system

Abstract

The system according to the embodiment aims to quantify the performance of children during training and provide effective measures for improvement. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects performance data of children during training. The analysis unit analyzes the data collected by the collection unit and quantifies performance indicators. The provision unit provides the analysis results obtained by the analysis unit.

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] Previous techniques have had the challenge of effectively quantifying children's performance during training and providing suggestions for improvement.

[0005] The system according to the embodiment aims to quantify the performance of children during training and provide effective measures for improvement. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects performance data of children during training. The analysis unit analyzes the data collected by the collection unit and quantifies performance indicators. The provision unit provides the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quantify the performance of children during training and provide effective measures for improvement. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 performance analysis system according to an embodiment of the present invention quantifies children's performance during training and provides effective improvement measures. The performance analysis system collects performance data from children during training, analyzes it using AI, and quantifies each performance indicator. The analysis results are provided to children, coaching staff, and parents. For example, the performance analysis system uses sensors to collect data such as swing trajectory, bat speed, and ball distance. Next, the performance analysis system analyzes the collected data using AI to analyze the swing trajectory and quantify swing accuracy and bat speed. The analysis results are provided to children, coaching staff, and parents. For example, if the swing trajectory is inaccurate, the system identifies the cause and suggests improvement measures. Specifically, the system suggests areas for improvement, such as the swing angle or the way the bat is gripped. Furthermore, if the bat speed is slow, the system suggests specific training methods, such as strength training or improving swing form. This allows children to objectively understand their own performance and implement effective improvement measures. Furthermore, even if coaching staff or parents lack sufficient expertise, they can implement effective practice and training methods based on the data and improvement measures provided by the system. Furthermore, the performance analysis system can also track changes in children's performance as they grow. For example, by collecting data periodically and comparing it with past data, it is possible to understand the degree of growth and progress of improvement. This allows for the creation of long-term training plans and supports children's development. In this way, the performance analysis system can automatically analyze children's performance and provide effective improvement measures. For example, children can quickly and accurately understand their own performance and identify specific areas for improvement. Furthermore, even if coaching staff or parents do not have specialized knowledge, they can implement effective practice methods and training based on the data and improvement measures provided by the system.

[0029] A performance analysis system according to an embodiment includes a collection unit, an analysis unit, and a providing unit. The collection unit collects performance data of children during training. The performance data includes, for example, a swing trajectory, a bat speed, and a hit distance, but is not limited to these examples. For example, the collection unit collects the swing trajectory using a sensor. The collection unit can also collect the bat speed using a velocity sensor. The collection unit can also collect the hit distance using a distance sensor. For example, the collection unit measures the swing trajectory using a high-precision sensor and collects the data. The velocity sensor measures the bat speed in real time and collects the data. The distance sensor accurately measures the hit distance and collects the data. The analysis unit analyzes the data collected by the collection unit and quantifies a performance index. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit quantifies the accuracy of the swing using statistical analysis. The analysis unit can also quantify the bat speed using a machine learning algorithm. The analysis unit can also quantify the distance traveled by the ball using deep learning. For example, the analysis unit analyzes data on the trajectory of the swing and evaluates the accuracy of the swing. A machine learning algorithm analyzes data on the speed of the bat and quantifies the speed. Deep learning analyzes data on the distance traveled by the ball and quantifies the distance. The providing unit provides the analysis results obtained by the analysis unit. The providing may indicate, for example, improvements to the swing angle or the gripping method of the bat, but is not limited to such examples. For example, the providing unit indicates improvements to the swing angle. The providing unit can also indicate improvements to the gripping method of the bat. The providing unit can also suggest specific training methods, such as strength training or improving the swing form. For example, the providing unit indicates a specific method for improving the swing angle. The providing unit indicates a specific method for improving the gripping method of the bat. The providing unit suggests a specific method for strength training. In this way, the performance analysis system according to the embodiment can automatically analyze children's performance and provide effective improvement measures.Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide improvements using an AI model that receives the analysis results obtained by the analysis unit as input and outputs improvements.

[0030] The collection unit can collect data on the swing trajectory, bat speed, and ball flight distance. The collection unit, for example, measures the swing trajectory with a sensor and collects the data. For example, the collection unit measures the swing trajectory with a high-precision sensor and collects the data. The collection unit can also measure the bat speed with a velocity sensor and collect the data. For example, the collection unit measures the bat speed in real time and collects the data. The collection unit can also measure the ball flight distance with a distance sensor and collect the data. For example, the collection unit accurately measures the ball flight distance and collects the data. This allows the collection unit to collect detailed performance data of children currently training. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data measured by the sensor to a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and quantify the swing accuracy and bat speed. For example, the analysis unit can analyze the collected data using statistical analysis to quantify the swing accuracy. For example, the analysis unit can analyze data on the swing motion trajectory and evaluate the swing accuracy. The analysis unit can also analyze the collected data using a machine learning algorithm to quantify the bat speed. For example, the analysis unit can analyze data on the bat speed and quantify the speed. The analysis unit can also analyze the collected data using deep learning to quantify the flight distance of the hit ball. For example, the analysis unit can analyze data on the flight distance of the hit ball and quantify the flight distance. In this way, the analysis unit can analyze the collected data and quantify a performance indicator. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0032] The providing unit can indicate areas for improvement in the swing angle or the gripping of the bat based on the analysis results. The providing unit, for example, indicates areas for improvement in the swing angle based on the analysis results. For example, the providing unit indicates a specific method for improving the swing angle. The providing unit can also indicate areas for improvement in the gripping of the bat based on the analysis results. For example, the providing unit indicates a specific method for improving the gripping of the bat. This allows the providing unit to indicate specific areas for improvement based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the analysis results into a generating AI and cause the generating AI to suggest areas for improvement.

[0033] The providing unit can propose a specific training method for strength training or swing form improvement based on the analysis results. The providing unit, for example, proposes a specific strength training method based on the analysis results. For example, the providing unit indicates the type, number of repetitions, load, etc. of strength training. The providing unit can also propose a specific method for improving swing form based on the analysis results. For example, the providing unit indicates checkpoints and improvement procedures for swing form. This allows the providing unit to propose a specific training method based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results into a generating AI and have the generating AI execute the training method proposal.

[0034] The collection unit periodically collects data and compares it with past data, thereby grasping the degree of growth or progress of improvement. The collection unit, for example, periodically collects data and compares it with past data. For example, the collection unit may collect data weekly and compare it with past data. The collection unit may also collect data monthly and compare it with past data. This allows the collection unit to grasp the degree of growth or progress of improvement. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the periodically collected data into the generation AI and cause the generation AI to grasp the degree of growth or progress of improvement.

[0035] The collection unit can collect data based on environmental conditions during training. For example, in a hot and humid environment, the collection unit collects data for a short period of time, taking into account the child's physical condition. For example, in a hot and humid environment, the collection unit collects data for a short period of time, taking into account the child's physical condition. The collection unit can also collect data after a warm-up in a low-temperature environment. For example, the collection unit collects data after a warm-up in a low-temperature environment. The collection unit can also collect indoor training data in rainy weather. For example, the collection unit collects indoor training data in rainy weather. In this way, the collection unit can collect more accurate data by collecting data taking into account the environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input environmental condition data to a generation AI and cause the generation AI to adjust the data collection.

[0036] The collection unit can monitor the child's physical condition and fatigue level and select an appropriate collection timing. The collection unit, for example, monitors the child's heart rate and collects data when it is stable. For example, the collection unit monitors the child's heart rate and collects data when it is stable. The collection unit can also monitor the child's fatigue level and collect data when the child is less tired. For example, the collection unit monitors the child's fatigue level and collects data when the child is less tired. The collection unit can also monitor the child's body temperature and collect data when it is within a normal range. For example, the collection unit monitors the child's body temperature and collects data when it is within a normal range. This allows the collection unit to select the optimal collection timing taking the child's physical condition and fatigue level into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's physical condition data to the generation AI and cause the generation AI to select the collection timing.

[0037] The collection unit can collect data from multiple angles by combining multiple sensors. For example, the collection unit can collect a swing motion trajectory by combining an acceleration sensor and a gyro sensor. For example, the collection unit can collect a swing motion trajectory by combining an acceleration sensor and a gyro sensor. The collection unit can also monitor a physical condition during training by combining a heart rate sensor and an acceleration sensor. For example, the collection unit can monitor a physical condition during training by combining a heart rate sensor and an acceleration sensor. The collection unit can also collect a bat speed and a child's physical condition simultaneously by combining a gyro sensor and a heart rate sensor. For example, the collection unit can collect a bat speed and a child's physical condition simultaneously by combining a gyro sensor and a heart rate sensor. In this way, the collection unit can obtain more detailed data by collecting data from multiple angles by combining different sensors. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data collected by multiple sensors into a generation AI and have the generation AI analyze the data.

[0038] When collecting data, the collection unit can customize the collection method based on the child's past training data. For example, the collection unit may focus on collecting a specific swing pattern from the past data. For example, the collection unit may focus on collecting a specific swing pattern from the past data. The collection unit may also employ a collection method specialized for a slow bat speed from the past data. For example, the collection unit may employ a collection method specialized for a slow bat speed from the past data. The collection unit may also employ a collection method specialized for a short ball flight distance from the past data. For example, the collection unit employs a collection method specialized for a short ball flight distance from the past data. This allows the collection unit to customize the collection method by referring to the past training data, enabling more effective data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input past training data into the generation AI and cause the generation AI to customize the collection method.

[0039] When collecting data, the collection unit can select the type of data to collect based on the child's training goal. For example, if the goal is to improve swing accuracy, the collection unit can focus on collecting the swing motion trajectory. For example, if the goal is to improve swing accuracy, the collection unit can focus on collecting the swing motion trajectory. Furthermore, if the goal is to improve bat speed, the collection unit can also focus on collecting the bat speed. For example, if the goal is to improve bat speed, the collection unit can focus on collecting the bat speed. Furthermore, if the goal is to improve the distance of a hit ball, the collection unit can also focus on collecting the distance of a hit ball. For example, if the goal is to improve the distance of a hit ball, the collection unit can focus on collecting the distance of a hit ball. In this way, by selecting the type of data to collect based on the training goal, effective data collection toward achieving the goal is possible. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input a training goal to the generation AI and cause the generation AI to select the collected data.

[0040] The collection unit can adjust the collection method by reflecting feedback from the child's training partner or coach when collecting data. For example, the collection unit adjusts the collection method for the swing motion trajectory based on the coach's feedback. For example, the collection unit adjusts the collection method for the swing motion trajectory based on the coach's feedback. The collection unit can also adjust the collection method for the bat speed based on the training partner's feedback. For example, the collection unit adjusts the collection method for the bat speed based on the training partner's feedback. The collection unit can also adjust the collection method for the hit ball distance by combining the feedback from the coach and the training partner. For example, the collection unit adjusts the collection method for the hit ball distance by combining the feedback from the coach and the training partner. This allows the collection unit to adjust the collection method by reflecting the feedback from the training partner or coach, thereby enabling more effective data collection. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit may input feedback data to a generation AI and cause the generation AI to adjust the collection method.

[0041] During analysis, the analysis unit can correct the analysis results based on the environmental conditions during training. The analysis unit, for example, corrects and analyzes data in a hot and humid environment. For example, the analysis unit corrects and analyzes data in a hot and humid environment. The analysis unit can also correct and analyze data in a low temperature environment. For example, the analysis unit corrects and analyzes data in a low temperature environment. The analysis unit can also correct and analyze data in rainy weather. For example, the analysis unit corrects and analyzes data in rainy weather. In this way, the analysis unit can correct the analysis results taking into account the environmental conditions, thereby enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input environmental condition data to the generation AI and cause the generation AI to correct the analysis results.

[0042] During analysis, the analysis unit can correct the analysis results based on the child's physical condition and fatigue level. The analysis unit corrects the analysis results, for example, taking into account the child's heart rate. For example, the analysis unit corrects the analysis results taking into account the child's heart rate. The analysis unit can also correct the analysis results taking into account the child's fatigue level. For example, the analysis unit corrects the analysis results taking into account the child's fatigue level. The analysis unit can also correct the analysis results taking into account the child's body temperature. For example, the analysis unit corrects the analysis results taking into account the child's body temperature. This allows the analysis unit to correct the analysis results taking into account the child's physical condition and fatigue level, thereby enabling more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the child's physical condition data into the generation AI and cause the generation AI to correct the analysis results.

[0043] The analysis unit can improve the accuracy of the analysis by combining multiple analysis methods during analysis. The analysis unit, for example, combines machine learning and deep learning to analyze the swing motion trajectory. For example, the analysis unit combines machine learning and deep learning to analyze the swing motion trajectory. The analysis unit can also analyze the bat speed by combining deep learning and statistical analysis. For example, the analysis unit combines deep learning and statistical analysis to analyze the bat speed. The analysis unit can also analyze the flight distance of a batted ball by combining machine learning and statistical analysis. For example, the analysis unit combines machine learning and statistical analysis to analyze the flight distance of a batted ball. This allows the analysis unit to improve the accuracy of the analysis by combining different analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input multiple analysis methods into the generation AI and cause the generation AI to improve the analysis accuracy.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the child's past training data. The analysis unit, for example, improves the analysis accuracy of the swing motion trajectory based on past data. For example, the analysis unit improves the analysis accuracy of the swing motion trajectory based on past data. The analysis unit can also improve the analysis accuracy of the bat speed based on past data. For example, the analysis unit improves the analysis accuracy of the bat speed based on past data. The analysis unit can also improve the analysis accuracy of the ball's flight distance based on past data. For example, the analysis unit improves the analysis accuracy of the ball's flight distance based on past data. In this way, the analysis unit can improve the analysis accuracy by referring to past training data, thereby enabling more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input past training data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0045] During analysis, the analysis unit can customize the analysis results based on the child's training goals. For example, if the goal is to improve swing accuracy, the analysis unit customizes the analysis results of the swing motion trajectory. For example, if the goal is to improve swing accuracy, the analysis unit customizes the analysis results of the swing motion trajectory. The analysis unit can also customize the analysis results of the bat speed if the goal is to improve bat speed. For example, if the goal is to improve bat speed, the analysis unit customizes the analysis results of the bat speed. The analysis unit can also customize the analysis results of the bat distance if the goal is to improve the bat distance. For example, if the goal is to improve the bat distance, the analysis unit customizes the analysis results of the bat distance. In this way, the analysis unit customizes the analysis results based on the training goals, enabling effective analysis toward goal achievement. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input training goals into the generation AI and cause the generation AI to customize the analysis results.

[0046] During analysis, the analysis unit can adjust the analysis method by reflecting feedback from the child's training partner or coach. The analysis unit, for example, adjusts the analysis method of the swing motion trajectory based on feedback from the coach. For example, the analysis unit adjusts the analysis method of the swing motion trajectory based on feedback from the coach. The analysis unit can also adjust the analysis method of the bat speed based on feedback from the training partner. For example, the analysis unit adjusts the analysis method of the bat speed based on feedback from the training partner. The analysis unit can also adjust the analysis method of the hit ball distance by combining feedback from the coach and the training partner. For example, the analysis unit adjusts the analysis method of the hit ball distance by combining feedback from the coach and the training partner. This enables the analysis unit to adjust the analysis method by reflecting feedback from the training partner or coach, thereby enabling more effective analysis. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit may input feedback data to a generation AI and cause the generation AI to adjust the analysis method.

[0047] The providing unit can suggest improvements based on the environmental conditions during training when providing the training data. The providing unit, for example, suggests improvements for training in a hot and humid environment. For example, the providing unit suggests improvements for training in a hot and humid environment. The providing unit can also suggest improvements for training in a low temperature environment. For example, the providing unit suggests improvements for training in a low temperature environment. The providing unit can also suggest improvements for training in rainy weather. For example, the providing unit suggests improvements for training in rainy weather. This allows the providing unit to suggest improvements taking into account the environmental conditions during training, thereby enabling more effective improvement. Some or all of the above-mentioned processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input environmental condition data to a generating AI and cause the generating AI to suggest improvements.

[0048] The providing unit can suggest points for improvement based on the child's physical condition and fatigue level at the time of providing the data. The providing unit, for example, suggests points for improvement taking into account the child's heart rate. For example, the providing unit suggests points for improvement taking into account the child's heart rate. The providing unit can also suggest points for improvement taking into account the child's fatigue level. For example, the providing unit suggests points for improvement taking into account the child's fatigue level. The providing unit can also suggest points for improvement taking into account the child's body temperature. For example, the providing unit suggests points for improvement taking into account the child's body temperature. This allows the providing unit to suggest points for improvement taking into account the child's physical condition and fatigue level, thereby enabling more appropriate improvements. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input data on the child's physical condition into the generating AI and cause the generating AI to suggest points for improvement.

[0049] The providing unit can effectively present improvements by combining multiple presentation methods during provision. The providing unit, for example, presents improvements by combining text and visuals. For example, the providing unit presents improvements by combining text and visuals. The providing unit can also present improvements by combining audio and visuals. For example, the providing unit presents improvements by combining audio and visuals. The providing unit can also present improvements by combining text, audio, and visuals. For example, the providing unit presents improvements by combining text, audio, and visuals. This enables the providing unit to present improvements more effectively by combining different presentation methods. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input presentation method data into a generation AI and cause the generation AI to present improvements.

[0050] The providing unit can customize the points for improvement based on the child's past training data when providing the data. The providing unit, for example, customizes the points for improvement of the swing motion trajectory based on past data. For example, the providing unit customizes the points for improvement of the swing motion trajectory based on past data. The providing unit can also customize the points for improvement of the bat speed based on past data. For example, the providing unit customizes the points for improvement of the bat speed based on past data. The providing unit can also customize the points for improvement of the ball's flight distance based on past data. For example, the providing unit customizes the points for improvement of the ball's flight distance based on past data. In this way, the providing unit can customize the points for improvement by referring to past training data, thereby enabling more effective improvement. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input past training data into the generating AI and cause the generating AI to customize the points for improvement.

[0051] The providing unit can suggest improvements based on the child's training goal during the provision. For example, if the goal is to improve swing accuracy, the providing unit suggests improvements to the swing motion trajectory. For example, if the goal is to improve swing accuracy, the providing unit suggests improvements to the swing motion trajectory. Furthermore, if the goal is to improve bat speed, the providing unit can also suggest improvements to bat speed. For example, if the goal is to improve bat speed, the providing unit suggests improvements to bat speed. Furthermore, if the goal is to improve the distance of a hit ball, the providing unit can also suggest improvements to the distance of a hit ball. For example, if the goal is to improve the distance of a hit ball, the providing unit suggests improvements to the distance of a hit ball. In this way, the providing unit suggests improvements based on the training goal, enabling effective improvement toward achieving the goal. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit may input the training goal into a generating AI and cause the generating AI to suggest improvements.

[0052] The providing unit can adjust points for improvement by reflecting feedback from the child's training partner or coach when providing the data. The providing unit, for example, adjusts points for improvement in the swing motion trajectory based on feedback from the coach. For example, the providing unit adjusts points for improvement in the swing motion trajectory based on feedback from the coach. The providing unit can also adjust points for improvement in bat speed based on feedback from the training partner. For example, the providing unit adjusts points for improvement in bat speed based on feedback from the training partner. The providing unit can also adjust points for improvement in the distance of the hit ball by combining feedback from the coach and the training partner. For example, the providing unit adjusts points for improvement in the distance of the hit ball by combining feedback from the coach and the training partner. This allows the providing unit to adjust points for improvement by reflecting feedback from the training partner or coach, enabling more effective improvement. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input feedback data to a generating AI and cause the generating AI to adjust points for improvement.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can adjust the accuracy of data collection based on the environmental conditions during training. For example, in a hot and humid environment, the sensitivity of the sensor can be adjusted to improve the accuracy of data collection. In a low-temperature environment, the operation of the sensor can be optimized to maintain the accuracy of data collection. Furthermore, in rainy weather, the waterproof performance of the sensor can be utilized to collect data. In this way, the collection unit can collect more accurate data by adjusting the accuracy of data collection based on the environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental condition data to the generation AI and cause the generation AI to adjust the accuracy of data collection.

[0055] During analysis, the analysis unit can select an analysis algorithm based on the environmental conditions during training. For example, in a hot and humid environment, a specific algorithm can be used to analyze data. In a low-temperature environment, a different algorithm can be used to analyze data. Furthermore, in rainy weather, data can be analyzed using an algorithm suitable for specific conditions. In this way, the analysis unit can perform more accurate analysis by selecting an analysis algorithm based on the environmental conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental condition data to the generation AI and have the generation AI select an analysis algorithm.

[0056] The providing unit can suggest a training method according to the environmental conditions during training based on the analysis results. For example, in a hot and humid environment, it can suggest increasing the frequency of hydration. In a low-temperature environment, it can suggest extending the warm-up time. Furthermore, in rainy weather, it can suggest indoor training methods. In this way, the providing unit can support more effective training by suggesting training methods according to environmental conditions. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input environmental condition data into a generating AI and cause the generating AI to suggest a training method.

[0057] The collection unit can monitor the child's physical condition and fatigue level and select an appropriate collection timing. For example, the collection unit can monitor the child's heart rate and collect data when it is stable. The child's fatigue level can also be monitored and collected data when fatigue is low. The collection unit can also monitor the child's body temperature and collect data when it is within a normal range. This allows the collection unit to select the optimal collection timing taking into account the child's physical condition and fatigue level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's physical condition data into the generation AI and have the generation AI select the collection timing.

[0058] During analysis, the analysis unit can improve the accuracy of the analysis by combining multiple analysis methods. For example, machine learning and deep learning can be combined to analyze the swing motion trajectory. Deep learning and statistical analysis can also be combined to analyze the bat speed. Furthermore, machine learning and statistical analysis can be combined to analyze the flight distance of the hit ball. This allows the analysis unit to improve the accuracy of the analysis by combining different analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input multiple analysis methods into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects performance data from children during training. Performance data includes the swing trajectory, bat speed, and ball distance. The collection unit collects this data using sensors. For example, a high-precision sensor measures the swing trajectory, a speed sensor measures the bat speed in real time, and a distance sensor accurately measures the ball distance. Step 2: The analysis unit analyzes the data collected by the collection unit and quantifies performance indicators. The analysis is performed using statistical analysis, machine learning algorithms, and deep learning. For example, statistical analysis is used to quantify swing accuracy, machine learning algorithms are used to quantify bat speed, and deep learning is used to quantify ball distance. Step 3: The providing unit provides the analysis results obtained by the analyzing unit. Providing the results includes suggesting areas for improvement, such as the swing angle or the grip of the bat, and suggesting specific training methods, such as strength training or improving swing form. For example, the providing unit suggests specific methods for improving the swing angle, the grip of the bat, and strength training.

[0061] (Example 2) A performance analysis system according to an embodiment of the present invention quantifies children's performance during training and provides effective improvement measures. The performance analysis system collects performance data from children during training, analyzes it using AI, and quantifies each performance indicator. The analysis results are provided to children, coaching staff, and parents. For example, the performance analysis system uses sensors to collect data such as swing trajectory, bat speed, and ball distance. Next, the performance analysis system analyzes the collected data using AI to analyze the swing trajectory and quantify swing accuracy and bat speed. The analysis results are provided to children, coaching staff, and parents. For example, if the swing trajectory is inaccurate, the system identifies the cause and suggests improvement measures. Specifically, the system suggests areas for improvement, such as the swing angle or the way the bat is gripped. Furthermore, if the bat speed is slow, the system suggests specific training methods, such as strength training or improving swing form. This allows children to objectively understand their own performance and implement effective improvement measures. Furthermore, even if coaching staff or parents lack sufficient expertise, they can implement effective practice and training methods based on the data and improvement measures provided by the system. Furthermore, the performance analysis system can also track changes in children's performance as they grow. For example, by collecting data periodically and comparing it with past data, it is possible to understand the degree of growth and progress of improvement. This allows for the creation of long-term training plans and supports children's development. In this way, the performance analysis system can automatically analyze children's performance and provide effective improvement measures. For example, children can quickly and accurately understand their own performance and identify specific areas for improvement. Furthermore, even if coaching staff or parents do not have specialized knowledge, they can implement effective practice methods and training based on the data and improvement measures provided by the system.

[0062] A performance analysis system according to an embodiment includes a collection unit, an analysis unit, and a providing unit. The collection unit collects performance data of children during training. The performance data includes, for example, a swing trajectory, a bat speed, and a hit distance, but is not limited to these examples. For example, the collection unit collects the swing trajectory using a sensor. The collection unit can also collect the bat speed using a velocity sensor. The collection unit can also collect the hit distance using a distance sensor. For example, the collection unit measures the swing trajectory using a high-precision sensor and collects the data. The velocity sensor measures the bat speed in real time and collects the data. The distance sensor accurately measures the hit distance and collects the data. The analysis unit analyzes the data collected by the collection unit and quantifies a performance index. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit quantifies the accuracy of the swing using statistical analysis. The analysis unit can also quantify the bat speed using a machine learning algorithm. The analysis unit can also quantify the distance traveled by the ball using deep learning. For example, the analysis unit analyzes data on the trajectory of the swing and evaluates the accuracy of the swing. A machine learning algorithm analyzes data on the speed of the bat and quantifies the speed. Deep learning analyzes data on the distance traveled by the ball and quantifies the distance. The providing unit provides the analysis results obtained by the analysis unit. The providing may indicate, for example, improvements to the swing angle or the gripping method of the bat, but is not limited to such examples. For example, the providing unit indicates improvements to the swing angle. The providing unit can also indicate improvements to the gripping method of the bat. The providing unit can also suggest specific training methods, such as strength training or improving the swing form. For example, the providing unit indicates a specific method for improving the swing angle. The providing unit indicates a specific method for improving the gripping method of the bat. The providing unit suggests a specific method for strength training. In this way, the performance analysis system according to the embodiment can automatically analyze children's performance and provide effective improvement measures.Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide improvements using an AI model that receives the analysis results obtained by the analysis unit as input and outputs improvements.

[0063] The collection unit can collect data on the swing trajectory, bat speed, and ball flight distance. The collection unit, for example, measures the swing trajectory with a sensor and collects the data. For example, the collection unit measures the swing trajectory with a high-precision sensor and collects the data. The collection unit can also measure the bat speed with a velocity sensor and collect the data. For example, the collection unit measures the bat speed in real time and collects the data. The collection unit can also measure the ball flight distance with a distance sensor and collect the data. For example, the collection unit accurately measures the ball flight distance and collects the data. This allows the collection unit to collect detailed performance data of children currently training. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data measured by the sensor to a generation AI and have the generation AI analyze the data.

[0064] The analysis unit can analyze the collected data and quantify the swing accuracy and bat speed. For example, the analysis unit can analyze the collected data using statistical analysis to quantify the swing accuracy. For example, the analysis unit can analyze data on the swing motion trajectory and evaluate the swing accuracy. The analysis unit can also analyze the collected data using a machine learning algorithm to quantify the bat speed. For example, the analysis unit can analyze data on the bat speed and quantify the speed. The analysis unit can also analyze the collected data using deep learning to quantify the flight distance of the hit ball. For example, the analysis unit can analyze data on the flight distance of the hit ball and quantify the flight distance. In this way, the analysis unit can analyze the collected data and quantify a performance indicator. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0065] The providing unit can indicate areas for improvement in the swing angle or the gripping of the bat based on the analysis results. The providing unit, for example, indicates areas for improvement in the swing angle based on the analysis results. For example, the providing unit indicates a specific method for improving the swing angle. The providing unit can also indicate areas for improvement in the gripping of the bat based on the analysis results. For example, the providing unit indicates a specific method for improving the gripping of the bat. This allows the providing unit to indicate specific areas for improvement based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the analysis results into a generating AI and cause the generating AI to suggest areas for improvement.

[0066] The providing unit can propose a specific training method for strength training or swing form improvement based on the analysis results. The providing unit, for example, proposes a specific strength training method based on the analysis results. For example, the providing unit indicates the type, number of repetitions, load, etc. of strength training. The providing unit can also propose a specific method for improving swing form based on the analysis results. For example, the providing unit indicates checkpoints and improvement procedures for swing form. This allows the providing unit to propose a specific training method based on the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results into a generating AI and have the generating AI execute the training method proposal.

[0067] The collection unit periodically collects data and compares it with past data, thereby grasping the degree of growth or progress of improvement. The collection unit, for example, periodically collects data and compares it with past data. For example, the collection unit may collect data weekly and compare it with past data. The collection unit may also collect data monthly and compare it with past data. This allows the collection unit to grasp the degree of growth or progress of improvement. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the periodically collected data into the generation AI and cause the generation AI to grasp the degree of growth or progress of improvement.

[0068] The collection unit can estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, the collection unit collects data at the start of training when the child is relaxed. For example, the collection unit collects data at the start of training when the child is relaxed. The collection unit can also collect data in the middle of training when the child is concentrating. For example, the collection unit collects data in the middle of training when the child is concentrating. The collection unit can also collect data at the end of training when the child is tired. For example, the collection unit collects data at the end of training when the child is tired. This allows the collection unit to adjust the timing of data collection based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input a child's emotional data into the generation AI and have the generation AI adjust the timing of data collection.

[0069] The collection unit can collect data based on environmental conditions during training. For example, in a hot and humid environment, the collection unit collects data for a short period of time, taking into account the child's physical condition. For example, in a hot and humid environment, the collection unit collects data for a short period of time, taking into account the child's physical condition. The collection unit can also collect data after a warm-up in a low-temperature environment. For example, the collection unit collects data after a warm-up in a low-temperature environment. The collection unit can also collect indoor training data in rainy weather. For example, the collection unit collects indoor training data in rainy weather. In this way, the collection unit can collect more accurate data by collecting data taking into account the environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input environmental condition data to a generation AI and cause the generation AI to adjust the data collection.

[0070] The collection unit can monitor the child's physical condition and fatigue level and select an appropriate collection timing. The collection unit, for example, monitors the child's heart rate and collects data when it is stable. For example, the collection unit monitors the child's heart rate and collects data when it is stable. The collection unit can also monitor the child's fatigue level and collect data when the child is less tired. For example, the collection unit monitors the child's fatigue level and collects data when the child is less tired. The collection unit can also monitor the child's body temperature and collect data when it is within a normal range. For example, the collection unit monitors the child's body temperature and collects data when it is within a normal range. This allows the collection unit to select the optimal collection timing taking the child's physical condition and fatigue level into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's physical condition data to the generation AI and cause the generation AI to select the collection timing.

[0071] The collection unit can collect data from multiple angles by combining multiple sensors. For example, the collection unit can collect a swing motion trajectory by combining an acceleration sensor and a gyro sensor. For example, the collection unit can collect a swing motion trajectory by combining an acceleration sensor and a gyro sensor. The collection unit can also monitor a physical condition during training by combining a heart rate sensor and an acceleration sensor. For example, the collection unit can monitor a physical condition during training by combining a heart rate sensor and an acceleration sensor. The collection unit can also collect a bat speed and a child's physical condition simultaneously by combining a gyro sensor and a heart rate sensor. For example, the collection unit can collect a bat speed and a child's physical condition simultaneously by combining a gyro sensor and a heart rate sensor. In this way, the collection unit can obtain more detailed data by collecting data from multiple angles by combining different sensors. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data collected by multiple sensors into a generation AI and have the generation AI analyze the data.

[0072] The collection unit can estimate the child's emotions and determine the priority of data to be collected based on the estimated child's emotions. For example, when the child is excited, the collection unit prioritizes collecting the swing motion trajectory. For example, when the child is excited, the collection unit prioritizes collecting the swing motion trajectory. The collection unit can also prioritize collecting the bat speed when the child is relaxed. For example, when the child is relaxed, the collection unit prioritizes collecting the bat speed. The collection unit can also prioritize collecting the ball distance when the child is concentrating. For example, when the child is concentrating, the collection unit prioritizes collecting the ball distance. This allows the collection unit to determine the priority of data to be collected based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input children's emotional data into the generation AI and have the generation AI determine the priority of the data.

[0073] When collecting data, the collection unit can customize the collection method based on the child's past training data. For example, the collection unit may focus on collecting a specific swing pattern from the past data. For example, the collection unit may focus on collecting a specific swing pattern from the past data. The collection unit may also employ a collection method specialized for a slow bat speed from the past data. For example, the collection unit may employ a collection method specialized for a slow bat speed from the past data. The collection unit may also employ a collection method specialized for a short ball flight distance from the past data. For example, the collection unit employs a collection method specialized for a short ball flight distance from the past data. This allows the collection unit to customize the collection method by referring to the past training data, enabling more effective data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input past training data into the generation AI and cause the generation AI to customize the collection method.

[0074] When collecting data, the collection unit can select the type of data to collect based on the child's training goal. For example, if the goal is to improve swing accuracy, the collection unit can focus on collecting the swing motion trajectory. For example, if the goal is to improve swing accuracy, the collection unit can focus on collecting the swing motion trajectory. Furthermore, if the goal is to improve bat speed, the collection unit can also focus on collecting the bat speed. For example, if the goal is to improve bat speed, the collection unit can focus on collecting the bat speed. Furthermore, if the goal is to improve the distance of a hit ball, the collection unit can also focus on collecting the distance of a hit ball. For example, if the goal is to improve the distance of a hit ball, the collection unit can focus on collecting the distance of a hit ball. In this way, by selecting the type of data to collect based on the training goal, effective data collection toward achieving the goal is possible. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input a training goal to the generation AI and cause the generation AI to select the collected data.

[0075] The collection unit can adjust the collection method by reflecting feedback from the child's training partner or coach when collecting data. For example, the collection unit adjusts the collection method for the swing motion trajectory based on the coach's feedback. For example, the collection unit adjusts the collection method for the swing motion trajectory based on the coach's feedback. The collection unit can also adjust the collection method for the bat speed based on the training partner's feedback. For example, the collection unit adjusts the collection method for the bat speed based on the training partner's feedback. The collection unit can also adjust the collection method for the hit ball distance by combining the feedback from the coach and the training partner. For example, the collection unit adjusts the collection method for the hit ball distance by combining the feedback from the coach and the training partner. This allows the collection unit to adjust the collection method by reflecting the feedback from the training partner or coach, thereby enabling more effective data collection. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit may input feedback data to a generation AI and cause the generation AI to adjust the collection method.

[0076] The analysis unit can estimate the child's emotions and adjust the analysis algorithm based on the estimated child's emotions. For example, when the child is relaxed, the analysis unit uses an algorithm that performs a detailed analysis. For example, when the child is relaxed, the analysis unit uses an algorithm that performs a detailed analysis. The analysis unit can also use an algorithm that performs a quick analysis when the child is excited. For example, when the child is excited, the analysis unit uses an algorithm that performs a quick analysis. The analysis unit can also use an algorithm that performs a simplified analysis when the child is tired. For example, when the child is tired, the analysis unit uses an algorithm that performs a simplified analysis. This allows the analysis unit to adjust the analysis algorithm based on the child's emotions, enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input a child's emotional data into the generation AI and have the generation AI adjust the analysis algorithm.

[0077] During analysis, the analysis unit can correct the analysis results based on the environmental conditions during training. The analysis unit, for example, corrects and analyzes data in a hot and humid environment. For example, the analysis unit corrects and analyzes data in a hot and humid environment. The analysis unit can also correct and analyze data in a low temperature environment. For example, the analysis unit corrects and analyzes data in a low temperature environment. The analysis unit can also correct and analyze data in rainy weather. For example, the analysis unit corrects and analyzes data in rainy weather. In this way, the analysis unit can correct the analysis results taking into account the environmental conditions, thereby enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input environmental condition data to the generation AI and cause the generation AI to correct the analysis results.

[0078] During analysis, the analysis unit can correct the analysis results based on the child's physical condition and fatigue level. The analysis unit corrects the analysis results, for example, taking into account the child's heart rate. For example, the analysis unit corrects the analysis results taking into account the child's heart rate. The analysis unit can also correct the analysis results taking into account the child's fatigue level. For example, the analysis unit corrects the analysis results taking into account the child's fatigue level. The analysis unit can also correct the analysis results taking into account the child's body temperature. For example, the analysis unit corrects the analysis results taking into account the child's body temperature. This allows the analysis unit to correct the analysis results taking into account the child's physical condition and fatigue level, thereby enabling more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the child's physical condition data into the generation AI and cause the generation AI to correct the analysis results.

[0079] The analysis unit can improve the accuracy of the analysis by combining multiple analysis methods during analysis. The analysis unit, for example, combines machine learning and deep learning to analyze the swing motion trajectory. For example, the analysis unit combines machine learning and deep learning to analyze the swing motion trajectory. The analysis unit can also analyze the bat speed by combining deep learning and statistical analysis. For example, the analysis unit combines deep learning and statistical analysis to analyze the bat speed. The analysis unit can also analyze the flight distance of a batted ball by combining machine learning and statistical analysis. For example, the analysis unit combines machine learning and statistical analysis to analyze the flight distance of a batted ball. This allows the analysis unit to improve the accuracy of the analysis by combining different analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input multiple analysis methods into the generation AI and cause the generation AI to improve the analysis accuracy.

[0080] The analysis unit can estimate the child's emotion and adjust the display method of the analysis results based on the estimated emotion. For example, when the child is relaxed, the analysis unit displays detailed analysis results. For example, when the child is relaxed, the analysis unit displays detailed analysis results. The analysis unit can also display concise analysis results when the child is excited. For example, when the child is excited, the analysis unit displays concise analysis results. The analysis unit can also display visually easy-to-understand analysis results when the child is tired. For example, when the child is tired, the analysis unit displays visually easy-to-understand analysis results. This allows the analysis unit to adjust the display method of the analysis results based on the child's emotion, thereby enabling more appropriate display. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input a child's emotional data into the generation AI and have the generation AI adjust the way the analysis results are displayed.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis based on the child's past training data. The analysis unit, for example, improves the analysis accuracy of the swing motion trajectory based on past data. For example, the analysis unit improves the analysis accuracy of the swing motion trajectory based on past data. The analysis unit can also improve the analysis accuracy of the bat speed based on past data. For example, the analysis unit improves the analysis accuracy of the bat speed based on past data. The analysis unit can also improve the analysis accuracy of the ball's flight distance based on past data. For example, the analysis unit improves the analysis accuracy of the ball's flight distance based on past data. In this way, the analysis unit can improve the analysis accuracy by referring to past training data, thereby enabling more accurate analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input past training data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0082] During analysis, the analysis unit can customize the analysis results based on the child's training goals. For example, if the goal is to improve swing accuracy, the analysis unit customizes the analysis results of the swing motion trajectory. For example, if the goal is to improve swing accuracy, the analysis unit customizes the analysis results of the swing motion trajectory. The analysis unit can also customize the analysis results of the bat speed if the goal is to improve bat speed. For example, if the goal is to improve bat speed, the analysis unit customizes the analysis results of the bat speed. The analysis unit can also customize the analysis results of the bat distance if the goal is to improve the bat distance. For example, if the goal is to improve the bat distance, the analysis unit customizes the analysis results of the bat distance. In this way, the analysis unit customizes the analysis results based on the training goals, enabling effective analysis toward goal achievement. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input training goals into the generation AI and cause the generation AI to customize the analysis results.

[0083] During analysis, the analysis unit can adjust the analysis method by reflecting feedback from the child's training partner or coach. The analysis unit, for example, adjusts the analysis method of the swing motion trajectory based on feedback from the coach. For example, the analysis unit adjusts the analysis method of the swing motion trajectory based on feedback from the coach. The analysis unit can also adjust the analysis method of the bat speed based on feedback from the training partner. For example, the analysis unit adjusts the analysis method of the bat speed based on feedback from the training partner. The analysis unit can also adjust the analysis method of the hit ball distance by combining feedback from the coach and the training partner. For example, the analysis unit adjusts the analysis method of the hit ball distance by combining feedback from the coach and the training partner. This enables the analysis unit to adjust the analysis method by reflecting feedback from the training partner or coach, thereby enabling more effective analysis. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit may input feedback data to a generation AI and cause the generation AI to adjust the analysis method.

[0084] The providing unit can estimate the child's emotions and adjust the method of presenting the improvement points based on the estimated child's emotions. For example, when the child is relaxed, the providing unit presents detailed improvement points. For example, when the child is relaxed, the providing unit presents detailed improvement points. Furthermore, when the child is excited, the providing unit can present concise improvement points. For example, when the child is excited, the providing unit presents concise improvement points. Furthermore, when the child is tired, the providing unit can present visually easy-to-understand improvement points. For example, when the child is tired, the providing unit presents visually easy-to-understand improvement points. In this way, the providing unit can present more appropriate improvement points by adjusting the method of presenting the improvement points based on the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the child's emotional data into the generating AI and have the generating AI adjust the way in which improvements are presented.

[0085] The providing unit can suggest improvements based on the environmental conditions during training when providing the training data. The providing unit, for example, suggests improvements for training in a hot and humid environment. For example, the providing unit suggests improvements for training in a hot and humid environment. The providing unit can also suggest improvements for training in a low temperature environment. For example, the providing unit suggests improvements for training in a low temperature environment. The providing unit can also suggest improvements for training in rainy weather. For example, the providing unit suggests improvements for training in rainy weather. This allows the providing unit to suggest improvements taking into account the environmental conditions during training, thereby enabling more effective improvement. Some or all of the above-mentioned processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input environmental condition data to a generating AI and cause the generating AI to suggest improvements.

[0086] The providing unit can suggest points for improvement based on the child's physical condition and fatigue level at the time of providing the data. The providing unit, for example, suggests points for improvement taking into account the child's heart rate. For example, the providing unit suggests points for improvement taking into account the child's heart rate. The providing unit can also suggest points for improvement taking into account the child's fatigue level. For example, the providing unit suggests points for improvement taking into account the child's fatigue level. The providing unit can also suggest points for improvement taking into account the child's body temperature. For example, the providing unit suggests points for improvement taking into account the child's body temperature. This allows the providing unit to suggest points for improvement taking into account the child's physical condition and fatigue level, thereby enabling more appropriate improvements. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input data on the child's physical condition into the generating AI and cause the generating AI to suggest points for improvement.

[0087] The providing unit can effectively present improvements by combining multiple presentation methods during provision. The providing unit, for example, presents improvements by combining text and visuals. For example, the providing unit presents improvements by combining text and visuals. The providing unit can also present improvements by combining audio and visuals. For example, the providing unit presents improvements by combining audio and visuals. The providing unit can also present improvements by combining text, audio, and visuals. For example, the providing unit presents improvements by combining text, audio, and visuals. This enables the providing unit to present improvements more effectively by combining different presentation methods. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input presentation method data into a generation AI and cause the generation AI to present improvements.

[0088] The providing unit can estimate the child's emotions and prioritize improvements based on the estimated emotions. For example, when the child is relaxed, the providing unit prioritizes presenting detailed improvements. For example, when the child is relaxed, the providing unit prioritizes presenting detailed improvements. Furthermore, when the child is excited, the providing unit can prioritize presenting concise improvements. For example, when the child is excited, the providing unit prioritizes presenting concise improvements. Furthermore, when the child is tired, the providing unit can prioritize presenting visually easy-to-understand improvements. For example, when the child is tired, the providing unit prioritizes presenting visually easy-to-understand improvements. In this way, the providing unit prioritizes the improvements based on the child's emotions, enabling more appropriate improvements. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input a child's emotional data into the generating AI and have the generating AI determine the priorities of areas for improvement.

[0089] The providing unit can customize the points for improvement based on the child's past training data when providing the data. The providing unit, for example, customizes the points for improvement of the swing motion trajectory based on past data. For example, the providing unit customizes the points for improvement of the swing motion trajectory based on past data. The providing unit can also customize the points for improvement of the bat speed based on past data. For example, the providing unit customizes the points for improvement of the bat speed based on past data. The providing unit can also customize the points for improvement of the ball's flight distance based on past data. For example, the providing unit customizes the points for improvement of the ball's flight distance based on past data. In this way, the providing unit can customize the points for improvement by referring to past training data, thereby enabling more effective improvement. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input past training data into the generating AI and cause the generating AI to customize the points for improvement.

[0090] The providing unit can suggest improvements based on the child's training goal during the provision. For example, if the goal is to improve swing accuracy, the providing unit suggests improvements to the swing motion trajectory. For example, if the goal is to improve swing accuracy, the providing unit suggests improvements to the swing motion trajectory. Furthermore, if the goal is to improve bat speed, the providing unit can also suggest improvements to bat speed. For example, if the goal is to improve bat speed, the providing unit suggests improvements to bat speed. Furthermore, if the goal is to improve the distance of a hit ball, the providing unit can also suggest improvements to the distance of a hit ball. For example, if the goal is to improve the distance of a hit ball, the providing unit suggests improvements to the distance of a hit ball. In this way, the providing unit suggests improvements based on the training goal, enabling effective improvement toward achieving the goal. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit may input the training goal into a generating AI and cause the generating AI to suggest improvements.

[0091] The providing unit can adjust points for improvement by reflecting feedback from the child's training partner or coach when providing the data. The providing unit, for example, adjusts points for improvement in the swing motion trajectory based on feedback from the coach. For example, the providing unit adjusts points for improvement in the swing motion trajectory based on feedback from the coach. The providing unit can also adjust points for improvement in bat speed based on feedback from the training partner. For example, the providing unit adjusts points for improvement in bat speed based on feedback from the training partner. The providing unit can also adjust points for improvement in the distance of the hit ball by combining feedback from the coach and the training partner. For example, the providing unit adjusts points for improvement in the distance of the hit ball by combining feedback from the coach and the training partner. This allows the providing unit to adjust points for improvement by reflecting feedback from the training partner or coach, enabling more effective improvement. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input feedback data to a generating AI and cause the generating AI to adjust points for improvement. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects performance data of children during training using a sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and quantifies performance indicators. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the analysis results to the children, coaching staff, and parents. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects performance data of children during training using a sensor in the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected data and quantifies performance indicators. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the analysis results to the children, coaching staff, and parents. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects performance data of the children during training using a sensor in the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected data and quantifies performance indicators. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the analysis results to the children, coaching staff, and parents. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects performance data of the children during training using a sensor in the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected data and quantifies performance indicators. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the analysis results to the children, coaching staff, and parents.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the child's emotions and adjust the feedback of the analysis results based on the estimated emotions. For example, if the child is relaxed, a detailed analysis result can be provided. If the child is excited, a concise analysis result can be provided. Furthermore, if the child is tired, a visually easy-to-understand analysis result can be provided. This allows the analysis unit to provide more appropriate feedback by adjusting the feedback of the analysis results based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the feedback of the analysis results.

[0094] The providing unit can estimate the child's emotions based on the analysis results and provide feedback to improve the child's motivation for training based on the estimated emotions. For example, if the child is relaxed, positive feedback can be emphasized. If the child is excited, a challenging goal can be presented. Also, if the child is tired, words of encouragement can be provided. In this way, the providing unit can provide feedback to improve the child's motivation for training based on the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to provide feedback.

[0095] The collection unit can estimate the child's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the child is relaxed, the frequency of data collection can be increased. If the child is excited, the frequency of data collection can be decreased. Also, if the child is tired, the frequency of data collection can be temporarily suspended. This allows the collection unit to adjust the frequency of data collection based on the child's emotions, thereby enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the child's emotion data into the generation AI and have the generation AI adjust the frequency of data collection.

[0096] The providing unit can estimate the child's emotions based on the analysis results and adjust the method of reporting the training progress based on the estimated emotions. For example, if the child is relaxed, a detailed progress report can be provided. If the child is excited, a concise progress report can be provided. Furthermore, if the child is tired, a visually easy-to-understand progress report can be provided. This allows the providing unit to adjust the method of reporting the training progress based on the child's emotions, thereby providing a more appropriate report. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the method of reporting the progress.

[0097] The analysis unit can estimate the child's emotions and provide guidelines to assist in interpreting the analysis results based on the estimated emotions. For example, if the child is relaxed, detailed interpretation guidelines can be provided. If the child is excited, concise interpretation guidelines can be provided. Furthermore, if the child is tired, visually easy-to-understand interpretation guidelines can be provided. Thus, the analysis unit can provide guidelines to assist in interpreting the analysis results based on the child's emotions, thereby supporting more appropriate interpretation. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to provide interpretation guidelines.

[0098] The collection unit can adjust the accuracy of data collection based on the environmental conditions during training. For example, in a hot and humid environment, the sensitivity of the sensor can be adjusted to improve the accuracy of data collection. In a low-temperature environment, the operation of the sensor can be optimized to maintain the accuracy of data collection. Furthermore, in rainy weather, the waterproof performance of the sensor can be utilized to collect data. In this way, the collection unit can collect more accurate data by adjusting the accuracy of data collection based on the environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental condition data to the generation AI and cause the generation AI to adjust the accuracy of data collection.

[0099] During analysis, the analysis unit can select an analysis algorithm based on the environmental conditions during training. For example, in a hot and humid environment, a specific algorithm can be used to analyze data. In a low-temperature environment, a different algorithm can be used to analyze data. Furthermore, in rainy weather, data can be analyzed using an algorithm suitable for specific conditions. In this way, the analysis unit can perform more accurate analysis by selecting an analysis algorithm based on the environmental conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental condition data to the generation AI and have the generation AI select an analysis algorithm.

[0100] The providing unit can suggest a training method according to the environmental conditions during training based on the analysis results. For example, in a hot and humid environment, it can suggest increasing the frequency of hydration. In a low-temperature environment, it can suggest extending the warm-up time. Furthermore, in rainy weather, it can suggest indoor training methods. In this way, the providing unit can support more effective training by suggesting training methods according to environmental conditions. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input environmental condition data into a generating AI and cause the generating AI to suggest a training method.

[0101] The collection unit can monitor the child's physical condition and fatigue level and select an appropriate collection timing. For example, the collection unit can monitor the child's heart rate and collect data when it is stable. The child's fatigue level can also be monitored and collected data when fatigue is low. The collection unit can also monitor the child's body temperature and collect data when it is within a normal range. This allows the collection unit to select the optimal collection timing taking into account the child's physical condition and fatigue level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's physical condition data into the generation AI and have the generation AI select the collection timing.

[0102] During analysis, the analysis unit can improve the accuracy of the analysis by combining multiple analysis methods. For example, machine learning and deep learning can be combined to analyze the swing motion trajectory. Deep learning and statistical analysis can also be combined to analyze the bat speed. Furthermore, machine learning and statistical analysis can be combined to analyze the flight distance of the hit ball. This allows the analysis unit to improve the accuracy of the analysis by combining different analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input multiple analysis methods into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects performance data from children during training. Performance data includes the swing trajectory, bat speed, and ball distance. The collection unit collects this data using sensors. For example, a high-precision sensor measures the swing trajectory, a speed sensor measures the bat speed in real time, and a distance sensor accurately measures the ball distance. Step 2: The analysis unit analyzes the data collected by the collection unit and quantifies performance indicators. The analysis is performed using statistical analysis, machine learning algorithms, and deep learning. For example, statistical analysis is used to quantify swing accuracy, machine learning algorithms are used to quantify bat speed, and deep learning is used to quantify ball distance. Step 3: The providing unit provides the analysis results obtained by the analyzing unit. Providing the results includes suggesting areas for improvement, such as the swing angle or the grip of the bat, and suggesting specific training methods, such as strength training or improving swing form. For example, the providing unit suggests specific methods for improving the swing angle, the grip of the bat, and strength training.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 collection department that collects performance data of children during training; an analysis unit that analyzes the data collected by the collection unit and quantifies performance indicators; a providing unit that provides the analysis results obtained by the analyzing unit. A system characterized by:

2. The collecting unit Collect data on swing trajectory, bat speed, and ball distance 2. The system of claim 1.

3. The analysis unit Analyze the collected data and quantify swing accuracy and bat speed 2. The system of claim 1.

4. The providing unit Based on the analysis results, it suggests areas for improvement in swing angle or bat grip.

2. The system of claim 1.

5. The providing unit Based on the analysis results, we will suggest specific training methods for strength training or improving swing form.

2. The system of claim 1.

6. The collecting unit Collect data periodically and compare it with past data to understand progress or improvement.

2. The system of claim 1.

7. The collecting unit Estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions.

2. The system of claim 1.

8. The collecting unit Data collection based on environmental conditions during training 2. The system of claim 1.

9. The collecting unit Monitor the child's physical condition or fatigue level and select the appropriate collection time.

2. The system of claim 1.

10. The collecting unit Combining multiple sensors to collect data from multiple angles 2. The system of claim 1.

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