System

Abstract

An object of a system according to an embodiment is to analyze training data of a user and provide an optimal training program.SOLUTION: A system according to an embodiment includes a data collector, an AI analyzer, a training program provider, and a feedback provider. The data collector is configured to collect training data from a device of a user. The AI analysis unit analyzes the data collected by the data collection unit. The training program providing unit provides an optimal training program to the user based on the result analyzed by the AI analyzing unit. The feedback providing unit may provide feedback or motivation to the user based on the training program provided by the training program providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had a problem in that they have not been able to effectively utilize a user's training data to provide a personalized training program.

[0005] The system according to the embodiment aims to analyze the training data of a user and provide an optimal training program. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an AI analysis unit, a training program provision unit, and a feedback provision unit. The data collection unit collects training data from a user's device. The AI ​​analysis unit analyzes the data collected by the data collection unit. The training program provision unit provides the user with an optimal training program based on the results of the analysis by the AI ​​analysis unit. The feedback provision unit provides the user with feedback and motivation based on the training program provided by the training program provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the training data of the user and provide an optimal training program. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A training program providing system according to an embodiment of the present invention collects training data from a user's device, analyzes it using a generation AI, provides an optimal training program, and provides feedback and motivation, thereby maximizing the effectiveness of the user's training.

[0029] A training program provision system according to an embodiment includes a data collection unit, an AI analysis unit, a training program provision unit, and a feedback provision unit. The data collection unit collects training data from a user's device. For example, an activity tracker or a heart rate monitor may be used to record information such as steps, distance, calories burned, heart rate, and sleep patterns. The data collection unit may also collect the user's training data using a smartphone or tablet. For example, when a user runs, the activity tracker records the distance and pace run, and the heart rate monitor records the heart rate. The AI ​​analysis unit analyzes the data collected by the data collection unit. For example, the generation AI generates an optimal training program based on the user's training history and current fitness level. The generation AI analyzes the user's training data using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the user's heart rate data and suggests an appropriate training intensity. The training program provision unit provides the user with an optimal training program based on the results of the analysis by the AI ​​analysis unit. For example, if a user is aiming to run a marathon, the AI ​​will suggest a long-distance running program and set appropriate paces and rest days. The feedback providing unit provides feedback and motivation to the user based on the training program provided by the training program providing unit. For example, the AI ​​may provide regular feedback to the user and evaluate their training progress. It may also provide messages and advice to motivate the user. For example, sending a message such as "Great pace! Keep it up" may motivate the user. This allows the training program providing system according to the embodiment to maximize the effectiveness of the user's training. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students or teachers wish to receive feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending them directly to students and parents.

[0030] The data collection unit can additionally collect the user's food and water intake and integrate it with the training data to comprehensively evaluate the training effect. For example, the data collection unit allows the user to input their daily food intake into a smartphone app and link that data with the activity tracker. For example, the data collection unit records the calorie intake and nutrient balance and integrates it with the training data. The data collection unit also records the amount of water the user drinks and integrates it with the training data. For example, the data collection unit records the amount and type of water the user drinks and integrates it with the training data. This allows for a more accurate evaluation of the training effect by taking the user's food and water intake into account.

[0031] The data collection unit can measure the user's stress level and integrate it with training data to consider the impact of stress on training. For example, the data collection unit adds a function to measure stress levels to the wearable device and analyzes heart rate variability and electrodermal activity. For example, the data collection unit adjusts training intensity during periods of high stress. The data collection unit also measures stress levels based on the user's self-report. For example, the data collection unit records the times and causes of stress felt by the user and integrates this with training data. The data collection unit also evaluates stress levels based on measurements of stress hormones. For example, the data collection unit analyzes the user's saliva or blood to measure stress hormone levels. This allows for a more accurate evaluation of training effectiveness by taking the user's stress level into consideration.

[0032] The data collection unit can collect additional environmental data and integrate it with the training data to optimize training conditions. For example, the data collection unit may link weather data to the wearable device to optimize the user's training conditions. For example, the data collection unit may adjust training intensity when the temperature or humidity is high. The data collection unit may also collect air quality data to optimize training conditions. For example, the data collection unit may measure the concentration of pollutants in the air and select a training location. The data collection unit may also collect temperature and humidity data to optimize training conditions. For example, the data collection unit may determine whether indoor or outdoor training is more suitable. This allows the training conditions to be optimized by taking environmental data into consideration.

[0033] The data collection unit can compare the data with other users' data to evaluate the relative fitness level. For example, the data collection unit builds a system that anonymizes the user's training data and compares it with the data of other users. For example, the data collection unit compares the user's training data with that of users of the same age or fitness level. The data collection unit also uploads the user's training data to the cloud and integrates it with the data of other users. For example, the data collection unit collects and compares data from users who use fitness apps. The data collection unit also develops an algorithm to evaluate the user's relative fitness level based on the user's training data. For example, the data collection unit compares the user's training data with the average value of the same age group or ranks performance. This makes it possible to evaluate the relative fitness level by comparing it with the data of other users.

[0034] The AI ​​analysis unit can learn the user's past training data and predict long-term training effects. For example, the AI ​​analysis unit uses a generative AI to analyze the user's past training data and develop an algorithm to predict long-term training effects. For example, it predicts future fitness levels based on past data. The AI ​​analysis unit also learns the user's training history and predicts long-term training effects. For example, it analyzes the user's training patterns and suggests effective training plans. The AI ​​analysis unit also tracks changes in the user's fitness level and evaluates long-term training effects. For example, it predicts improvements in the user's physical strength and health. This makes it possible to predict long-term training effects by learning from past training data.

[0035] The AI ​​analysis unit can adjust the training program taking into account the user's health condition. For example, the AI ​​analysis unit constructs a system in which the generation AI analyzes the user's health data (e.g., blood pressure and blood sugar levels) and adjusts the training program. For example, it suggests training intensity according to the health condition. The AI ​​analysis unit also develops an algorithm that adjusts the training program taking into account the user's health condition. For example, it suggests appropriate exercise types and intensities based on the user's health data. The AI ​​analysis unit also monitors the user's health condition and adjusts the training program. For example, it tracks fluctuations in the user's blood pressure and blood sugar levels and optimizes the training program. This allows the training program to be adjusted more appropriately by taking the user's health condition into account.

[0036] The AI ​​analysis unit can compare different training methods and suggest the most suitable method. For example, the AI ​​analysis unit constructs a system in which a generating AI analyzes different training methods (e.g., HIIT or yoga) and suggests the most suitable method to the user. For example, it selects a method based on the user's fitness level and goals. The AI ​​analysis unit also develops an algorithm that compares different training methods and suggests the most suitable method. For example, it selects an effective training method based on the user's training data. The AI ​​analysis unit also learns the user's training history and suggests the most suitable training method. For example, it suggests a new method based on the training methods that the user has used successfully in the past. This makes it possible to compare different training methods and suggest the most suitable method to the user.

[0037] The AI ​​analysis unit can anonymize a user's data and integrate it with the data of other users to analyze training effects. For example, the AI ​​analysis unit builds a system in which a generating AI anonymizes a user's training data and integrates it with the data of other users to analyze training effects. For example, it compares the user's data with users of the same age or fitness level. The AI ​​analysis unit also develops algorithms to anonymize the user's data and integrate it with the data of other users. For example, it deletes personal information and encrypts the data. The AI ​​analysis unit also integrates the data with other users' data to analyze training effects. For example, it compares the user's data with the average value of the same age group and ranks their performance. This allows for a more accurate analysis of training effects by anonymizing the user's data and integrating it with the data of other users.

[0038] The training program providing unit can adjust the training program taking into account the user's lifestyle. For example, the training program providing unit constructs a system in which a generation AI analyzes the user's work schedule and adjusts the training program. For example, it suggests a short training session on busy days. The training program providing unit also adjusts the training program taking into account the user's home situation. For example, it adjusts the time and frequency of training according to home circumstances. The training program providing unit also provides a training program that reflects the user's hobbies and interests. For example, it suggests a program that incorporates the user's favorite sports. This allows the training program to be adjusted more appropriately by taking into account the user's lifestyle.

[0039] The training program providing unit can provide training programs that reflect the user's preferences and interests. For example, the training program providing unit constructs a system in which a generating AI analyzes the user's preferences and interests and provides training programs based on the analysis. For example, the training program providing unit proposes programs that incorporate the user's favorite sports. The training program providing unit also develops an algorithm that provides training programs that reflect the user's interests. For example, the training program providing unit proposes programs that incorporate fitness activities that the user is interested in. The training program providing unit also provides training programs that reflect the user's preferences and interests. For example, the training program providing unit proposes training programs that can be done while the user listens to their favorite music. This allows the training programs to be more personalized by reflecting the user's preferences and interests.

[0040] The training program providing unit can simulate different training programs and select the most effective program. For example, the training program providing unit constructs a system in which a generation AI simulates different training programs and suggests the most effective program to the user. For example, it compares multiple programs and selects the most effective one. The training program providing unit also develops an algorithm that simulates different training programs and selects the most effective program. For example, it selects an effective training program based on the user's training data. The training program providing unit also learns the user's training history and suggests the most effective training program. For example, it suggests a new program based on the training programs that the user has been successful with in the past. This makes it possible to select the most effective program by simulating different training programs.

[0041] The training program providing unit can share the user's training program with other users and improve it based on their feedback. For example, the training program providing unit builds a system in which a generation AI shares the user's training program with other users and improves it based on their feedback. For example, it collects comments and ratings on the training program. The training program providing unit also develops an algorithm that shares the user's training program with other users and improves it based on their feedback. For example, it uploads the user's training program to the cloud and collects feedback from other users. The training program providing unit also improves the training program based on their feedback. For example, it optimizes the program based on comments and ratings on the user's training program. In this way, a more effective program can be provided by sharing the training program with other users and improving it based on their feedback.

[0042] The feedback providing unit visualizes the user's training results, allowing the user to intuitively understand their progress. For example, the feedback providing unit constructs a system in which a generation AI visualizes the user's training results, allowing the user to intuitively understand their progress. For example, the training results are displayed using graphs and charts. The feedback providing unit also develops an algorithm to visualize the user's training results. For example, the results are displayed in a visually easy-to-understand format based on the user's training data. The feedback providing unit also evaluates the user's progress based on the user's training results. For example, the feedback providing unit visualizes the user's level of goal achievement and changes in training frequency and intensity. In this way, the user's training results are visualized, allowing the user to intuitively understand their progress.

[0043] The feedback providing unit can provide a function for competing with other users based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI provides a function for competing with other users based on the user's training data. For example, it provides a function for competing in running distance or pace. The feedback providing unit also develops an algorithm for competing with other users based on the user's training data. For example, it provides a ranking system or a battle mode based on the user's training data. The feedback providing unit also provides a competition function based on the user's training data. For example, it provides a system in which users compete with each other for points. By providing a function for competing with other users, it is possible to increase the user's motivation.

[0044] The feedback providing unit can share successful training cases based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI shares successful training cases based on the user's training data. For example, the feedback providing unit introduces successful training programs to other users. The feedback providing unit also develops an algorithm for sharing successful cases based on the user's training data. For example, the feedback providing unit identifies successful training programs based on the user's training data. The feedback providing unit also shares successful cases based on the user's training data. For example, the feedback providing unit introduces goals that the user has achieved or improved health conditions to other users. This makes it possible to motivate other users by sharing successful training cases.

[0045] The feedback providing unit can suggest optimal rest days based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI analyzes the user's training data and suggests optimal rest days. For example, rest days are set according to the frequency and intensity of training. The feedback providing unit also develops an algorithm that suggests optimal rest days based on the user's training data. For example, it suggests effective rest days based on the user's training data. The feedback providing unit also optimizes rest days based on the user's training data. For example, it analyzes the user's training data and adjusts rest days. This allows the user's training effect to be maximized by suggesting optimal rest days.

[0046] The feedback providing unit can adjust the training intensity in stages based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI analyzes the user's training data and adjusts the training intensity in stages. For example, the intensity is increased or decreased depending on the training results. The feedback providing unit also develops an algorithm for adjusting the training intensity in stages based on the user's training data. For example, the feedback providing unit suggests an effective training intensity based on the user's training data. The feedback providing unit also adjusts the training intensity based on the user's training data. For example, the feedback providing unit analyzes the user's training data and optimizes the training intensity. In this way, the training effect of the user can be maximized by adjusting the training intensity in stages.

[0047] The feedback providing unit can simulate different training schedules based on the user's training data and select the optimal schedule. For example, the feedback providing unit constructs a system in which a generation AI simulates different training schedules based on the user's training data and proposes the optimal schedule. For example, it compares multiple schedules and selects the optimal one. The feedback providing unit also develops an algorithm that simulates different training schedules based on the user's training data and selects the optimal schedule. For example, it selects an effective training schedule based on the user's training data. The feedback providing unit also optimizes the training schedule based on the user's training data. For example, it analyzes the user's training data and adjusts the training schedule. In this way, it is possible to select the optimal schedule by simulating different training schedules.

[0048] The feedback providing unit can share the user's training schedule with other users and improve it based on their feedback. For example, the feedback providing unit constructs a system in which a generation AI shares the user's training schedule with other users and improves it based on their feedback. For example, the feedback providing unit collects comments and ratings on the training schedule. The feedback providing unit also develops an algorithm that shares the user's training schedule with other users and improves it based on their feedback. For example, the feedback providing unit uploads the user's training schedule to the cloud and collects feedback from other users. The feedback providing unit also improves the training schedule based on their feedback. For example, the schedule is optimized based on comments and ratings on the user's training schedule. In this way, a more effective schedule can be provided by sharing the training schedule with other users and improving it based on their feedback.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The training program providing system can further include a hobby analysis unit that takes into account the user's hobbies and interests. The hobby analysis unit, for example, analyzes the user's favorite sports and activities and provides a training program based on them. For example, if the user likes soccer, the hobby analysis unit can suggest soccer-related training. The hobby analysis unit can also analyze the user's musical preferences and suggest music suitable for training. For example, if the user likes rock music, rock music can be played during training. This makes it possible to provide a training program that reflects the user's hobbies and interests, thereby making training more enjoyable.

[0051] The training program providing system may further include a sleep analysis unit that collects the user's sleep data and reflects it in the training program. The sleep analysis unit, for example, analyzes the user's sleep patterns and sleep quality and adjusts the training program. For example, if the user is not getting enough sleep, it may suggest reducing the training intensity. The sleep analysis unit may also suggest optimal training times based on the user's sleep data. For example, if the user is a nocturnal person, it may suggest nighttime training. This allows the training program to be adjusted more effectively by taking the user's sleep data into consideration.

[0052] The training program providing system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, periodically measures the user's blood pressure and blood sugar levels and adjusts the training program. For example, if the user's blood pressure is high, it may suggest low-intensity training. The health monitoring unit may also develop an algorithm that optimizes the training program based on the user's health condition. For example, it may analyze the user's health data and suggest appropriate exercise types and intensities. This allows the training program to be more appropriately adjusted by taking the user's health condition into consideration.

[0053] The training program providing system may further include a competition providing unit that provides a function for competing with other users based on the user's training data. The competition providing unit may, for example, build a system for competing with other users based on the user's training data. For example, it may provide a function for competing in running distance or pace. The competition providing unit may also develop an algorithm that provides a ranking system or a battle mode based on the user's training data. For example, it may provide a system in which users compete with each other for points. This may increase the user's motivation by providing a function for competing with other users.

[0054] The training program providing system may further include a rest day suggestion unit that suggests optimal rest days based on the user's training data. The rest day suggestion unit may, for example, analyze the user's training data and suggest optimal rest days. For example, the rest days may be set based on the frequency and intensity of training. The rest day suggestion unit may also develop an algorithm that suggests effective rest days based on the user's training data. For example, the algorithm may analyze the user's training data and adjust the rest days. This may maximize the effect of the user's training by suggesting optimal rest days.

[0055] The training program providing system may further include an intensity adjustment unit that gradually adjusts the training intensity based on the user's training data. The intensity adjustment unit, for example, analyzes the user's training data and gradually adjusts the training intensity. For example, it increases or decreases the intensity depending on the training results. The intensity adjustment unit may also develop an algorithm that suggests an effective training intensity based on the user's training data. For example, it may analyze the user's training data and optimize the training intensity. This allows the user's training effect to be maximized by gradually adjusting the training intensity.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection unit collects training data from the user's device. For example, an activity tracker or heart rate monitor can be used to record information such as steps, distance, calories burned, heart rate, and sleep patterns. Alternatively, the data collection unit can also collect training data from a smartphone or tablet. Step 2: The AI ​​analysis unit analyzes the data collected by the data collection unit. For example, the generation AI considers the user's training history and current fitness level to generate an optimal training program. The generation AI analyzes the user's training data using text generation AI (e.g., LLM) or multimodal generation AI. For example, it analyzes the user's heart rate data and suggests appropriate training intensity. Step 3: The training program provider provides the user with an optimal training program based on the results of the analysis by the AI ​​analysis unit. For example, if the user is aiming to run a marathon, the AI ​​will suggest a long-distance running program and set an appropriate pace and rest days. Step 4: The feedback provider provides feedback and motivation to the user based on the training program provided by the training program provider. For example, the AI ​​provides regular feedback to the user to evaluate the progress of the training. It also provides messages and advice to increase motivation. For example, it motivates the user by sending a message such as, "Great pace! Keep it up!"

[0058] (Example 2) A training program providing system according to an embodiment of the present invention collects training data from a user's device, analyzes it using a generation AI, provides an optimal training program, and provides feedback and motivation, thereby maximizing the effectiveness of the user's training.

[0059] A training program provision system according to an embodiment includes a data collection unit, an AI analysis unit, a training program provision unit, and a feedback provision unit. The data collection unit collects training data from a user's device. For example, an activity tracker or a heart rate monitor may be used to record information such as steps, distance, calories burned, heart rate, and sleep patterns. The data collection unit may also collect the user's training data using a smartphone or tablet. For example, when a user runs, the activity tracker records the distance and pace run, and the heart rate monitor records the heart rate. The AI ​​analysis unit analyzes the data collected by the data collection unit. For example, the generation AI generates an optimal training program based on the user's training history and current fitness level. The generation AI analyzes the user's training data using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the user's heart rate data and suggests an appropriate training intensity. The training program provision unit provides the user with an optimal training program based on the results of the analysis by the AI ​​analysis unit. For example, if a user is aiming to run a marathon, the AI ​​will suggest a long-distance running program and set appropriate paces and rest days. The feedback providing unit provides feedback and motivation to the user based on the training program provided by the training program providing unit. For example, the AI ​​may provide regular feedback to the user and evaluate their training progress. It may also provide messages and advice to motivate the user. For example, sending a message such as "Great pace! Keep it up" may motivate the user. This allows the training program providing system according to the embodiment to maximize the effectiveness of the user's training. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students or teachers wish to receive feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending them directly to students and parents.

[0060] The data collection unit can additionally collect the user's food and water intake and integrate it with the training data to comprehensively evaluate the training effect. For example, the data collection unit allows the user to input their daily food intake into a smartphone app and link that data with the activity tracker. For example, the data collection unit records the calorie intake and nutrient balance and integrates it with the training data. The data collection unit also records the amount of water the user drinks and integrates it with the training data. For example, the data collection unit records the amount and type of water the user drinks and integrates it with the training data. This allows for a more accurate evaluation of the training effect by taking the user's food and water intake into account.

[0061] The data collection unit can measure the user's stress level and integrate it with training data to consider the impact of stress on training. For example, the data collection unit adds a function to measure stress levels to the wearable device and analyzes heart rate variability and electrodermal activity. For example, the data collection unit adjusts training intensity during periods of high stress. The data collection unit also measures stress levels based on the user's self-report. For example, the data collection unit records the times and causes of stress felt by the user and integrates this with training data. The data collection unit also evaluates stress levels based on measurements of stress hormones. For example, the data collection unit analyzes the user's saliva or blood to measure stress hormone levels. This allows for a more accurate evaluation of training effectiveness by taking the user's stress level into consideration.

[0062] The data collection unit is equipped with an emotion estimation function and can collect the user's emotional state in real time, integrate it with training data, and reflect it in the training program. The data collection unit, for example, is equipped with an emotion estimation function in a wearable device and analyzes the user's facial expressions and voice to collect the user's emotional state in real time. For example, emotional changes during training are recorded. The data collection unit also collects the user's emotional state using a biosensor. For example, the data collection unit analyzes heart rate and electrodermal activity to evaluate the emotional state. The data collection unit also collects the emotional state based on the user's self-report. For example, the data collection unit records the emotions felt by the user and their causes and integrates them with the training data. This allows the training program to be more personalized by taking the user's emotional state into consideration.

[0063] The data collection unit can collect additional environmental data and integrate it with the training data to optimize training conditions. For example, the data collection unit may link weather data to the wearable device to optimize the user's training conditions. For example, the data collection unit may adjust training intensity when the temperature or humidity is high. The data collection unit may also collect air quality data to optimize training conditions. For example, the data collection unit may measure the concentration of pollutants in the air and select a training location. The data collection unit may also collect temperature and humidity data to optimize training conditions. For example, the data collection unit may determine whether indoor or outdoor training is more suitable. This allows the training conditions to be optimized by taking environmental data into consideration.

[0064] The data collection unit can compare the data with other users' data to evaluate the relative fitness level. For example, the data collection unit builds a system that anonymizes the user's training data and compares it with the data of other users. For example, the data collection unit compares the user's training data with that of users of the same age or fitness level. The data collection unit also uploads the user's training data to the cloud and integrates it with the data of other users. For example, the data collection unit collects and compares data from users who use fitness apps. The data collection unit also develops an algorithm to evaluate the user's relative fitness level based on the user's training data. For example, the data collection unit compares the user's training data with the average value of the same age group or ranks performance. This makes it possible to evaluate the relative fitness level by comparing it with the data of other users.

[0065] The data collection unit can use the emotion estimation function to record emotions felt by the user during training and provide training advice based on the emotions. For example, the data collection unit uses the emotion estimation function to record emotions felt by the user during training in real time. For example, it provides advice to reinforce positive emotions during training. The data collection unit also analyzes the user's emotion data and develops an algorithm to provide training advice based on the emotions. For example, if the user is feeling stressed, it suggests relaxation exercises. The data collection unit also adjusts the training program based on the user's emotion data. For example, if the user is feeling fatigued, it suggests increasing rest days. In this way, the effectiveness of training can be maximized by providing training advice based on the user's emotions.

[0066] The AI ​​analysis unit can learn the user's past training data and predict long-term training effects. For example, the AI ​​analysis unit uses a generative AI to analyze the user's past training data and develop an algorithm to predict long-term training effects. For example, it predicts future fitness levels based on past data. The AI ​​analysis unit also learns the user's training history and predicts long-term training effects. For example, it analyzes the user's training patterns and suggests effective training plans. The AI ​​analysis unit also tracks changes in the user's fitness level and evaluates long-term training effects. For example, it predicts improvements in the user's physical strength and health. This makes it possible to predict long-term training effects by learning from past training data.

[0067] The AI ​​analysis unit can adjust the training program taking into account the user's health condition. For example, the AI ​​analysis unit constructs a system in which the generation AI analyzes the user's health data (e.g., blood pressure and blood sugar levels) and adjusts the training program. For example, it suggests training intensity according to the health condition. The AI ​​analysis unit also develops an algorithm that adjusts the training program taking into account the user's health condition. For example, it suggests appropriate exercise types and intensities based on the user's health data. The AI ​​analysis unit also monitors the user's health condition and adjusts the training program. For example, it tracks fluctuations in the user's blood pressure and blood sugar levels and optimizes the training program. This allows the training program to be adjusted more appropriately by taking the user's health condition into account.

[0068] The AI ​​analysis unit can compare different training methods and suggest the most suitable method. For example, the AI ​​analysis unit constructs a system in which a generating AI analyzes different training methods (e.g., HIIT or yoga) and suggests the most suitable method to the user. For example, it selects a method based on the user's fitness level and goals. The AI ​​analysis unit also develops an algorithm that compares different training methods and suggests the most suitable method. For example, it selects an effective training method based on the user's training data. The AI ​​analysis unit also learns the user's training history and suggests the most suitable training method. For example, it suggests a new method based on the training methods that the user has used successfully in the past. This makes it possible to compare different training methods and suggest the most suitable method to the user.

[0069] The AI ​​analysis unit can anonymize a user's data and integrate it with the data of other users to analyze training effects. For example, the AI ​​analysis unit builds a system in which a generating AI anonymizes a user's training data and integrates it with the data of other users to analyze training effects. For example, it compares the user's data with users of the same age or fitness level. The AI ​​analysis unit also develops algorithms to anonymize the user's data and integrate it with the data of other users. For example, it deletes personal information and encrypts the data. The AI ​​analysis unit also integrates the data with other users' data to analyze training effects. For example, it compares the user's data with the average value of the same age group and ranks their performance. This allows for a more accurate analysis of training effects by anonymizing the user's data and integrating it with the data of other users.

[0070] The AI ​​analysis unit can use the emotion estimation function to compare a user's emotional data with other users and form an emotion-based training community. For example, the AI ​​analysis unit uses the emotion estimation function to compare a user's emotional data with other users and build a system to form an emotion-based training community. For example, it connects users with strong positive emotions. The AI ​​analysis unit also compares a user's emotional data with other users and develops an algorithm to form an emotion-based training community. For example, it groups users who share common emotions based on the user's emotional data. The AI ​​analysis unit also forms an emotion-based training community and promotes interaction between users. For example, users share their training progress and emotions through online forums or group chats. In this way, an emotion-based training community can be formed by comparing a user's emotional data with other users.

[0071] The training program providing unit can adjust the training program taking into account the user's lifestyle. For example, the training program providing unit constructs a system in which a generation AI analyzes the user's work schedule and adjusts the training program. For example, it suggests a short training session on busy days. The training program providing unit also adjusts the training program taking into account the user's home situation. For example, it adjusts the time and frequency of training according to home circumstances. The training program providing unit also provides a training program that reflects the user's hobbies and interests. For example, it suggests a program that incorporates the user's favorite sports. This allows the training program to be adjusted more appropriately by taking into account the user's lifestyle.

[0072] The training program providing unit can provide training programs that reflect the user's preferences and interests. For example, the training program providing unit constructs a system in which a generating AI analyzes the user's preferences and interests and provides training programs based on the analysis. For example, the training program providing unit proposes programs that incorporate the user's favorite sports. The training program providing unit also develops an algorithm that provides training programs that reflect the user's interests. For example, the training program providing unit proposes programs that incorporate fitness activities that the user is interested in. The training program providing unit also provides training programs that reflect the user's preferences and interests. For example, the training program providing unit proposes training programs that can be done while the user listens to their favorite music. This allows the training programs to be more personalized by reflecting the user's preferences and interests.

[0073] The training program providing unit can use the emotion estimation function to provide a training program that corresponds to the user's emotional state. The training program providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system that provides a training program based on that. For example, if the user has strong positive emotions, it suggests a challenging training session. The training program providing unit also develops an algorithm that adjusts the training program based on the user's emotional state. For example, if the user is feeling stressed, it suggests a relaxing exercise. The training program providing unit also optimizes the training program based on the user's emotional state. For example, if the user is feeling fatigued, it suggests increasing the number of rest days. In this way, the training effect can be maximized by providing a training program that corresponds to the user's emotional state.

[0074] The training program providing unit can simulate different training programs and select the most effective program. For example, the training program providing unit constructs a system in which a generation AI simulates different training programs and suggests the most effective program to the user. For example, it compares multiple programs and selects the most effective one. The training program providing unit also develops an algorithm that simulates different training programs and selects the most effective program. For example, it selects an effective training program based on the user's training data. The training program providing unit also learns the user's training history and suggests the most effective training program. For example, it suggests a new program based on the training programs that the user has been successful with in the past. This makes it possible to select the most effective program by simulating different training programs.

[0075] The training program providing unit can share the user's training program with other users and improve it based on their feedback. For example, the training program providing unit builds a system in which a generation AI shares the user's training program with other users and improves it based on their feedback. For example, it collects comments and ratings on the training program. The training program providing unit also develops an algorithm that shares the user's training program with other users and improves it based on their feedback. For example, it uploads the user's training program to the cloud and collects feedback from other users. The training program providing unit also improves the training program based on their feedback. For example, it optimizes the program based on comments and ratings on the user's training program. In this way, a more effective program can be provided by sharing the training program with other users and improving it based on their feedback.

[0076] The training program providing unit can use the emotion estimation function to provide variations of the training program based on the user's emotions. The training program providing unit, for example, uses the emotion estimation function to build a system that provides variations of the training program according to the user's emotional state. For example, it suggests challenging training when the user has strong positive emotions. The training program providing unit also develops an algorithm that provides variations of the training program based on the user's emotional state. For example, it suggests relaxing exercises when the user is feeling stressed. The training program providing unit also provides variations of the training program based on the user's emotional state. For example, it suggests increasing the number of rest days when the user is feeling fatigued. In this way, the effectiveness of the training can be maximized by providing variations of the training program based on the user's emotions.

[0077] The feedback providing unit visualizes the user's training results, allowing the user to intuitively understand their progress. For example, the feedback providing unit constructs a system in which a generation AI visualizes the user's training results, allowing the user to intuitively understand their progress. For example, the training results are displayed using graphs and charts. The feedback providing unit also develops an algorithm to visualize the user's training results. For example, the results are displayed in a visually easy-to-understand format based on the user's training data. The feedback providing unit also evaluates the user's progress based on the user's training results. For example, the feedback providing unit visualizes the user's level of goal achievement and changes in training frequency and intensity. In this way, the user's training results are visualized, allowing the user to intuitively understand their progress.

[0078] The feedback providing unit can use the emotion estimation function to provide motivational messages according to the user's emotional state. The feedback providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system that provides motivational messages based on that. For example, an encouraging message is sent when the user has strong positive emotions. The feedback providing unit also develops an algorithm that provides motivational messages based on the user's emotional state. For example, if the user is feeling stressed, a message encouraging relaxation is sent. The feedback providing unit also provides motivational messages based on the user's emotional state. For example, if the user is feeling fatigued, a message encouraging rest is sent. In this way, by providing motivational messages according to the user's emotional state, training motivation can be maintained.

[0079] The feedback providing unit can provide a function for competing with other users based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI provides a function for competing with other users based on the user's training data. For example, it provides a function for competing in running distance or pace. The feedback providing unit also develops an algorithm for competing with other users based on the user's training data. For example, it provides a ranking system or a battle mode based on the user's training data. The feedback providing unit also provides a competition function based on the user's training data. For example, it provides a system in which users compete with each other for points. By providing a function for competing with other users, it is possible to increase the user's motivation.

[0080] The feedback providing unit can share successful training cases based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI shares successful training cases based on the user's training data. For example, the feedback providing unit introduces successful training programs to other users. The feedback providing unit also develops an algorithm for sharing successful cases based on the user's training data. For example, the feedback providing unit identifies successful training programs based on the user's training data. The feedback providing unit also shares successful cases based on the user's training data. For example, the feedback providing unit introduces goals that the user has achieved or improved health conditions to other users. This makes it possible to motivate other users by sharing successful training cases.

[0081] The feedback providing unit uses the emotion estimation function to provide feedback based on the user's emotions, thereby maintaining motivation. The feedback providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system that provides feedback based on that. For example, an encouraging message is sent when the user has strong positive emotions. The feedback providing unit also develops an algorithm that provides feedback based on the user's emotional state. For example, if the user is feeling stressed, the feedback providing unit sends a message encouraging the user to relax. The feedback providing unit also provides feedback based on the user's emotional state. For example, if the user is feeling tired, the feedback providing unit sends a message encouraging the user to take a rest. In this way, motivation can be maintained by providing feedback based on the user's emotions.

[0082] The feedback providing unit can suggest optimal rest days based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI analyzes the user's training data and suggests optimal rest days. For example, rest days are set according to the frequency and intensity of training. The feedback providing unit also develops an algorithm that suggests optimal rest days based on the user's training data. For example, it suggests effective rest days based on the user's training data. The feedback providing unit also optimizes rest days based on the user's training data. For example, it analyzes the user's training data and adjusts rest days. This allows the user's training effect to be maximized by suggesting optimal rest days.

[0083] The feedback providing unit can adjust the training intensity in stages based on the user's training data. For example, the feedback providing unit constructs a system in which a generation AI analyzes the user's training data and adjusts the training intensity in stages. For example, the intensity is increased or decreased depending on the training results. The feedback providing unit also develops an algorithm for adjusting the training intensity in stages based on the user's training data. For example, the feedback providing unit suggests an effective training intensity based on the user's training data. The feedback providing unit also adjusts the training intensity based on the user's training data. For example, the feedback providing unit analyzes the user's training data and optimizes the training intensity. In this way, the training effect of the user can be maximized by adjusting the training intensity in stages.

[0084] The feedback providing unit can use the emotion estimation function to provide a training schedule that corresponds to the user's emotional state. For example, the feedback providing unit uses the emotion estimation function to analyze the user's emotional state in real time and build a system that provides a training schedule based on that. For example, if the user has strong positive emotions, the feedback providing unit suggests a challenging training session. The feedback providing unit also develops an algorithm that provides a training schedule based on the user's emotional state. For example, if the user is feeling stressed, the feedback providing unit suggests a relaxing exercise. The feedback providing unit also provides a training schedule based on the user's emotional state. For example, if the user is feeling fatigued, the feedback providing unit suggests increasing the number of rest days. In this way, the effectiveness of training can be maximized by providing a training schedule that corresponds to the user's emotional state.

[0085] The feedback providing unit can simulate different training schedules based on the user's training data and select the optimal schedule. For example, the feedback providing unit constructs a system in which a generation AI simulates different training schedules based on the user's training data and proposes the optimal schedule. For example, it compares multiple schedules and selects the optimal one. The feedback providing unit also develops an algorithm that simulates different training schedules based on the user's training data and selects the optimal schedule. For example, it selects an effective training schedule based on the user's training data. The feedback providing unit also optimizes the training schedule based on the user's training data. For example, it analyzes the user's training data and adjusts the training schedule. In this way, it is possible to select the optimal schedule by simulating different training schedules.

[0086] The feedback providing unit can share the user's training schedule with other users and improve it based on their feedback. For example, the feedback providing unit constructs a system in which a generation AI shares the user's training schedule with other users and improves it based on their feedback. For example, the feedback providing unit collects comments and ratings on the training schedule. The feedback providing unit also develops an algorithm that shares the user's training schedule with other users and improves it based on their feedback. For example, the feedback providing unit uploads the user's training schedule to the cloud and collects feedback from other users. The feedback providing unit also improves the training schedule based on their feedback. For example, the schedule is optimized based on comments and ratings on the user's training schedule. In this way, a more effective schedule can be provided by sharing the training schedule with other users and improving it based on their feedback.

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

[0088] The training program providing system can further include a hobby analysis unit that takes into account the user's hobbies and interests. The hobby analysis unit, for example, analyzes the user's favorite sports and activities and provides a training program based on them. For example, if the user likes soccer, the hobby analysis unit can suggest soccer-related training. The hobby analysis unit can also analyze the user's musical preferences and suggest music suitable for training. For example, if the user likes rock music, rock music can be played during training. This makes it possible to provide a training program that reflects the user's hobbies and interests, thereby making training more enjoyable.

[0089] The training program providing system may further include a sleep analysis unit that collects the user's sleep data and reflects it in the training program. The sleep analysis unit, for example, analyzes the user's sleep patterns and sleep quality and adjusts the training program. For example, if the user is not getting enough sleep, it may suggest reducing the training intensity. The sleep analysis unit may also suggest optimal training times based on the user's sleep data. For example, if the user is a nocturnal person, it may suggest nighttime training. This allows the training program to be adjusted more effectively by taking the user's sleep data into consideration.

[0090] The training program providing system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, periodically measures the user's blood pressure and blood sugar levels and adjusts the training program. For example, if the user's blood pressure is high, it may suggest low-intensity training. The health monitoring unit may also develop an algorithm that optimizes the training program based on the user's health condition. For example, it may analyze the user's health data and suggest appropriate exercise types and intensities. This allows the training program to be more appropriately adjusted by taking the user's health condition into consideration.

[0091] The training program providing system may further include an emotion analysis unit that analyzes the user's emotional state in real time and provides training advice based on the emotion. The emotion analysis unit may, for example, analyze the user's facial expressions and voice to collect the user's emotional state and reflect this in the training program. For example, if the user is feeling stressed, it may suggest relaxing exercises. The emotion analysis unit may also develop an algorithm that adjusts the training program based on the user's emotional data. For example, if the user is feeling positive, it may suggest challenging training. This allows the training program to be more personalized by taking the user's emotional state into consideration.

[0092] The training program providing system can further include a motivation providing unit that provides motivational messages based on the user's emotional state. The motivation providing unit, for example, analyzes the user's emotional state in real time and provides motivational messages based on the analysis. For example, if the user is feeling positive, it sends an encouraging message. The motivation providing unit can also evaluate the progress of training based on the user's emotional state. For example, if the user is feeling stressed, it sends a message encouraging the user to relax. In this way, by providing motivational messages according to the user's emotional state, it is possible to maintain motivation for training.

[0093] The training program providing system may further include a competition providing unit that provides a function for competing with other users based on the user's training data. The competition providing unit may, for example, build a system for competing with other users based on the user's training data. For example, it may provide a function for competing in running distance or pace. The competition providing unit may also develop an algorithm that provides a ranking system or a battle mode based on the user's training data. For example, it may provide a system in which users compete with each other for points. This may increase the user's motivation by providing a function for competing with other users.

[0094] The training program providing system can further include an emotion adjustment unit that adjusts the training program based on the user's emotional state. The emotion adjustment unit, for example, analyzes the user's emotional state in real time and provides a training program based on that. For example, if the user is feeling stressed, it can suggest relaxing exercises. The emotion adjustment unit can also develop an algorithm that adjusts the training program based on the user's emotional data. For example, if the user is feeling positive, it can suggest challenging training. This allows the training program to be more personalized by taking the user's emotional state into consideration.

[0095] The training program providing system may further include a rest day suggestion unit that suggests optimal rest days based on the user's training data. The rest day suggestion unit may, for example, analyze the user's training data and suggest optimal rest days. For example, the rest days may be set based on the frequency and intensity of training. The rest day suggestion unit may also develop an algorithm that suggests effective rest days based on the user's training data. For example, the algorithm may analyze the user's training data and adjust the rest days. This may maximize the effect of the user's training by suggesting optimal rest days.

[0096] The training program providing system may further include an emotion scheduling unit that provides a training schedule based on the user's emotional state. The emotion scheduling unit, for example, analyzes the user's emotional state in real time and provides a training schedule based on that. For example, if the user has positive emotions, it may suggest a challenging training session. The emotion scheduling unit may also develop an algorithm that adjusts the training schedule based on the user's emotional data. For example, if the user is feeling stressed, it may suggest a relaxing exercise. This allows the training schedule to be more personalized by taking the user's emotional state into consideration.

[0097] The training program providing system may further include an intensity adjustment unit that gradually adjusts the training intensity based on the user's training data. The intensity adjustment unit, for example, analyzes the user's training data and gradually adjusts the training intensity. For example, it increases or decreases the intensity depending on the training results. The intensity adjustment unit may also develop an algorithm that suggests an effective training intensity based on the user's training data. For example, it may analyze the user's training data and optimize the training intensity. This allows the user's training effect to be maximized by gradually adjusting the training intensity.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The data collection unit collects training data from the user's device. For example, an activity tracker or heart rate monitor can be used to record information such as steps, distance, calories burned, heart rate, and sleep patterns. Alternatively, the data collection unit can also collect training data from a smartphone or tablet. Step 2: The AI ​​analysis unit analyzes the data collected by the data collection unit. For example, the generation AI considers the user's training history and current fitness level to generate an optimal training program. The generation AI analyzes the user's training data using text generation AI (e.g., LLM) or multimodal generation AI. For example, it analyzes the user's heart rate data and suggests appropriate training intensity. Step 3: The training program provider provides the user with an optimal training program based on the results of the analysis by the AI ​​analysis unit. For example, if the user is aiming to run a marathon, the AI ​​will suggest a long-distance running program and set an appropriate pace and rest days. Step 4: The feedback provider provides feedback and motivation to the user based on the training program provided by the training program provider. For example, the AI ​​provides regular feedback to the user to evaluate the progress of the training. It also provides messages and advice to increase motivation. For example, it motivates the user by sending a message such as, "Great pace! Keep it up!"

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects training data from a user's device; an AI analysis unit that analyzes the data collected by the data collection unit; a training program providing unit that provides an optimal training program to a user based on the results of the analysis by the AI ​​analysis unit; a feedback providing unit that provides feedback and motivation to the user based on the training program provided by the training program providing unit. A system characterized by:

2. The data collection unit Equipped with an emotion estimation function, it collects the user's emotional state in real time, integrates it with the training data, and reflects it in the training program.

2. The system of claim 1.

3. The data collection unit Collect additional environmental data and integrate it with the training data to optimize training conditions.

2. The system of claim 1.

4. The AI ​​analysis unit Learns from a user's past training data and predicts long-term training effects 2. The system of claim 1.

5. The training program providing unit Adjust your training program to take into account your lifestyle 2. The system of claim 1.

6. The feedback providing unit: Proposes specific improvements based on user training data 2. The system of claim 1.

7. The feedback providing unit: Providing a training schedule that responds to the user's emotional state 2. The system of claim 1.

8. The AI ​​analysis unit Analyze user emotion data and generate emotion-based training programs 2. The system of claim 1.

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