] The feedback unit can provide optimal feedback by taking into account the user's geographical location information. For example, if the user is in a specific region, the feedback unit can provide feedback appropriate to the climate and environment of that region. If the user is traveling, the feedback unit can provide feedback on fitness facilities and health foods available at the travel destination. If the user is at home, the feedback unit can provide feedback on training and nutritional guidance that can be done at home. In this way, the feedback unit can provide feedback appropriate to the user's environment by taking into account the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0049] The feedback unit can analyze the user's social media activity and adjust the content of the feedback when providing it. For example, the feedback unit can analyze the training and meal content shared by the user on social media and provide relevant feedback. The feedback unit can analyze the content of posts by fitness influencers that the user follows and provide relevant feedback. The feedback unit can provide relevant feedback based on the health topics that the user has shown interest in on social media. In this way, the feedback unit can provide feedback based on the user's interests and concerns by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the feedback.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can more accurately understand a user's current health status by referencing the user's past health data when collecting information about the user's health. For example, the data collection unit can analyze the user's past blood pressure and heart rate data and compare it to their current health status to understand changes in their health. Furthermore, the data collection unit can identify areas for improvement in the user's current lifestyle by referencing the user's past diet and exercise data. In addition, the data collection unit can analyze the user's past sleep patterns and evaluate the quality of their current sleep. This allows the data collection unit to gather more accurate information by utilizing the user's past health data.

[0052] The generation unit can provide an optimal training plan by referencing the user's past training data when generating a user's training plan. For example, the generation unit can analyze the results of the user's past training and identify which training was effective. It can also adjust the current training plan by referencing the frequency and intensity of the user's past training. Furthermore, the generation unit can analyze changes in the user's motivation over time and provide a training plan to maintain that motivation. In this way, the generation unit can leverage the user's past training data to provide a more effective training plan.

[0053] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering information about the user's health status. For example, if the user is in a specific region, the data collection unit can collect health information that is appropriate for the climate and environment of that region. Furthermore, if the user is traveling, the data collection unit can collect information about fitness facilities and health foods available at their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of information about home-based training and nutritional guidance. In this way, the data collection unit can provide information tailored to the user's environment by considering their geographical location when collecting information.

[0054] The feedback unit can provide optimal feedback to users by referring to their training history as they perform training. For example, the feedback unit can evaluate the user's progress based on their past training history and suggest the next steps. It can also analyze the effectiveness of specific training sessions from the user's training history and provide feedback on areas for improvement. Furthermore, the feedback unit can refer to the user's training history and provide feedback to maintain motivation. In this way, the feedback unit can provide optimal feedback tailored to the user's progress by referring to their training history.

[0055] The data collection unit can collect relevant information by analyzing users' social media activity when gathering information about their health status. For example, it can analyze training and dietary information shared by users on social media and collect relevant information. It can also analyze posts from fitness influencers that users follow and collect relevant information. Furthermore, it can collect relevant information based on health topics that users have shown interest in on social media. In this way, the data collection unit can collect information based on users' interests and concerns by analyzing users' social media activity.

[0056] The feedback unit can provide optimal feedback to users when they are training, taking into account their geographical location. For example, if the user is in a specific region, the feedback unit can provide feedback that is appropriate for the climate and environment of that region. Furthermore, if the user is traveling, the feedback unit can provide feedback on fitness facilities and health foods available at their destination. Additionally, if the user is at home, the feedback unit can provide feedback on home-based training and nutritional guidance. In this way, the feedback unit can provide feedback that is appropriate for the user's environment by considering their geographical location.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects information about the user's goals, health status, and lifestyle. For example, it stores information about the user's goals, health status, and lifestyle in a database. The data collection unit can collect specific details about the user's goals, such as weight loss, muscle building, and maintaining health. It can also collect specific indicators about the user's health status, such as blood pressure, heart rate, and body fat percentage. Furthermore, it can collect specific elements about the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. Step 2: The generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. For example, it analyzes the collected information using data analysis methods and algorithms. The generation unit can generate specific details such as the type, frequency, and intensity of exercise as a training plan tailored to the user's needs. It can also generate specific details such as the balance of meals, calorie intake, and nutrient distribution as nutritional guidance tailored to the user's needs. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can perform generation using an AI model that takes the information collected by the collection unit as input and outputs individual training plans and nutritional guidance. Step 3: The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, it monitors form corrections and training progress when the user is performing training. As form corrections, the feedback unit can provide points for adjusting posture and correcting movements when the user is performing training. As training progress, it can provide methods for monitoring achievement and progress. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can use an AI model that takes the plan generated by the generation unit as input and provides feedback in real time.

[0059] (Example of form 2) The home fitness AI system according to an embodiment of the present invention is a system targeted at people who are highly interested in health and fitness and who seek personalized training and nutritional guidance. This system provides accurate advice from AI, especially to people who lead busy lives or lack knowledge about training. Specifically, it consists of the following steps: First, the user inputs information about their goals for home fitness, current health status, and lifestyle. Next, the AI ​​analyzes this information and generates a personalized training plan and nutritional guidance. The generated plan is adjusted to the user's schedule and fitness level. Furthermore, as the user performs training, the AI ​​provides real-time feedback, correcting form and monitoring training progress. This allows the user to train effectively at home. For example, if the user's goal is weight loss, the AI ​​analyzes the user's diet and exercise level and provides a plan to manage calorie intake and expenditure. For users aiming to increase muscle strength, it provides appropriate training menus and nutritional advice. Furthermore, for users aiming to reduce stress, it provides relaxation exercises and mental health care advice. This system allows users to easily perform fitness at home and maximize the effects of health management and training. Furthermore, because the AI ​​provides personalized advice tailored to individual needs, it is suitable for fitness beginners and people with busy lifestyles. This allows home fitness AI systems to maximize the user's health management and training effectiveness.

[0060] The home fitness AI system according to the embodiment comprises a collection unit, a generation unit, and a feedback unit. The collection unit collects information about the user's goals, health status, and lifestyle. For example, the collection unit stores information about the user's goals, health status, and lifestyle in a database. The collection unit can collect specific details as the user's goals, such as weight loss, muscle building, and maintaining health. The collection unit can collect specific indicators as the user's health status, such as blood pressure, heart rate, and body fat percentage. The collection unit can collect specific elements as the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. The generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. For example, the generation unit analyzes the collected information using data analysis methods and algorithms. The generation unit can generate specific details as a training plan tailored to the user's needs, such as the type, frequency, and intensity of exercise. The generation unit can generate specific details as nutritional guidance tailored to the user's needs, such as dietary balance, calorie intake, and nutrient distribution. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can perform generation using an AI model that takes information collected by the collection unit as input and outputs individual training plans and nutritional guidance. The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, the feedback unit monitors form corrections and training progress when the user is performing training. When the user is performing training, the feedback unit can provide, for example, points for posture adjustments and movement corrections as form corrections. When the user is performing training, the feedback unit can provide, for example, methods for monitoring achievement and progress as training progress. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI.For example, the feedback unit can take the plan generated by the generation unit as input and provide feedback using an AI model that provides real-time feedback. As a result, the home fitness AI system according to the embodiment can provide individualized training plans and nutritional guidance tailored to the user's goals, health condition, and lifestyle, and provide real-time feedback, enabling effective training.

[0061] The data collection unit collects information about the user's goals, health status, and lifestyle. Specifically, it stores the user's input information about their goals, health status, and lifestyle in a database. The data collection unit can collect specific details about the user's goals, such as weight loss, muscle building, and maintaining health. This includes detailed goals such as the weight, muscle mass, and health indicators the user wants to achieve. The data collection unit can collect specific indicators about the user's health status, such as blood pressure, heart rate, and body fat percentage. This includes the results of regular health checkups and health data that the user measures daily. The data collection unit can collect specific elements about the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. This includes the content and calories of the food the user eats daily, the type and frequency of exercise, and the duration and quality of sleep. The data collection unit centrally manages this information and builds a customized database for each user. Furthermore, the data collection unit regularly updates user information and maintains the latest data, ensuring that the generation and feedback units can always process information based on accurate data. The data collection unit can collect not only user input data but also data from wearable devices and smartphone apps. This allows for a more detailed and accurate understanding of the user's health status and lifestyle. For example, it can collect data such as heart rate, steps taken, and calories burned in real time from wearable devices, and collect meal records and exercise logs from smartphone apps. As a result, the data collection unit can comprehensively collect information on the user's health status and lifestyle, improving the accuracy and effectiveness of the entire system.

[0062] The generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. Specifically, it analyzes the collected information using data analysis methods and algorithms. The generation unit can generate training plans tailored to the user's needs, including specific details such as the type, frequency, and intensity of exercise. This includes designing an optimal exercise program based on the user's goals, health condition, and lifestyle. For example, it can generate a plan centered on aerobic exercise for users aiming to lose weight, and a plan centered on strength training for users aiming to increase muscle mass. The generation unit can also generate nutritional guidance tailored to the user's needs, including specific details such as meal balance, calorie intake, and nutrient distribution. This includes designing a meal plan tailored to the user's goals and health condition. For example, it can generate a low-calorie, nutritionally balanced meal plan for users aiming to lose weight, and a high-protein, energy-supply-appropriate meal plan for users aiming to increase muscle mass. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use an AI model that takes information collected by the collection unit as input and outputs individual training plans and nutritional guidance. The AI ​​model uses an algorithm to generate the optimal plan for the user's needs based on past data and statistical information. This allows the generation unit to provide customized training plans and nutritional guidance for each user, supporting effective training. Furthermore, the generation unit can continuously improve the training plans and nutritional guidance based on user feedback. This allows the generation unit to always provide the optimal plan based on the latest information, supporting the user in achieving their goals.

[0063] The feedback unit provides real-time feedback based on the plan generated by the generation unit. Specifically, it monitors the user's form and training progress as they perform their workouts. When the user performs their workouts, the feedback unit can provide, for example, corrections to posture and points for correcting movement as part of form corrections. This includes specific advice and instructions to help the user perform the workout with the correct form. For example, it can provide real-time instructions on fine details such as knee position, back angle, and arm movement when performing squats. When the user performs their workouts, the feedback unit can provide methods for monitoring achievement and progress as part of training progress. This includes specific indicators for evaluating progress towards user-set goals and training results. For example, it can monitor the number of repetitions and sets, weight used, and calories burned in real time and provide feedback to the user. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can use an AI model that takes the plan generated by the generation unit as input and provides real-time feedback. The AI ​​model uses algorithms to analyze user training data and provide optimal feedback. This allows the feedback system to support users in effectively training and achieving their goals. Furthermore, the feedback system can continuously improve training plans and feedback content based on user feedback. This ensures that the feedback system always provides the most up-to-date and optimal support, helping users achieve their goals.

[0064] The data collection unit can collect information about the user's goals, current health status, and lifestyle. For example, the data collection unit can store the user's entered information about their goals, health status, and lifestyle in a database. The data collection unit can collect specific details about the user's goals, such as weight loss, muscle building, and maintaining health. The data collection unit can collect specific indicators about the user's health status, such as blood pressure, heart rate, and body fat percentage. The data collection unit can collect specific elements about the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. By collecting information about the user's goals, current health status, and lifestyle, the data collection unit can obtain basic data for providing individualized training plans and nutritional guidance. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the information entered by the user into a generating AI and have the generating AI perform the information collection.

[0065] The generation unit can analyze the collected information and generate training plans and nutritional guidance tailored to the user's needs. The generation unit analyzes the collected information using, for example, data analysis methods and algorithms. The generation unit can generate specific details for training plans tailored to the user's needs, such as the type of exercise, frequency, and intensity. The generation unit can generate specific details for nutritional guidance tailored to the user's needs, such as the balance of meals, calorie intake, and nutrient distribution. In this way, the generation unit can provide advice tailored to individual needs by analyzing the collected information and generating training plans and nutritional guidance tailored to the user's needs. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the information collected by the collection unit into a generation AI and have the generation AI execute the generation of training plans and nutritional guidance.

[0066] The feedback unit can provide real-time feedback to users as they perform training. For example, the feedback unit can monitor form corrections and training progress as the user performs training. As a form correction, the feedback unit can provide points for adjusting posture and correcting movements as the user performs training. As a training progress, the feedback unit can provide methods for monitoring achievement and progress as the user performs training. In this way, the feedback unit can maximize the effectiveness of training by providing real-time feedback to users as they perform training. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the plan generated by the generation unit into the generation AI and have the generation AI perform the process of providing real-time feedback.

[0067] The generation unit can adjust the plan to match the user's schedule and fitness level. For example, the generation unit adjusts the plan considering the user's schedule, such as daily appointments and training times. The generation unit also adjusts the plan considering specific indicators such as endurance, strength, and flexibility as part of the user's fitness level. In this way, the generation unit can provide training that is not overly strenuous by adjusting the plan to match the user's schedule and fitness level. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input information about the user's schedule and fitness level into a generation AI and have the generation AI perform the plan adjustments.

[0068] The feedback unit can monitor form corrections and training progress. For example, when a user is performing training, the feedback unit provides points for correcting posture and movement as form corrections. For example, when a user is performing training, the feedback unit provides methods for monitoring achievement and progress as training progress. In this way, by correcting form and monitoring training progress, the feedback unit can ensure that training is performed with correct form and effective training is achieved. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's training data into a generating AI and have the generating AI perform form corrections and monitor training progress.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during times when the user is relaxed. If the user is relaxed, the data collection unit can increase the frequency of information collection to collect more detailed data. If the user is busy, the data collection unit can collect the necessary information in a short amount of time. In this way, by adjusting the timing of information collection based on the user's emotions, the data collection unit can collect information at an appropriate time according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0070] The data collection unit can analyze the user's past health data and select the optimal information collection method. For example, the data collection unit can identify a tendency for health to deteriorate during specific time periods from the user's past health data and focus on collecting information during those times. Based on the user's past health data, the data collection unit can analyze the impact of specific diets or exercises on health and adjust the information collection method accordingly. The data collection unit can analyze the user's past health data and adjust the timing of information collection according to seasonal changes in health. In this way, the data collection unit can select the optimal information collection method and perform effective information collection by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the information collection method.

[0071] The data collection unit can filter information based on the user's current activity level and dietary content. For example, if the user is at a high activity level, the data collection unit will prioritize collecting information related to post-exercise recovery. After the user consumes a particular meal, the data collection unit can analyze the impact of that meal on their health and collect relevant information. If the user is at a low activity level, the data collection unit can collect information related to light exercise or stretching to improve their health. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current activity level and dietary content. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's activity level and dietary content into a generating AI and have the generating AI perform the information filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information related to relaxation and stress reduction. If the user is relaxed, the data collection unit can prioritize collecting information related to maintaining health and improving performance. If the user is tired, the data collection unit can prioritize collecting information related to recovery and rest. In this way, by determining the priority of information to collect based on the user's emotions, the data collection unit can prioritize collecting information appropriate to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0073] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit can collect training and nutrition information suitable for the climate and environment of that region. If the user is traveling, the data collection unit can collect information about fitness facilities and health foods available at the travel destination. If the user is at home, the data collection unit can prioritize the collection of information about training and nutrition guidance that can be done at home. In this way, the data collection unit can provide information that is suitable for the user's environment by considering the user's geographical location when collecting information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0074] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze training and meal content shared by the user on social media and collect relevant information. The data collection unit can analyze posts from fitness influencers followed by the user and collect relevant information. The data collection unit can collect relevant information based on health topics the user has shown interest in on social media. In this way, the data collection unit can collect information based on the user's interests and concerns by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0075] The generation unit can estimate the user's emotions and adjust the presentation of training plans and nutritional guidance based on the estimated emotions. For example, if the user is stressed, the generation unit can provide relaxing training plans and nutritional guidance. If the user is relaxed, the generation unit can provide challenging training plans and nutritional guidance. If the user is tired, the generation unit can provide recovery-focused training plans and nutritional guidance. In this way, the generation unit can provide the best possible advice to the user by adjusting the presentation of training plans and nutritional guidance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of training plans and nutritional guidance.

[0076] The generation unit can adjust the level of detail of the plan based on the user's health goals during generation. For example, if the user's goal is weight loss, the generation unit can provide a detailed plan focused on calorie management and fat burning. If the user's goal is muscle building, the generation unit can provide a detailed plan focused on strength training and nutrition. If the user's goal is stress reduction, the generation unit can provide a detailed plan focused on relaxation exercises and mental health care. By adjusting the level of detail of the plan based on the user's health goals, the generation unit can provide specific advice to help the user achieve their goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health goal information into a generation AI and have the generation AI adjust the level of detail of the plan.

[0077] The generation unit can apply different generation algorithms depending on the user's health condition during generation. For example, if the user is in good health, the generation unit can apply a normal training plan generation algorithm. If the user's health condition is deteriorating, the generation unit can apply a training plan generation algorithm that prioritizes recovery. If the user has a specific health problem, the generation unit can apply a training plan generation algorithm that addresses that problem. In this way, by applying different generation algorithms according to the user's health condition, the generation unit can provide the optimal training plan and nutritional guidance for the user's condition. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about the user's health condition into a generation AI and have the generation AI execute the application of the generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the length of the plan based on the estimated emotions. For example, if the user is stressed, the generation unit can provide a short, effective training plan. If the user is relaxed, the generation unit can provide a longer training plan. If the user is tired, the generation unit can provide a short, recovery-focused training plan. In this way, by adjusting the length of the plan based on the user's emotions, the generation unit can provide an appropriate training plan according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the plan.

[0079] The generation unit can determine the priority of plans based on the user's daily rhythm during generation. For example, if the user is a morning person, the generation unit can provide a plan that prioritizes morning training. If the user is a night owl, the generation unit can provide a plan that prioritizes evening training. The generation unit can provide plans that adjust the timing of training to match the user's daily rhythm. In this way, by determining the priority of plans based on the user's daily rhythm, the generation unit can provide training plans that are tailored to the user's lifestyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about the user's daily rhythm into a generation AI and have the generation AI perform the determination of plan priorities.

[0080] The generation unit can adjust the order of the plan based on the user's relevant health data during generation. For example, the generation unit can provide an optimal training order based on the user's past training data. The generation unit can analyze the user's health data and adjust the training order. The generation unit can adjust the training order according to the user's fitness level. In this way, the generation unit can provide a training plan that is optimal for the user's condition by adjusting the order of the plan based on the user's relevant health data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's relevant health data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0081] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide encouraging words or suggestions for relaxation. If the user is relaxed, the feedback unit can provide specific advice for taking the next step. If the user is tired, the feedback unit can provide feedback encouraging rest. In this way, the feedback unit can provide the most appropriate feedback for the user by adjusting the content of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0082] The feedback unit can provide optimal feedback by referring to the user's training history during the feedback process. For example, the feedback unit can evaluate the user's progress based on their past training history and suggest the next steps. The feedback unit can analyze the effectiveness of a particular training session from the user's training history and provide feedback on areas for improvement. The feedback unit can refer to the user's training history and provide feedback to maintain motivation. In this way, the feedback unit can provide optimal feedback according to the user's progress by referring to their training history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's training history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0083] The feedback unit can adjust the intensity of the feedback according to the user's fitness level. For example, if the user's fitness level is high, the feedback unit can provide challenging feedback. If the user's fitness level is low, the feedback unit can provide gentle feedback. The feedback unit can adjust the content of the feedback according to the user's fitness level. In this way, the feedback unit can provide the optimal feedback for the user by adjusting the intensity of the feedback according to the user's fitness level. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input information about the user's fitness level into a generating AI and have the generating AI adjust the intensity of the feedback.

[0084] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit may prioritize feedback related to stress reduction. If the user is relaxed, the feedback unit may prioritize feedback that encourages the user to take the next step. If the user is tired, the feedback unit may prioritize feedback that encourages rest. In this way, by determining the priority of feedback based on the user's emotions, the feedback unit can prioritize providing appropriate feedback according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform the determination of feedback priorities.

[0085] The feedback unit can provide optimal feedback by taking into account the user's geographical location information. For example, if the user is in a specific region, the feedback unit can provide feedback appropriate to the climate and environment of that region. If the user is traveling, the feedback unit can provide feedback on fitness facilities and health foods available at the travel destination. If the user is at home, the feedback unit can provide feedback on training and nutritional guidance that can be done at home. In this way, the feedback unit can provide feedback appropriate to the user's environment by taking into account the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0086] The feedback unit can analyze the user's social media activity and adjust the content of the feedback when providing it. For example, the feedback unit can analyze the training and meal content shared by the user on social media and provide relevant feedback. The feedback unit can analyze the content of posts by fitness influencers that the user follows and provide relevant feedback. The feedback unit can provide relevant feedback based on the health topics that the user has shown interest in on social media. In this way, the feedback unit can provide feedback based on the user's interests and concerns by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the feedback.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit can more accurately understand a user's current health status by referencing the user's past health data when collecting information about the user's health. For example, the data collection unit can analyze the user's past blood pressure and heart rate data and compare it to their current health status to understand changes in their health. Furthermore, the data collection unit can identify areas for improvement in the user's current lifestyle by referencing the user's past diet and exercise data. In addition, the data collection unit can analyze the user's past sleep patterns and evaluate the quality of their current sleep. This allows the data collection unit to gather more accurate information by utilizing the user's past health data.

[0089] The generation unit can provide an optimal training plan by referencing the user's past training data when generating a user's training plan. For example, the generation unit can analyze the results of the user's past training and identify which training was effective. It can also adjust the current training plan by referencing the frequency and intensity of the user's past training. Furthermore, the generation unit can analyze changes in the user's motivation over time and provide a training plan to maintain that motivation. In this way, the generation unit can leverage the user's past training data to provide a more effective training plan.

[0090] The feedback unit can estimate the user's emotions as they undergo training and adjust the content of the feedback based on those emotions. For example, if the user is feeling stressed, the feedback unit can provide relaxing feedback. If the user is relaxed, the feedback unit can provide specific advice for moving on to the next step. Furthermore, if the user is tired, the feedback unit can provide feedback encouraging rest. In this way, the feedback unit can provide the most optimal feedback for the user by adjusting the content of the feedback based on the user's emotions.

[0091] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering information about the user's health status. For example, if the user is in a specific region, the data collection unit can collect health information that is appropriate for the climate and environment of that region. Furthermore, if the user is traveling, the data collection unit can collect information about fitness facilities and health foods available at their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of information about home-based training and nutritional guidance. In this way, the data collection unit can provide information tailored to the user's environment by considering their geographical location when collecting information.

[0092] The generation unit can estimate the user's emotions when generating a user's training plan and adjust the content of the training plan based on those emotions. For example, if the user is feeling stressed, the generation unit can provide a relaxing training plan. If the user is relaxed, the generation unit can provide a challenging training plan. Furthermore, if the user is tired, the generation unit can provide a training plan that emphasizes recovery. In this way, the generation unit can provide the user with the optimal training plan by adjusting the content of the training plan based on the user's emotions.

[0093] The feedback unit can provide optimal feedback to users by referring to their training history as they perform training. For example, the feedback unit can evaluate the user's progress based on their past training history and suggest the next steps. It can also analyze the effectiveness of specific training sessions from the user's training history and provide feedback on areas for improvement. Furthermore, the feedback unit can refer to the user's training history and provide feedback to maintain motivation. In this way, the feedback unit can provide optimal feedback tailored to the user's progress by referring to their training history.

[0094] The data collection unit can collect relevant information by analyzing users' social media activity when gathering information about their health status. For example, it can analyze training and dietary information shared by users on social media and collect relevant information. It can also analyze posts from fitness influencers that users follow and collect relevant information. Furthermore, it can collect relevant information based on health topics that users have shown interest in on social media. In this way, the data collection unit can collect information based on users' interests and concerns by analyzing users' social media activity.

[0095] The generation unit can estimate the user's emotions when generating a training plan and adjust how the training plan is presented based on those emotions. For example, if the user is feeling stressed, the generation unit can provide a relaxing training plan. If the user is relaxed, the generation unit can provide a challenging training plan. Furthermore, if the user is tired, the generation unit can provide a training plan that emphasizes recovery. In this way, the generation unit can provide the optimal training plan for the user by adjusting how the training plan is presented based on the user's emotions.

[0096] The feedback unit can provide optimal feedback to users when they are training, taking into account their geographical location. For example, if the user is in a specific region, the feedback unit can provide feedback that is appropriate for the climate and environment of that region. Furthermore, if the user is traveling, the feedback unit can provide feedback on fitness facilities and health foods available at their destination. Additionally, if the user is at home, the feedback unit can provide feedback on home-based training and nutritional guidance. In this way, the feedback unit can provide feedback that is appropriate for the user's environment by considering their geographical location.

[0097] The feedback unit can estimate the user's emotions as they undergo training and prioritize feedback based on those emotions. For example, if the user is feeling stressed, the feedback unit can prioritize feedback related to stress reduction. If the user is relaxed, the feedback unit can prioritize feedback that encourages them to move on to the next step. Furthermore, if the user is tired, the feedback unit can prioritize feedback that encourages rest. In this way, the feedback unit can prioritize appropriate feedback tailored to the user's state by determining feedback priorities based on the user's emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects information about the user's goals, health status, and lifestyle. For example, it stores information about the user's goals, health status, and lifestyle in a database. The data collection unit can collect specific details about the user's goals, such as weight loss, muscle building, and maintaining health. It can also collect specific indicators about the user's health status, such as blood pressure, heart rate, and body fat percentage. Furthermore, it can collect specific elements about the user's lifestyle, such as eating habits, exercise habits, and sleep patterns. Step 2: The generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. For example, it analyzes the collected information using data analysis methods and algorithms. The generation unit can generate specific details such as the type, frequency, and intensity of exercise as a training plan tailored to the user's needs. It can also generate specific details such as the balance of meals, calorie intake, and nutrient distribution as nutritional guidance tailored to the user's needs. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can perform generation using an AI model that takes the information collected by the collection unit as input and outputs individual training plans and nutritional guidance. Step 3: The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, it monitors form corrections and training progress when the user is performing training. As form corrections, the feedback unit can provide points for adjusting posture and correcting movements when the user is performing training. As training progress, it can provide methods for monitoring achievement and progress. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can use an AI model that takes the plan generated by the generation unit as input and provides feedback in real time.

[0100] 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.

[0101] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the collection unit, generation unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14, which collects information on goals, health status, and lifestyle entered by the user and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information to generate individual training plans and nutritional guidance. The feedback unit is implemented by the control unit 46A of the smart device 14, which provides real-time feedback based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0107] 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.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0109] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] 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.

[0111] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the collection unit, generation unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214, which collects information on goals, health status, and lifestyle entered by the user and stores it in the database 24. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to generate individual training plans and nutritional guidance. The feedback unit is implemented by the control unit 46A of the smart glasses 214, which provides real-time feedback based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0123] 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.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0125] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] 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.

[0127] 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.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the collection unit, generation unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, which collects information on goals, health status, and lifestyle entered by the user and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information to generate individual training plans and nutritional guidance. The feedback unit is implemented by the control unit 46A of the headset terminal 314, which provides real-time feedback based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0139] 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.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0141] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] 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.

[0143] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] 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.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, generation unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414, which collects information on goals, health status, and lifestyle entered by the user and stores it in the database 24. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information to generate individual training plans and nutritional guidance. The feedback unit is implemented by the control unit 46A of the robot 414, which provides real-time feedback based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] Figure 9 shows the 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.

[0155] 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.

[0156] 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.

[0157] 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, and motorcycles, 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 based, for example, 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.

[0158] 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."

[0159] 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.

[0160] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] 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 other things 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.

[0170] 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.

[0171] (Note 1) A data collection unit that collects information about the user's goals, health status, and lifestyle, A generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance, The system includes a feedback unit that provides real-time feedback based on the plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information about the user's goals, current health status, and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The collected information is analyzed to generate training plans and nutritional guidance tailored to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Provides real-time feedback to users as they conduct training. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The plan is adjusted to suit the user's schedule and fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Correct your form and monitor your training progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current activity level and dietary habits. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the way training plans and nutritional guidance are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail in the plan is adjusted based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the plan's priority is determined based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the order of plans is adjusted based on the user's relevant health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, refer to the user's training history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is During feedback, the intensity of the feedback is adjusted according to the user's fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and adjust the content of the feedback accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects information about the user's goals, health status, and lifestyle, A generation unit analyzes the information collected by the collection unit and generates individual training plans and nutritional guidance. The system includes a feedback unit that provides real-time feedback based on the plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information about the user's goals, current health status, and lifestyle. The system according to feature 1.

3. The generating unit is The collected information is analyzed to generate training plans and nutritional guidance tailored to the user's needs. The system according to feature 1.

4. The aforementioned feedback unit is Provides real-time feedback to users as they conduct training. The system according to feature 1.

5. The generating unit is The plan is adjusted to suit the user's schedule and fitness level. The system according to feature 1.

6. The aforementioned feedback unit is Correct your form and monitor your training progress. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past health data and select the optimal method for collecting information. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current activity level and dietary habits. The system according to feature 1.

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