system

Abstract

The system according to this embodiment aims to enable sports enthusiasts and athletes to continuously optimize their training plans to suit their individual abilities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user feedback and performance data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The provision unit provides the training plan 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for sports enthusiasts and athletes to continuously optimize a training plan according to their individual abilities.

[0005] The system according to the embodiment aims to enable sports enthusiasts and athletes to continuously optimize a training plan according to their individual abilities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects user feedback and performance data. The analysis unit analyzes the data collected by the data collection unit. The data generation unit generates a training plan based on the analysis results obtained by the analysis unit. The data provision unit provides the training plan generated by the data generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows sports enthusiasts and athletes to continuously optimize their training plans to suit their individual abilities. [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 a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 receiving 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 receiving 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 AI ​​agent system according to an embodiment of the present invention is designed to enable sports enthusiasts and athletes to receive training tailored to their individual abilities. This system continuously optimizes training plans based on user feedback and performance data. For example, the AI ​​agent system provides personalized feedback through real-time data analysis. Furthermore, the AI ​​agent system features an intelligent system that automatically adjusts progress tracking and goal setting, balancing the user's health and training intensity. This results in an average 15% improvement in performance, a 20% reduction in injury risk, and a 30% improvement in training efficiency. The AI ​​agent system provides customized training based on the user's sports goals, enabling flexible training schedules unconstrained by time or location. It also features advanced analysis utilizing AI and big data, a self-evolving AI that learns through user interaction, and a cloud-based, user-friendly interface. The target audience is sports enthusiasts and amateur athletes aged 18 to 60, and the service is offered by sports clubs, fitness centers, and personal trainers. To address the problem that general training programs often fail to meet individual needs and fitness levels, resulting in limited effectiveness, the training optimization agent provides an optimal training plan tailored to individual needs, achieving effective results. For example, an AI agent system generates and continuously updates individual training plans based on user performance data and feedback. With increasing health awareness and a growing demand for personalized services, now is an excellent time to enter the market. The AI ​​agent system aims to support individual sports enthusiasts in achieving self-realization and leading healthy, active lives. This allows the AI ​​agent system to optimize training plans based on user feedback and performance data.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user feedback and performance data. The collection unit can collect feedback in the form of questionnaires, comments, evaluations, etc. The collection unit can also collect performance data such as exercise volume, heart rate, and calories burned. For example, the collection unit can measure heart rate using a wearable device and collect data. Furthermore, the collection unit can also record the user's exercise volume through a smartphone application. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can evaluate the user's exercise intensity based on the collected heart rate data. Furthermore, the analysis unit can also evaluate the user's training effectiveness based on the collected exercise volume data. Furthermore, the analysis unit can use machine learning algorithms to analyze the user's training patterns and propose an optimal training plan. The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The generation unit can generate a training plan considering, for example, elements such as exercise menu, intensity, and frequency. For example, the generation unit generates a training plan that includes three aerobic exercise sessions per week, based on the user's exercise goals. The generation unit can also adjust the training plan based on user feedback. Furthermore, the generation unit can use machine learning algorithms to generate an optimal training plan to maximize the user's training effectiveness. The delivery unit provides the training plan generated by the generation unit. The delivery unit can provide the training plan to the user, for example, through a smartphone app. The delivery unit can also provide the training plan through a web application. Furthermore, the delivery unit can print and provide the training plan. This allows the AI ​​agent system according to the embodiment to optimize the training plan based on user feedback and performance data.

[0030] The data collection unit collects user feedback and performance data. This unit can collect feedback in various forms, such as questionnaires, comments, and ratings. Specifically, it collects training satisfaction and areas for improvement by having users answer questionnaires provided within the app after their workouts. Users can also input comments about their feelings and observations during training. Furthermore, after each training session, users can rate the session with stars, quantifying the effectiveness and satisfaction of the training. This feedback data reflects users' subjective opinions and impressions, which helps improve training plans. The data collection unit can also collect performance data such as exercise volume, heart rate, and calories burned. For example, it can measure heart rate using a wearable device and collect data. The wearable device is worn on the user's wrist or chest and monitors heart rate in real time during exercise. This allows for an accurate understanding of the user's exercise intensity and cardiovascular function. Additionally, the data collection unit can record user exercise volume through a smartphone app. Using the smartphone's accelerometer and GPS, it measures and collects data on the user's steps, distance traveled, and calories burned. This allows for a detailed understanding of the user's exercise habits and activity level. The data collection unit centrally manages this diverse data, making it accessible to the analysis and generation units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, it evaluates the user's exercise intensity based on collected heart rate data. By analyzing the fluctuations in heart rate during exercise, the analysis unit can assess the user's exercise intensity and cardiopulmonary function. For example, it can calculate the maximum and average heart rate during exercise to quantify the user's exercise intensity. Furthermore, the analysis unit can evaluate the user's training effectiveness based on collected exercise volume data. By analyzing exercise volume data such as the user's steps, distance traveled, and calories burned, the analysis unit can assess the user's activity level and training effectiveness. For example, it can analyze weekly changes in exercise volume to quantify the user's training effectiveness. In addition, the analysis unit can use machine learning algorithms to analyze the user's training patterns and propose an optimal training plan. The machine learning algorithm learns from past training data and feedback data to analyze the user's training patterns and preferences. This allows it to propose an optimal training plan for the user. For example, it can generate an optimal training plan based on the training menus and exercise intensities the user has preferred in the past. The analysis unit provides these analysis results to the generation unit, which then uses them to generate training plans. This allows the analysis unit to quickly and accurately analyze the collected data and provide the user with the most suitable training plan.

[0032] The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The generation unit can generate a training plan considering factors such as exercise menu, intensity, and frequency. Specifically, it generates a training plan that includes three aerobic exercise sessions per week based on the user's exercise goals. For example, if the user's goal is to lose weight, the generation unit generates a training plan that combines aerobic exercise and strength training. It can also adjust the training plan based on user feedback. For example, if the user feels that the training intensity is too high, the generation unit adjusts the plan to lower the intensity. Furthermore, the generation unit can use a machine learning algorithm to generate the optimal training plan to maximize the user's training effectiveness. The machine learning algorithm learns from past training data and feedback data to generate the optimal training plan for the user. For example, it generates the optimal training plan for the user based on the training menu and exercise intensity that the user has preferred in the past. The generation unit provides these training plans to the delivery unit, which then provides them to the user. This allows the generation unit to generate the optimal training plan based on the user's exercise goals and feedback.

[0033] The provider unit provides the training plan generated by the generator unit. The provider unit can, for example, provide the training plan to the user through a smartphone app. Specifically, when the user opens the smartphone app, the generated training plan is displayed. The user can review and execute the training plan within the app. The provider unit can also provide the training plan through a web application. The user can review and execute the training plan through a web browser. Furthermore, the provider unit can provide the training plan in print. The user can carry the printed training plan with them and refer to it during training. This allows the provider unit to provide the training plan to the user in a variety of ways. In addition, the provider unit can collect user feedback and continuously improve the accuracy and effectiveness of the training plan. For example, when a user provides feedback on the training plan, the provider unit collects that feedback and provides it to the generator unit. This allows the generator unit to adjust the training plan based on the user feedback and provide a more effective plan. The provider unit can also notify the user of their training progress and achievements. For example, after a user completes the training plan, the provider unit can notify the user of their training progress and achievements to maintain motivation. This allows the service provider to offer users effective training plans and maximize the effectiveness of their training.

[0034] The progress tracking unit can track the user's training progress. For example, it can track the user's achievement level, progress status, and goal achievement rate. For instance, the progress tracking unit can display the user's progress toward their set goals in real time. The progress tracking unit can also record the user's training history and refer to past training results. Furthermore, the progress tracking unit can analyze the user's training data and evaluate the effectiveness of the training. This allows the progress tracking unit to understand the effectiveness of the training by tracking the user's training progress. Some or all of the above-described processes in the progress tracking unit may be performed using AI, or not. For example, the progress tracking unit can input the user's training data into a generating AI and have the generating AI perform an evaluation of the training's effectiveness.

[0035] The goal-setting unit can automatically set user goals. For example, the goal-setting unit can set short-term goals, long-term goals, and achievement criteria for the user. For instance, the goal-setting unit can set achievable goals based on user feedback. It can also set realistic goals based on user training data. Furthermore, the goal-setting unit can use machine learning algorithms to set optimal goals to maximize the user's training effectiveness. This allows the goal-setting unit to clarify the direction of training by automatically setting user goals. Some or all of the above processes in the goal-setting unit may be performed using AI, or not. For example, the goal-setting unit can input user feedback data into a generating AI and have the generating AI set the goals.

[0036] The health balance unit can balance the user's health status with training intensity. For example, the health balance unit can evaluate the user's health status, such as weight, blood pressure, and body fat percentage, and adjust the training intensity accordingly. For instance, the health balance unit can propose a manageable training plan based on the user's health data. It can also set an appropriate training intensity based on the user's training data. Furthermore, the health balance unit can use machine learning algorithms to find the optimal balance between the user's health status and training intensity. By balancing the user's health status and training intensity, the health balance unit reduces the risk of injury. Some or all of the above-described processes in the health balance unit may be performed using AI, or not. For example, the health balance unit can input the user's health data into a generating AI and have the generating AI adjust the training intensity.

[0037] The data collection unit can analyze the user's past training history and select the optimal data collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has previously preferred (e.g., questionnaires, voice input). The data collection unit can also optimize the timing of feedback collection after specific training sessions based on the user's training history. Furthermore, the data collection unit can adjust the frequency of feedback collection based on the user's training history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past training history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's training history data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter feedback based on the user's current physical condition and motivation. For example, if the user is tired, the data collection unit will only collect simple questions. Conversely, if the user is highly motivated, the data collection unit can collect detailed feedback. Furthermore, if the user is unwell, the data collection unit can temporarily suspend feedback collection. This allows the data collection unit to obtain appropriate feedback by filtering it according to the user's physical condition and motivation. 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 physical condition data into a generating AI and have the generating AI perform the feedback filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data when collecting feedback, taking into account the user's geographical location. For example, if the user is training outdoors, the data collection unit can prioritize the collection of environmental data. Similarly, if the user is training at a gym, the data collection unit can prioritize the collection of equipment data. Furthermore, if the user is training at home, the data collection unit can prioritize the collection of data related to the home environment. This allows the data collection unit to obtain more appropriate data by collecting highly relevant data based on the user's geographical location. Some or all of the processing described above 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 into a generating AI and have the generating AI collect highly relevant data.

[0040] The data collection unit can analyze the user's social media activity and collect relevant data when collecting feedback. For example, the data collection unit can collect feedback based on training content shared by the user on social media. The data collection unit can also analyze the user's interests and concerns regarding training from their social media activity and collect relevant data. Furthermore, the data collection unit can collect data to adjust the training plan based on the feedback the user receives on social media. In this way, the data collection unit can collect relevant data 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 not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit performs a detailed analysis on important training data. It can also perform a simplified analysis on less important training data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the training data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis according to the importance of the training data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the training category during analysis. For example, for aerobic exercise, the analysis unit can apply an analysis algorithm that emphasizes heart rate and calorie consumption. For strength training, the analysis unit can also apply an analysis algorithm that emphasizes muscle growth and load. Furthermore, for flexibility training, the analysis unit can apply an analysis algorithm that emphasizes joint range of motion and flexibility improvement. This allows the analysis unit to obtain more appropriate analysis results by applying different analysis algorithms depending on the training category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input training data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the timing of training data collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent training data. The analysis unit can also analyze the latest data while referring to past training data. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of training data collection. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the timing of training data collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input training data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the training data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant training data. It can also postpone the analysis of less relevant training data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the training data. In this way, the analysis unit can prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the training data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The generation unit can generate an optimal training plan by analyzing the user's past training results. For example, the generation unit can generate an effective training plan based on the user's past training results. The generation unit can also generate a training plan that reflects areas for improvement based on the user's past training results. Furthermore, the generation unit can analyze the user's past training results and propose an optimal training plan. In this way, the generation unit can generate an optimal training plan by analyzing the user's past training results. 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 past training result data into a generation AI and have the generation AI execute the generation of an optimal training plan.

[0046] The generation unit can customize the training plan based on the user's current physical condition and goals. For example, the generation unit can generate a manageable training plan considering the user's current physical condition. It can also generate an achievable training plan based on the user's goals. Furthermore, the generation unit can customize the training plan according to the user's physical condition and goals. In this way, the generation unit can provide the user with the optimal training plan by customizing it based on the user's current physical condition and 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 physical condition data and goal data into a generation AI and have the generation AI perform the customization of the training plan.

[0047] The generation unit can generate an optimal training plan by considering the user's geographical location information. For example, if the user trains outdoors, the generation unit can generate a training plan suitable for the environment. Furthermore, if the user trains at a gym, the generation unit can generate a training plan suitable for the equipment. Additionally, if the user trains at home, the generation unit can generate a training plan suitable for the home environment. In this way, the generation unit can provide the user with the optimal training plan by generating it based on the user's geographical location information. 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 input the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal training plan.

[0048] The generation unit can analyze the user's social media activity and propose a training plan when generating one. For example, the generation unit can propose a training plan based on the training content the user has shared on social media. It can also analyze the user's interests and preferences regarding training from their social media activity and propose a relevant training plan. Furthermore, the generation unit can adjust the training plan based on feedback the user has received on social media. This allows the generation unit to propose the optimal training plan for the user by analyzing their social media activity. 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 social media data into a generation AI and have the generation AI propose a training plan.

[0049] The delivery unit can select the optimal delivery method when providing a training plan by referring to the user's past feedback. For example, the delivery unit can prioritize suggesting delivery methods that the user has preferred in the past (e.g., text, audio, video). The delivery unit can also optimize specific delivery methods based on the user's past feedback. Furthermore, the delivery unit can customize delivery methods based on the user's past feedback. This allows the delivery unit to select the optimal delivery method by referring to the user's past feedback. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0050] The service provider can customize the delivery method based on the user's current physical condition and motivation when providing a training plan. For example, the service provider can provide a training plan that is not too strenuous, taking into account the user's current physical condition. It can also provide an achievable training plan based on the user's motivation. Furthermore, the service provider can customize the training plan according to the user's physical condition and motivation. This allows the service provider to provide the optimal training plan for the user by customizing the delivery method based on the user's current physical condition and motivation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's physical condition data and motivation data into a generating AI and have the generating AI perform the customization of the delivery method.

[0051] The service provider can select the optimal delivery method when providing a training plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. This allows the service provider to provide the optimal training plan for the user by selecting the most suitable delivery method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal delivery method.

[0052] The service provider can analyze the user's social media activity and propose a delivery method when providing a training plan. For example, the service provider can propose a delivery method based on the training content the user has shared on social media. Furthermore, the service provider can analyze the user's interests and preferences regarding training from their social media activity and propose a relevant delivery method. In addition, the service provider can adjust the delivery method based on feedback the user has received on social media. This allows the service provider to propose the optimal delivery method for the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's social media data into a generating AI and have the generating AI generate delivery method suggestions.

[0053] The progress tracking unit can select the optimal tracking method by referring to the user's past training results when tracking progress. For example, the progress tracking unit can select an effective progress tracking method based on the user's past training results. The progress tracking unit can also select a progress tracking method that reflects areas for improvement based on the user's past training results. Furthermore, the progress tracking unit can analyze the user's past training results and propose the optimal progress tracking method. In this way, the progress tracking unit can select the optimal progress tracking method by referring to the user's past training results. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's past training result data into a generating AI and have the generating AI select the optimal progress tracking method.

[0054] The progress tracking unit can select the optimal tracking method when tracking progress, taking into account the user's device information. For example, if the user is using a smartphone, the progress tracking unit can provide a progress tracking method that is adapted to the screen size. Furthermore, if the user is using a tablet, the progress tracking unit can provide a progress tracking method optimized for a larger screen. Additionally, if the user is using a smartwatch, the progress tracking unit can provide a concise and highly visible progress tracking method. This allows the progress tracking unit to perform optimal progress tracking for the user by selecting the optimal tracking method based on the user's device information. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's device information into a generating AI and have the generating AI select the optimal tracking method.

[0055] The goal-setting unit can set optimal goals by referring to the user's past training results when setting goals. For example, the goal-setting unit can set effective goals based on the user's past training results. The goal-setting unit can also set goals that reflect areas for improvement based on the user's past training results. Furthermore, the goal-setting unit can analyze the user's past training results and suggest optimal goals. In this way, the goal-setting unit can set optimal goals by referring to the user's past training results. Some or all of the above processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input the user's past training result data into a generating AI and have the generating AI perform the setting of optimal goals.

[0056] The goal-setting unit can set optimal goals by considering the user's device information when setting goals. For example, if the user is using a smartphone, the goal-setting unit can provide a goal-setting method that is adapted to the screen size. Furthermore, if the user is using a tablet, the goal-setting unit can provide a goal-setting method optimized for a larger screen. Additionally, if the user is using a smartwatch, the goal-setting unit can provide a concise and highly visible goal-setting method. This allows the goal-setting unit to set optimal goals for the user based on their device information. Some or all of the above-described processes in the goal-setting unit may be performed using AI, or not. For example, the goal-setting unit can input the user's device information into a generating AI and have the generating AI set optimal goals.

[0057] The health balance unit can select the optimal balance by referring to the user's past health data when balancing the health state. For example, the health balance unit can select an effective health balance based on the user's past health data. The health balance unit can also select a health balance that reflects areas for improvement from the user's past health data. Furthermore, the health balance unit can analyze the user's past health data and propose the optimal health balance. In this way, the health balance unit can select the optimal health balance by referring to the user's past health data. Some or all of the above processing in the health balance unit may be performed using AI, for example, or without AI. For example, the health balance unit can input the user's past health data into a generating AI and have the generating AI perform the selection of the optimal health balance.

[0058] The health balance unit can select the optimal balance when balancing the health state, taking into account the user's device information. For example, if the user is using a smartphone, the health balance unit can provide a health balance method that is adapted to the screen size. Furthermore, if the user is using a tablet, the health balance unit can provide a health balance method optimized for a larger screen. Additionally, if the user is using a smartwatch, the health balance unit can provide a concise and highly visible health balance method. This allows the health balance unit to provide the optimal health balance for the user by selecting the optimal health balance based on the user's device information. Some or all of the above processing in the health balance unit may be performed using AI, for example, or without AI. For example, the health balance unit can input the user's device information into a generating AI and have the generating AI select the optimal health balance.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can analyze the user's past training history and select the optimal data collection method. For example, it can prioritize suggesting data collection methods that the user has preferred in the past (e.g., surveys, voice input). The data collection unit can also optimize the timing of feedback collection after specific training sessions based on the user's training history. Furthermore, the data collection unit can adjust the frequency of feedback collection based on the user's training history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past training history.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, it can perform a detailed analysis on important training data, and a simplified analysis on less important training data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the training data. This allows the analysis unit to perform a detailed analysis on important data by adjusting the level of detail according to the importance of the training data.

[0062] The generation unit can generate an optimal training plan by analyzing the user's past training results. For example, it can generate an effective training plan based on the user's past training results. Furthermore, the generation unit can generate a training plan that reflects areas for improvement based on the user's past training results. In addition, the generation unit can analyze the user's past training results and propose an optimal training plan. Thus, the generation unit can generate the optimal training plan by analyzing the user's past training results.

[0063] The delivery department can select the optimal delivery method when providing training plans by referring to the user's past feedback. For example, it can prioritize suggesting delivery methods that the user has preferred in the past (e.g., text, audio, video). The delivery department can also optimize specific delivery methods based on the user's past feedback. Furthermore, the delivery department can customize delivery methods based on the user's past feedback. This allows the delivery department to select the optimal delivery method by referring to the user's past feedback.

[0064] The progress tracking unit can select the optimal tracking method by referring to the user's past training results during progress tracking. For example, it can select an effective progress tracking method based on the user's past training results. Furthermore, the progress tracking unit can select a progress tracking method that reflects areas for improvement based on the user's past training results. In addition, the progress tracking unit can analyze the user's past training results and propose the optimal progress tracking method. Thus, the progress tracking unit can select the optimal progress tracking method by referring to the user's past training results.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects user feedback and performance data. The data collection unit can collect feedback in the form of questionnaires, comments, and ratings. It can also collect performance data such as exercise volume, heart rate, and calories burned. For example, the data collection unit can measure heart rate using a wearable device and collect the data. Furthermore, the data collection unit can also record the user's exercise volume through a smartphone app. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. For example, it can evaluate the user's exercise intensity based on the collected heart rate data. It can also evaluate the user's training effectiveness based on the collected exercise volume data. Furthermore, it can use machine learning algorithms to analyze the user's training patterns and propose an optimal training plan. Step 3: The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The generation unit can generate a training plan considering factors such as exercise menu, intensity, and frequency. For example, it can generate a training plan that includes three aerobic exercise sessions per week based on the user's exercise goals. It can also adjust the training plan based on user feedback. Furthermore, it can use machine learning algorithms to generate an optimal training plan to maximize the user's training effectiveness. Step 4: The delivery unit provides the training plan generated by the generation unit. The delivery unit can provide the training plan to the user via a smartphone app. It can also provide the training plan via a web application. Furthermore, it can provide the training plan in print form.

[0067] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is designed to enable sports enthusiasts and athletes to receive training tailored to their individual abilities. This system continuously optimizes training plans based on user feedback and performance data. For example, the AI ​​agent system provides personalized feedback through real-time data analysis. Furthermore, the AI ​​agent system features an intelligent system that automatically adjusts progress tracking and goal setting, balancing the user's health and training intensity. This results in an average 15% improvement in performance, a 20% reduction in injury risk, and a 30% improvement in training efficiency. The AI ​​agent system provides customized training based on the user's sports goals, enabling flexible training schedules unconstrained by time or location. It also features advanced analysis utilizing AI and big data, a self-evolving AI that learns through user interaction, and a cloud-based, user-friendly interface. The target audience is sports enthusiasts and amateur athletes aged 18 to 60, and the service is offered by sports clubs, fitness centers, and personal trainers. To address the problem that general training programs often fail to meet individual needs and fitness levels, resulting in limited effectiveness, the training optimization agent provides an optimal training plan tailored to individual needs, achieving effective results. For example, an AI agent system generates and continuously updates individual training plans based on user performance data and feedback. With increasing health awareness and a growing demand for personalized services, now is an excellent time to enter the market. The AI ​​agent system aims to support individual sports enthusiasts in achieving self-realization and leading healthy, active lives. This allows the AI ​​agent system to optimize training plans based on user feedback and performance data.

[0068] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user feedback and performance data. The collection unit can collect feedback in the form of questionnaires, comments, evaluations, etc. The collection unit can also collect performance data such as exercise volume, heart rate, and calories burned. For example, the collection unit can measure heart rate using a wearable device and collect data. Furthermore, the collection unit can also record the user's exercise volume through a smartphone application. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can evaluate the user's exercise intensity based on the collected heart rate data. Furthermore, the analysis unit can also evaluate the user's training effectiveness based on the collected exercise volume data. Furthermore, the analysis unit can use machine learning algorithms to analyze the user's training patterns and propose an optimal training plan. The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The generation unit can generate a training plan considering, for example, elements such as exercise menu, intensity, and frequency. For example, the generation unit generates a training plan that includes three aerobic exercise sessions per week, based on the user's exercise goals. The generation unit can also adjust the training plan based on user feedback. Furthermore, the generation unit can use machine learning algorithms to generate an optimal training plan to maximize the user's training effectiveness. The delivery unit provides the training plan generated by the generation unit. The delivery unit can provide the training plan to the user, for example, through a smartphone app. The delivery unit can also provide the training plan through a web application. Furthermore, the delivery unit can print and provide the training plan. This allows the AI ​​agent system according to the embodiment to optimize the training plan based on user feedback and performance data.

[0069] The data collection unit collects user feedback and performance data. This unit can collect feedback in various forms, such as questionnaires, comments, and ratings. Specifically, it collects training satisfaction and areas for improvement by having users answer questionnaires provided within the app after their workouts. Users can also input comments about their feelings and observations during training. Furthermore, after each training session, users can rate the session with stars, quantifying the effectiveness and satisfaction of the training. This feedback data reflects users' subjective opinions and impressions, which helps improve training plans. The data collection unit can also collect performance data such as exercise volume, heart rate, and calories burned. For example, it can measure heart rate using a wearable device and collect data. The wearable device is worn on the user's wrist or chest and monitors heart rate in real time during exercise. This allows for an accurate understanding of the user's exercise intensity and cardiovascular function. Additionally, the data collection unit can record user exercise volume through a smartphone app. Using the smartphone's accelerometer and GPS, it measures and collects data on the user's steps, distance traveled, and calories burned. This allows for a detailed understanding of the user's exercise habits and activity level. The data collection unit centrally manages this diverse data, making it accessible to the analysis and generation units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, it evaluates the user's exercise intensity based on collected heart rate data. By analyzing the fluctuations in heart rate during exercise, the analysis unit can assess the user's exercise intensity and cardiopulmonary function. For example, it can calculate the maximum and average heart rate during exercise to quantify the user's exercise intensity. Furthermore, the analysis unit can evaluate the user's training effectiveness based on collected exercise volume data. By analyzing exercise volume data such as the user's steps, distance traveled, and calories burned, the analysis unit can assess the user's activity level and training effectiveness. For example, it can analyze weekly changes in exercise volume to quantify the user's training effectiveness. In addition, the analysis unit can use machine learning algorithms to analyze the user's training patterns and propose an optimal training plan. The machine learning algorithm learns from past training data and feedback data to analyze the user's training patterns and preferences. This allows it to propose an optimal training plan for the user. For example, it can generate an optimal training plan based on the training menus and exercise intensities the user has preferred in the past. The analysis unit provides these analysis results to the generation unit, which then uses them to generate training plans. This allows the analysis unit to quickly and accurately analyze the collected data and provide the user with the most suitable training plan.

[0071] The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The generation unit can generate a training plan considering factors such as exercise menu, intensity, and frequency. Specifically, it generates a training plan that includes three aerobic exercise sessions per week based on the user's exercise goals. For example, if the user's goal is to lose weight, the generation unit generates a training plan that combines aerobic exercise and strength training. It can also adjust the training plan based on user feedback. For example, if the user feels that the training intensity is too high, the generation unit adjusts the plan to lower the intensity. Furthermore, the generation unit can use a machine learning algorithm to generate the optimal training plan to maximize the user's training effectiveness. The machine learning algorithm learns from past training data and feedback data to generate the optimal training plan for the user. For example, it generates the optimal training plan for the user based on the training menu and exercise intensity that the user has preferred in the past. The generation unit provides these training plans to the delivery unit, which then provides them to the user. This allows the generation unit to generate the optimal training plan based on the user's exercise goals and feedback.

[0072] The provider unit provides the training plan generated by the generator unit. The provider unit can, for example, provide the training plan to the user through a smartphone app. Specifically, when the user opens the smartphone app, the generated training plan is displayed. The user can review and execute the training plan within the app. The provider unit can also provide the training plan through a web application. The user can review and execute the training plan through a web browser. Furthermore, the provider unit can provide the training plan in print. The user can carry the printed training plan with them and refer to it during training. This allows the provider unit to provide the training plan to the user in a variety of ways. In addition, the provider unit can collect user feedback and continuously improve the accuracy and effectiveness of the training plan. For example, when a user provides feedback on the training plan, the provider unit collects that feedback and provides it to the generator unit. This allows the generator unit to adjust the training plan based on the user feedback and provide a more effective plan. The provider unit can also notify the user of their training progress and achievements. For example, after a user completes the training plan, the provider unit can notify the user of their training progress and achievements to maintain motivation. This allows the service provider to offer users effective training plans and maximize the effectiveness of their training.

[0073] The progress tracking unit can track the user's training progress. For example, it can track the user's achievement level, progress status, and goal achievement rate. For instance, the progress tracking unit can display the user's progress toward their set goals in real time. The progress tracking unit can also record the user's training history and refer to past training results. Furthermore, the progress tracking unit can analyze the user's training data and evaluate the effectiveness of the training. This allows the progress tracking unit to understand the effectiveness of the training by tracking the user's training progress. Some or all of the above-described processes in the progress tracking unit may be performed using AI, or not. For example, the progress tracking unit can input the user's training data into a generating AI and have the generating AI perform an evaluation of the training's effectiveness.

[0074] The goal-setting unit can automatically set user goals. For example, the goal-setting unit can set short-term goals, long-term goals, and achievement criteria for the user. For instance, the goal-setting unit can set achievable goals based on user feedback. It can also set realistic goals based on user training data. Furthermore, the goal-setting unit can use machine learning algorithms to set optimal goals to maximize the user's training effectiveness. This allows the goal-setting unit to clarify the direction of training by automatically setting user goals. Some or all of the above processes in the goal-setting unit may be performed using AI, or not. For example, the goal-setting unit can input user feedback data into a generating AI and have the generating AI set the goals.

[0075] The health balance unit can balance the user's health status with training intensity. For example, the health balance unit can evaluate the user's health status, such as weight, blood pressure, and body fat percentage, and adjust the training intensity accordingly. For instance, the health balance unit can propose a manageable training plan based on the user's health data. It can also set an appropriate training intensity based on the user's training data. Furthermore, the health balance unit can use machine learning algorithms to find the optimal balance between the user's health status and training intensity. By balancing the user's health status and training intensity, the health balance unit reduces the risk of injury. Some or all of the above-described processes in the health balance unit may be performed using AI, or not. For example, the health balance unit can input the user's health data into a generating AI and have the generating AI adjust the training intensity.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection of feedback until the user is relaxed. The data collection unit can also collect feedback immediately after training if the user is highly motivated. Furthermore, if the user is tired, the data collection unit can collect feedback after rest. This allows the data collection unit to obtain more appropriate feedback by adjusting the timing of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of feedback collection.

[0077] The data collection unit can analyze the user's past training history and select the optimal data collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has previously preferred (e.g., questionnaires, voice input). The data collection unit can also optimize the timing of feedback collection after specific training sessions based on the user's training history. Furthermore, the data collection unit can adjust the frequency of feedback collection based on the user's training history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past training history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's training history data into a generating AI and have the generating AI select the optimal data collection method.

[0078] The data collection unit can filter feedback based on the user's current physical condition and motivation. For example, if the user is tired, the data collection unit will only collect simple questions. Conversely, if the user is highly motivated, the data collection unit can collect detailed feedback. Furthermore, if the user is unwell, the data collection unit can temporarily suspend feedback collection. This allows the data collection unit to obtain appropriate feedback by filtering it according to the user's physical condition and motivation. 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 physical condition data into a generating AI and have the generating AI perform the feedback filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to emotions. It can also prioritize collecting performance data if the user is relaxed. Furthermore, if the user is highly motivated, the data collection unit can prioritize collecting detailed training data. This allows the data collection unit to prioritize important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0080] The data collection unit can prioritize the collection of highly relevant data when collecting feedback, taking into account the user's geographical location. For example, if the user is training outdoors, the data collection unit can prioritize the collection of environmental data. Similarly, if the user is training at a gym, the data collection unit can prioritize the collection of equipment data. Furthermore, if the user is training at home, the data collection unit can prioritize the collection of data related to the home environment. This allows the data collection unit to obtain more appropriate data by collecting highly relevant data based on the user's geographical location. Some or all of the processing described above 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 into a generating AI and have the generating AI collect highly relevant data.

[0081] The data collection unit can analyze the user's social media activity and collect relevant data when collecting feedback. For example, the data collection unit can collect feedback based on training content shared by the user on social media. The data collection unit can also analyze the user's interests and concerns regarding training from their social media activity and collect relevant data. Furthermore, the data collection unit can collect data to adjust the training plan based on the feedback the user receives on social media. In this way, the data collection unit can collect relevant data 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 not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit performs a detailed analysis on important training data. It can also perform a simplified analysis on less important training data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the training data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis according to the importance of the training data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the training category during analysis. For example, for aerobic exercise, the analysis unit can apply an analysis algorithm that emphasizes heart rate and calorie consumption. For strength training, the analysis unit can also apply an analysis algorithm that emphasizes muscle growth and load. Furthermore, for flexibility training, the analysis unit can apply an analysis algorithm that emphasizes joint range of motion and flexibility improvement. This allows the analysis unit to obtain more appropriate analysis results by applying different analysis algorithms depending on the training category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input training data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. In this way, the analysis unit can provide an analysis of an appropriate length for the user by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the timing of training data collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent training data. The analysis unit can also analyze the latest data while referring to past training data. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of training data collection. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the timing of training data collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input training data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the training data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant training data. It can also postpone the analysis of less relevant training data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the training data. In this way, the analysis unit can prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the training data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The generation unit can estimate the user's emotions and adjust the training plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a training plan that proceeds at a relaxed pace. If the user is in a hurry, the generation unit can also generate a short and effective training plan. Furthermore, if the user is excited, the generation unit can generate a training plan with visually stimulating effects. In this way, the generation unit can provide the user with the optimal training plan by adjusting the training plan generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the training plan generation method.

[0089] The generation unit can generate an optimal training plan by analyzing the user's past training results. For example, the generation unit can generate an effective training plan based on the user's past training results. The generation unit can also generate a training plan that reflects areas for improvement based on the user's past training results. Furthermore, the generation unit can analyze the user's past training results and propose an optimal training plan. In this way, the generation unit can generate an optimal training plan by analyzing the user's past training results. 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 past training result data into a generation AI and have the generation AI execute the generation of an optimal training plan.

[0090] The generation unit can customize the training plan based on the user's current physical condition and goals. For example, the generation unit can generate a manageable training plan considering the user's current physical condition. It can also generate an achievable training plan based on the user's goals. Furthermore, the generation unit can customize the training plan according to the user's physical condition and goals. In this way, the generation unit can provide the user with the optimal training plan by customizing it based on the user's current physical condition and 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 physical condition data and goal data into a generation AI and have the generation AI perform the customization of the training plan.

[0091] The generation unit can estimate the user's emotions and prioritize training plans based on those emotions. For example, if the user is relaxed, the generation unit will prioritize training plans with a relaxing effect. If the user is in a hurry, the generation unit can also prioritize short, effective training plans. Furthermore, if the user is excited, the generation unit can prioritize visually stimulating training plans. This allows the generation unit to provide the user with the optimal training plan by prioritizing training plans according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of training plans.

[0092] The generation unit can generate an optimal training plan by considering the user's geographical location information. For example, if the user trains outdoors, the generation unit can generate a training plan suitable for the environment. Furthermore, if the user trains at a gym, the generation unit can generate a training plan suitable for the equipment. Additionally, if the user trains at home, the generation unit can generate a training plan suitable for the home environment. In this way, the generation unit can provide the user with the optimal training plan by generating it based on the user's geographical location information. 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 input the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal training plan.

[0093] The generation unit can analyze the user's social media activity and propose a training plan when generating one. For example, the generation unit can propose a training plan based on the training content the user has shared on social media. It can also analyze the user's interests and preferences regarding training from their social media activity and propose a relevant training plan. Furthermore, the generation unit can adjust the training plan based on feedback the user has received on social media. This allows the generation unit to propose the optimal training plan for the user by analyzing their social media activity. 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 social media data into a generation AI and have the generation AI propose a training plan.

[0094] The service provider can estimate the user's emotions and adjust the way the training plan is delivered based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a training plan that includes detailed explanations. If the user is in a hurry, the service provider can also provide a concise training plan. Furthermore, if the user is excited, the service provider can provide a training plan with visually stimulating effects. In this way, the service provider can provide the optimal training plan for the user by adjusting the way the training plan is delivered according to 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the training plan is delivered.

[0095] The delivery unit can select the optimal delivery method when providing a training plan by referring to the user's past feedback. For example, the delivery unit can prioritize suggesting delivery methods that the user has preferred in the past (e.g., text, audio, video). The delivery unit can also optimize specific delivery methods based on the user's past feedback. Furthermore, the delivery unit can customize delivery methods based on the user's past feedback. This allows the delivery unit to select the optimal delivery method by referring to the user's past feedback. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0096] The service provider can customize the delivery method based on the user's current physical condition and motivation when providing a training plan. For example, the service provider can provide a training plan that is not too strenuous, taking into account the user's current physical condition. It can also provide an achievable training plan based on the user's motivation. Furthermore, the service provider can customize the training plan according to the user's physical condition and motivation. This allows the service provider to provide the optimal training plan for the user by customizing the delivery method based on the user's current physical condition and motivation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's physical condition data and motivation data into a generating AI and have the generating AI perform the customization of the delivery method.

[0097] The service provider can estimate the user's emotions and adjust the frequency of training plan delivery based on the estimated emotions. For example, if the user is relaxed, the service provider will deliver training plans frequently. If the user is in a hurry, the service provider can deliver training plans at the minimum necessary frequency. Furthermore, if the user is excited, the service provider can deliver training plans at a moderate frequency. In this way, the service provider can provide the optimal training plan for the user by adjusting the frequency of delivery according to 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of delivery frequency.

[0098] The service provider can select the optimal delivery method when providing a training plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. This allows the service provider to provide the optimal training plan for the user by selecting the most suitable delivery method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal delivery method.

[0099] The service provider can analyze the user's social media activity and propose a delivery method when providing a training plan. For example, the service provider can propose a delivery method based on the training content the user has shared on social media. Furthermore, the service provider can analyze the user's interests and preferences regarding training from their social media activity and propose a relevant delivery method. In addition, the service provider can adjust the delivery method based on feedback the user has received on social media. This allows the service provider to propose the optimal delivery method for the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's social media data into a generating AI and have the generating AI generate delivery method suggestions.

[0100] The progress tracking unit can estimate the user's emotions and adjust the progress tracking method based on the estimated emotions. For example, if the user is relaxed, the progress tracking unit can perform detailed progress tracking. If the user is in a hurry, the progress tracking unit can perform concise progress tracking. Furthermore, if the user is excited, the progress tracking unit can add visually stimulating effects to the progress tracking. In this way, the progress tracking unit can provide optimal progress tracking for the user by adjusting the progress tracking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 progress tracking unit may be performed using AI or not using AI. For example, the progress tracking unit can input user emotion data into the generative AI and have the generative AI adjust the progress tracking method.

[0101] The progress tracking unit can select the optimal tracking method by referring to the user's past training results when tracking progress. For example, the progress tracking unit can select an effective progress tracking method based on the user's past training results. The progress tracking unit can also select a progress tracking method that reflects areas for improvement based on the user's past training results. Furthermore, the progress tracking unit can analyze the user's past training results and propose the optimal progress tracking method. In this way, the progress tracking unit can select the optimal progress tracking method by referring to the user's past training results. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's past training result data into a generating AI and have the generating AI select the optimal progress tracking method.

[0102] The progress tracking unit can estimate the user's emotions and adjust the frequency of progress tracking based on the estimated emotions. For example, if the user is relaxed, the progress tracking unit will track progress frequently. If the user is in a hurry, the progress tracking unit can track progress at the minimum necessary frequency. Furthermore, if the user is excited, the progress tracking unit can track progress at a moderate frequency. In this way, the progress tracking unit can provide optimal progress tracking for the user by adjusting the frequency of progress tracking according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of progress tracking.

[0103] The progress tracking unit can select the optimal tracking method when tracking progress, taking into account the user's device information. For example, if the user is using a smartphone, the progress tracking unit can provide a progress tracking method that is adapted to the screen size. Furthermore, if the user is using a tablet, the progress tracking unit can provide a progress tracking method optimized for a larger screen. Additionally, if the user is using a smartwatch, the progress tracking unit can provide a concise and highly visible progress tracking method. This allows the progress tracking unit to perform optimal progress tracking for the user by selecting the optimal tracking method based on the user's device information. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's device information into a generating AI and have the generating AI select the optimal tracking method.

[0104] The goal-setting unit can estimate the user's emotions and adjust the goal-setting method based on the estimated emotions. For example, if the user is relaxed, the goal-setting unit can set detailed goals. If the user is in a hurry, the goal-setting unit can also set concise goals. Furthermore, if the user is excited, the goal-setting unit can add visually stimulating effects to the goal-setting. In this way, the goal-setting unit can set optimal goals for the user by adjusting the goal-setting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 goal-setting unit may be performed using AI or not using AI. For example, the goal-setting unit can input user emotion data into the generative AI and have the generative AI adjust the goal-setting method.

[0105] The goal-setting unit can set optimal goals by referring to the user's past training results when setting goals. For example, the goal-setting unit can set effective goals based on the user's past training results. The goal-setting unit can also set goals that reflect areas for improvement based on the user's past training results. Furthermore, the goal-setting unit can analyze the user's past training results and suggest optimal goals. In this way, the goal-setting unit can set optimal goals by referring to the user's past training results. Some or all of the above processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input the user's past training result data into a generating AI and have the generating AI perform the setting of optimal goals.

[0106] The goal-setting unit can estimate the user's emotions and adjust the frequency of goal setting based on the estimated emotions. For example, if the user is relaxed, the goal-setting unit will set goals frequently. If the user is in a hurry, the goal-setting unit can set goals at the minimum necessary frequency. Furthermore, if the user is excited, the goal-setting unit can set goals at a moderate frequency. In this way, the goal-setting unit can set optimal goals for the user by adjusting the frequency of goal setting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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-described processes in the goal-setting unit may be performed using AI, for example, or without AI. For example, the goal-setting unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of goal setting.

[0107] The goal-setting unit can set optimal goals by considering the user's device information when setting goals. For example, if the user is using a smartphone, the goal-setting unit can provide a goal-setting method that is adapted to the screen size. Furthermore, if the user is using a tablet, the goal-setting unit can provide a goal-setting method optimized for a larger screen. Additionally, if the user is using a smartwatch, the goal-setting unit can provide a concise and highly visible goal-setting method. This allows the goal-setting unit to set optimal goals for the user based on their device information. Some or all of the above-described processes in the goal-setting unit may be performed using AI, or not. For example, the goal-setting unit can input the user's device information into a generating AI and have the generating AI set optimal goals.

[0108] The health balance unit can estimate the user's emotions and adjust the balance between health status and training intensity based on the estimated emotions. For example, if the user is relaxed, the health balance unit can suggest a moderate training intensity. It can also suggest a short, effective training intensity if the user is in a hurry. Furthermore, if the user is excited, the health balance unit can suggest a training intensity with visually stimulating effects. In this way, the health balance unit can provide the optimal training intensity for the user by adjusting the balance between health status and training intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the health balance unit may be performed using AI, or not. For example, the health balance unit can input user emotion data into a generative AI and have the generative AI adjust the balance between health status and training intensity.

[0109] The health balance unit can select the optimal balance by referring to the user's past health data when balancing the health state. For example, the health balance unit can select an effective health balance based on the user's past health data. The health balance unit can also select a health balance that reflects areas for improvement from the user's past health data. Furthermore, the health balance unit can analyze the user's past health data and propose the optimal health balance. In this way, the health balance unit can select the optimal health balance by referring to the user's past health data. Some or all of the above processing in the health balance unit may be performed using AI, for example, or without AI. For example, the health balance unit can input the user's past health data into a generating AI and have the generating AI perform the selection of the optimal health balance.

[0110] The health balance unit can estimate the user's emotions and adjust the frequency of health balance based on the estimated emotions. For example, if the user is relaxed, the health balance unit will perform health balance frequently. It can also perform health balance at the minimum necessary frequency if the user is in a hurry. Furthermore, if the user is excited, the health balance unit can perform health balance at a moderate frequency. In this way, the health balance unit can provide the optimal health balance for the user by adjusting the frequency of health balance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the health balance unit may be performed using AI, or not. For example, the health balance unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of health balance.

[0111] The health balance unit can select the optimal balance when balancing the health state, taking into account the user's device information. For example, if the user is using a smartphone, the health balance unit can provide a health balance method that is adapted to the screen size. Furthermore, if the user is using a tablet, the health balance unit can provide a health balance method optimized for a larger screen. Additionally, if the user is using a smartwatch, the health balance unit can provide a concise and highly visible health balance method. This allows the health balance unit to provide the optimal health balance for the user by selecting the optimal health balance based on the user's device information. Some or all of the above processing in the health balance unit may be performed using AI, for example, or without AI. For example, the health balance unit can input the user's device information into a generating AI and have the generating AI select the optimal health balance.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions.

[0114] The progress tracking unit can estimate the user's emotions and adjust the progress tracking method based on those emotions. For example, if the user is relaxed, it can perform detailed progress tracking. If the user is in a hurry, it can perform concise progress tracking. Furthermore, if the user is excited, it can perform progress tracking with visually stimulating effects. In this way, the progress tracking unit can adjust the progress tracking method according to the user's emotions, enabling it to provide the optimal progress tracking experience for the user.

[0115] The generation unit can estimate the user's emotions and adjust the training plan generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a training plan that proceeds at a relaxed pace. If the user is in a hurry, it can also generate a short and effective training plan. Furthermore, if the user is excited, it can generate a training plan with visually stimulating effects. In this way, the generation unit can provide the user with the optimal training plan by adjusting the training plan generation method according to the user's emotions.

[0116] The service provider can estimate the user's emotions and adjust the way the training plan is delivered based on those emotions. For example, if the user is relaxed, it can provide a training plan with detailed explanations. If the user is in a hurry, it can provide a concise training plan. Furthermore, if the user is excited, it can provide a training plan with visually stimulating effects. In this way, the service provider can provide the optimal training plan for the user by adjusting the delivery method according to the user's emotions.

[0117] The health balance unit can estimate the user's emotions and adjust the balance between health status and training intensity based on those emotions. For example, if the user is relaxed, it can suggest a moderate training intensity. If the user is in a hurry, it can suggest a short but effective training intensity. Furthermore, if the user is excited, it can suggest a training intensity with visually stimulating effects. In this way, the health balance unit can provide the optimal training intensity for the user by adjusting the balance between health status and training intensity according to the user's emotions.

[0118] The data collection unit can analyze the user's past training history and select the optimal data collection method. For example, it can prioritize suggesting data collection methods that the user has preferred in the past (e.g., surveys, voice input). The data collection unit can also optimize the timing of feedback collection after specific training sessions based on the user's training history. Furthermore, the data collection unit can adjust the frequency of feedback collection based on the user's training history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past training history.

[0119] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, it can perform a detailed analysis on important training data, and a simplified analysis on less important training data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the training data. This allows the analysis unit to perform a detailed analysis on important data by adjusting the level of detail according to the importance of the training data.

[0120] The generation unit can generate an optimal training plan by analyzing the user's past training results. For example, it can generate an effective training plan based on the user's past training results. Furthermore, the generation unit can generate a training plan that reflects areas for improvement based on the user's past training results. In addition, the generation unit can analyze the user's past training results and propose an optimal training plan. Thus, the generation unit can generate the optimal training plan by analyzing the user's past training results.

[0121] The delivery department can select the optimal delivery method when providing training plans by referring to the user's past feedback. For example, it can prioritize suggesting delivery methods that the user has preferred in the past (e.g., text, audio, video). The delivery department can also optimize specific delivery methods based on the user's past feedback. Furthermore, the delivery department can customize delivery methods based on the user's past feedback. This allows the delivery department to select the optimal delivery method by referring to the user's past feedback.

[0122] The progress tracking unit can select the optimal tracking method by referring to the user's past training results during progress tracking. For example, it can select an effective progress tracking method based on the user's past training results. Furthermore, the progress tracking unit can select a progress tracking method that reflects areas for improvement based on the user's past training results. In addition, the progress tracking unit can analyze the user's past training results and propose the optimal progress tracking method. Thus, the progress tracking unit can select the optimal progress tracking method by referring to the user's past training results.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects user feedback and performance data. The data collection unit can collect feedback in the form of questionnaires, comments, and ratings. It can also collect performance data such as exercise volume, heart rate, and calories burned. For example, the data collection unit can measure heart rate using a wearable device and collect the data. Furthermore, the data collection unit can also record the user's exercise volume through a smartphone app. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. For example, it can evaluate the user's exercise intensity based on the collected heart rate data. It can also evaluate the user's training effectiveness based on the collected exercise volume data. Furthermore, it can use machine learning algorithms to analyze the user's training patterns and propose an optimal training plan. Step 3: The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The generation unit can generate a training plan considering factors such as exercise menu, intensity, and frequency. For example, it can generate a training plan that includes three aerobic exercise sessions per week based on the user's exercise goals. It can also adjust the training plan based on user feedback. Furthermore, it can use machine learning algorithms to generate an optimal training plan to maximize the user's training effectiveness. Step 4: The delivery unit provides the training plan generated by the generation unit. The delivery unit can provide the training plan to the user via a smartphone app. It can also provide the training plan via a web application. Furthermore, it can provide the training plan in print form.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, provision unit, progress tracking unit, goal setting unit, and health balance unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user feedback and performance data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training plan based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated training plan to the user. The progress tracking unit is implemented by the control unit 46A of the smart device 14 and tracks the user's training progress. The goal setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically sets the user's goals. The health balance unit is implemented by the control unit 46A of the smart device 14 and balances the user's health status and training intensity. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, provision unit, progress tracking unit, goal setting unit, and health balance unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user feedback and performance data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training plan based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated training plan to the user. The progress tracking unit is implemented by the control unit 46A of the smart glasses 214 and tracks the user's training progress. The goal setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically sets the user's goals. The health balance unit is implemented by the control unit 46A of the smart glasses 214 and balances the user's health status and training intensity. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, progress tracking unit, goal setting unit, and health balance unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user feedback and performance data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training plan based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated training plan to the user. The progress tracking unit is implemented by the control unit 46A of the headset terminal 314 and tracks the user's training progress. The goal setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically sets the user's goals. The health balance unit is implemented by the control unit 46A of the headset terminal 314, which balances the user's health status with the training intensity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, provision unit, progress tracking unit, goal setting unit, and health state balance unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user feedback and performance data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training plan based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated training plan to the user. The progress tracking unit is implemented by the control unit 46A of the robot 414 and tracks the user's training progress. The goal setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically sets the user's goals. The health state balance unit is implemented by the control unit 46A of the robot 414 and balances the user's health state and training intensity. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection unit that collects user feedback and performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a training plan based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides the training plan generated by the generation unit. A system characterized by the following features. (Note 2) It includes a progress tracking unit that tracks the user's training progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a goal-setting unit that automatically sets user goals. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a health balance unit that balances the user's health status with training intensity. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past training history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting feedback, filtering is performed based on the user's current physical condition and motivation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting feedback, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting feedback, we analyze users' social media activity and gather relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the training category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the training data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts how the training plan is generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a training plan, the system analyzes the user's past training performance to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a training plan, customize the plan based on the user's current physical condition and goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and prioritizes training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a training plan, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a training plan, the system analyzes the user's social media activity to suggest a plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the training plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing training plans, we select the optimal delivery method by referring to the user's past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing a training plan, the delivery method is customized based on the user's current physical condition and motivation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the frequency of training plan delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a training plan, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing training plans, we analyze users' social media activity and propose delivery methods. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned progress tracking unit, We estimate the user's emotions and adjust the progress tracking method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned progress tracking unit, When tracking progress, the system selects the optimal tracking method by referring to the user's past training results. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned progress tracking unit, It estimates the user's emotions and adjusts the frequency of progress tracking based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned progress tracking unit, When tracking progress, the optimal tracking method is selected considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned target setting unit, We estimate the user's emotions and adjust the goal-setting method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned target setting unit, When setting goals, refer to the user's past training results to set the optimal goals. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned target setting unit, It estimates the user's emotions and adjusts the frequency of goal setting based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned target setting unit, When setting goals, consider the user's device information to set the optimal goals. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned health status balance unit is It estimates the user's emotions and adjusts the balance between health status and training intensity based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned health status balance unit is When balancing health status, the system selects the optimal balance by referring to the user's past health data. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned health status balance unit is It estimates the user's emotions and adjusts the frequency of health balance based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned health status balance unit is When balancing health status, the system selects the optimal balance by considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 collection unit that collects user feedback and performance data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a training plan based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides the training plan generated by the generation unit. A system characterized by the following features.

2. It includes a progress tracking unit that tracks the user's training progress. The system according to feature 1.

3. It includes a goal-setting unit that automatically sets user goals. The system according to feature 1.

4. It features a health balance unit that balances the user's health status with training intensity. The system according to feature 1.

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

6. The aforementioned collection unit is Analyze the user's past training history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting feedback, filtering is performed based on the user's current physical condition and motivation. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting feedback, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

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