system

Abstract

The system according to this embodiment aims to analyze the player's performance in detail and provide appropriate feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects real-time data of the players. The analysis unit analyzes the data collected by the collection unit. The provision unit provides feedback based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in sports guidance by sports instructors lacking or by teachers without competitive experience, there is a problem that it is difficult to analyze the performance of athletes in detail and provide appropriate feedback.

[0005] The system according to the embodiment aims to analyze the performance of athletes in detail and provide appropriate feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects real-time data of players. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides feedback based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the player's performance in detail and provide appropriate feedback. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The sports coaching system according to an embodiment of the present invention is a system that utilizes generative AI to analyze athletes' performance in detail and provide an optimal training plan. This system aims to solve the problems of a shortage of sports coaches, particularly in junior and senior high schools, and the challenges of sports coaching by teachers without competitive experience. The system uses a GPS tracker-integrated chest strap worn by the athlete to collect real-time data such as heart rate and movement. This allows the system to acquire and visualize the athlete's movement data and display it on a dashboard. Furthermore, it outputs the performance of each athlete in combination with the heart rate sensor. Next, the generative AI quantitatively evaluates training and game performance and provides feedback to the athlete and coach. This makes it possible to identify the athlete's strengths and areas that need improvement and generate an individualized training plan. For example, it can analyze athlete performance in detail and provide an optimal training plan for outdoor sports such as soccer, rugby, tennis, American football, and baseball. This system reduces the burden on coaches and enables high-quality sports coaching. In addition, athletes can track their own progress in real time and make adjustments independently to achieve their goals. This is expected to effectively support sports coaching in educational settings and raise the overall ability of athletes in Japan. This allows sports coaching systems to analyze athletes' performance in detail and provide optimal training plans.

[0029] The sports coaching system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects real-time data of the athlete. The data collection unit can collect real-time data such as heart rate and movement using, for example, a GPS tracker-integrated chest strap worn by the athlete. The data collection unit can measure the athlete's heart rate using, for example, a heart rate sensor and collect the data in real time. The data collection unit can also use an accelerometer to detect the athlete's movement. For example, the data collection unit can acquire the athlete's movement data, visualize it, and display it on a dashboard. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, statistically analyze the collected data and evaluate the athlete's performance. The analysis unit can, for example, analyze the data using a machine learning algorithm to identify the athlete's strengths and areas that need improvement. The analysis unit can also analyze the data using generative AI to quantitatively evaluate the athlete's performance. The data provision unit provides feedback based on the analysis results obtained by the analysis unit. The data provision unit can, for example, propose a training plan based on the analysis results. The data provision unit can, for example, report on the athlete's performance evaluation and point out areas for improvement. Furthermore, the data provider can generate individual training plans based on the analysis results and provide them to athletes and coaches. This allows the sports coaching system according to this embodiment to collect, analyze, and provide feedback on athletes' real-time data, thereby offering optimal training plans.

[0030] The data collection unit collects real-time data from athletes. For example, it can collect real-time data such as heart rate and movement using a GPS tracker-integrated chest strap worn by the athlete. Specifically, a heart rate sensor accurately measures the athlete's heart rate and collects the data in real time. This allows for immediate understanding of the athlete's exercise intensity and fatigue level. Additionally, an accelerometer detects the athlete's movement in three dimensions, providing detailed data such as speed, direction, and acceleration. This enables analysis of the athlete's movement patterns and form. Furthermore, the data collection unit centrally manages this data and transmits it to a cloud server, allowing for real-time data sharing. For example, athlete heart rate and movement data can be visualized on a dedicated dashboard, allowing coaches and trainers to instantly review it. This provides the data collection unit with a foundation for real-time monitoring of athlete performance and for adjusting appropriate guidance and training. The data collection unit can also perform regular calibration and maintenance to ensure data accuracy and reliability. This allows the data collection unit to collect athlete performance data with high accuracy and improve the overall system reliability.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can statistically analyze the collected data to evaluate the athlete's performance. Specifically, it uses machine learning algorithms to analyze the data and identify the athlete's strengths and areas for improvement. For example, by analyzing the athlete's heart rate data and analyzing the heart rate variability pattern during exercise, it can evaluate the athlete's endurance and explosive power. It can also analyze movement data obtained from acceleration sensors to evaluate the athlete's form and movement efficiency. Furthermore, the analysis unit can use generative AI to analyze the data and quantitatively evaluate the athlete's performance. The generative AI predicts the athlete's performance based on a large amount of data and proposes an optimal training plan. For example, the generative AI can analyze trends in an athlete's performance based on past training data and match data and predict future performance improvements. This allows the analysis unit to evaluate the athlete's performance from multiple angles and identify specific areas for improvement. Furthermore, the analysis unit visualizes the analysis results so that coaches and trainers can easily understand them. For example, graphs and charts are used to visually show fluctuations in the athlete's performance, making it easier to understand intuitively. This allows the analysis unit to evaluate the players' performance with high accuracy and provide specific areas for improvement.

[0032] The service provider provides feedback based on the analysis results obtained by the analysis provider. Specifically, they can propose training plans based on the analysis results. For example, based on the athlete's heart rate and movement data, they can propose running plans to improve endurance or sprint training to improve explosive power. The service provider also reports on the athlete's performance evaluation and points out areas for improvement. For example, by evaluating the athlete's form and movement efficiency and indicating specific areas for improvement, the athlete can take concrete actions to improve their performance. Furthermore, the service provider can generate individual training plans based on the analysis results and provide them to athletes and coaches. For example, they can create training plans that take into account the athlete's strengths and weaknesses and provide guidelines for the athlete to train efficiently. In this way, the service provider can provide specific feedback to improve the athlete's performance and propose the optimal training plan. Furthermore, the service provider can continuously monitor the effects of the feedback and modify the training plan as needed. For example, by regularly collecting athlete performance data and evaluating the effectiveness of the training plan, they can provide a more effective training plan. In this way, the service provider can provide support to continuously improve the athlete's performance.

[0033] The data collection unit can collect real-time data such as heart rate and movement using a GPS tracker-integrated chest strap worn by the athlete. For example, the data collection unit can measure heart rate using the GPS tracker-integrated chest strap worn by the athlete and collect data in real time. The data collection unit can also use an accelerometer to detect the athlete's movement. For example, the data collection unit can acquire the athlete's movement data, visualize it, and display it on a dashboard. This allows for the accurate collection of real-time data such as the athlete's heart rate and movement. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data acquired from the GPS tracker-integrated chest strap worn by the athlete into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can analyze the collected data and evaluate the players' performance. For example, the analysis unit can statistically analyze the collected data to evaluate the players' performance. The analysis unit can also use machine learning algorithms to analyze the data and identify the players' strengths and areas for improvement. For example, the analysis unit can use generative AI to analyze the data and quantitatively evaluate the players' performance. This allows for an accurate evaluation of the players' performance by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0035] The service provider can provide feedback to athletes and coaches based on the analysis results. For example, the service provider can propose training plans based on the analysis results. The service provider can also report on the athletes' performance evaluations and point out areas for improvement. For example, the service provider can generate individual training plans based on the analysis results and provide them to athletes and coaches. This allows athletes and coaches to conduct appropriate training by providing feedback based on the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI perform the generation of feedback.

[0036] The service provider can identify an athlete's strengths and areas for improvement, and generate an individualized training plan. For example, the service provider can identify an athlete's strengths and areas for improvement based on analysis results. The service provider can also generate an individualized training plan and provide it to the athlete or coach. For example, the service provider can analyze an athlete's performance data, identify their strengths and areas for improvement, and create a training plan based on that. This allows for improvement in athlete performance by identifying an athlete's strengths and areas for improvement and providing an individualized training plan. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input analysis results into a generating AI and have the generating AI generate a training plan.

[0037] The service provider enables athletes to track their progress in real time and make adjustments independently to achieve their goals. For example, the service provider collects athlete progress data in real time and displays it on a dashboard. The service provider can also analyze athlete progress data and adjust training plans to achieve goals. For example, the service provider updates training plans based on athlete progress data and provides feedback to the athlete. This allows athletes to track their progress in real time and make adjustments independently to achieve their goals. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input athlete progress data into a generating AI and have the generating AI perform adjustments to the training plan.

[0038] The data collection unit can analyze a player's past performance data and select the optimal data collection method. For example, the data collection unit can identify the peak performance time of a player from past data and concentrate data collection during that time. The data collection unit can also identify a player's weaknesses from past data and focus data collection on those areas. For example, the data collection unit can analyze a player's growth pattern from past data and select a data collection method to promote growth. In this way, the optimal data collection method can be selected by analyzing past performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the player's current physical condition and environmental conditions during data collection. For example, if a player is unwell, the data collection unit can temporarily suspend data collection and wait until their condition improves. Similarly, if environmental conditions are unfavorable (e.g., high temperature and humidity), the data collection unit can refrain from collecting data and wait until appropriate environmental conditions are met. For example, the data collection unit can adjust the type and frequency of data collected based on the player's physical condition and environmental conditions. By adjusting data collection based on the player's physical condition and environmental conditions, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input player physical condition data and environmental condition data into a generating AI and have the generating AI perform the data collection filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the player's geographical location during data collection. For example, if a player is in a specific training area, the data collection unit will collect data specific to that area. Furthermore, if a player is in a match, the data collection unit can prioritize the collection of match-related data. For example, if a player is traveling, the data collection unit will collect travel-related data and monitor performance fluctuations. This allows for the priority collection of highly relevant data by considering the player's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the player's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0041] The data collection unit can analyze the player's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on training content shared by the player on social media. The data collection unit can also adjust the timing of data collection based on the player's physical condition or mood mentioned on social media. For example, the data collection unit can estimate the player's training motivation from their social media activity and determine the priority of data collection. This allows for the collection of relevant data by analyzing the player's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the player's social media data into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data (e.g., heart rate) to improve performance. It can also perform a simplified analysis on less important data (e.g., distance traveled). For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data to evaluate stress levels. The analysis unit can also apply a tracking algorithm to movement data to analyze movement patterns. For example, the analysis unit can apply a motion analysis algorithm to movement data to improve performance. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also evaluate current performance by referring to past data. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. This enables real-time feedback by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and perform an overall performance evaluation. Alternatively, the analysis unit can postpone the analysis of less relevant data and focus on analyzing important data. For example, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The service provider can analyze a player's past performance during feedback to select the optimal feedback method. For example, the service provider can provide feedback that highlights a player's strengths based on past performance data. It can also provide feedback to improve a player's weaknesses based on past performance data. For example, the service provider can provide feedback to promote a player's growth based on past performance data. This allows the service provider to select the optimal feedback method by analyzing past performance. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input past performance data into a generating AI and have the generating AI select the optimal feedback method.

[0047] The feedback system can customize the content of feedback based on the player's current physical condition and environmental conditions. For example, if a player is unwell, the system will provide feedback that takes their physical condition into account. Similarly, if environmental conditions are unfavorable (e.g., high temperature and humidity), the system can provide feedback that takes these conditions into account. For instance, the system adjusts the content of the feedback based on the player's physical condition and environmental conditions. This allows for the provision of more appropriate feedback by adjusting the content based on the player's physical condition and environmental conditions. Some or all of the above-described processes in the feedback system may be performed using AI, or not. For example, the system can input player physical condition data and environmental condition data into a generating AI and have the generating AI customize the content of the feedback.

[0048] The service provider can select the optimal feedback method by considering the player's geographical location information during feedback. For example, if the player is in a specific training area, the service provider can provide feedback tailored to that area. Furthermore, if the player is in a match, the service provider can prioritize providing match-related feedback. For example, if the player is traveling, the service provider can provide travel-related feedback and monitor performance fluctuations. This allows the service provider to select the optimal feedback method by considering the player's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0049] The service provider can analyze the athlete's social media activity and suggest content for feedback. For example, the service provider can provide relevant feedback based on training content shared by the athlete on social media. The service provider can also adjust the content of the feedback based on the athlete's physical condition or mood mentioned on social media. For example, the service provider can estimate the athlete's training motivation from their social media activity and suggest content for feedback. In this way, relevant feedback can be provided by analyzing the athlete's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the athlete's social media data into a generating AI and have the generating AI suggest content for the feedback.

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

[0051] The data collection unit can analyze a player's past performance data and select the optimal data collection method. For example, the data collection unit can identify the peak performance time of a player from past data and concentrate data collection during that time. The data collection unit can also identify a player's weaknesses from past data and focus data collection on those areas. For example, the data collection unit can analyze a player's growth pattern from past data and select a data collection method to promote growth. In this way, the optimal data collection method can be selected by analyzing past performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data (e.g., heart rate) to improve performance. It can also perform a simplified analysis on less important data (e.g., distance traveled). For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0053] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data to evaluate stress levels. The analysis unit can also apply a tracking algorithm to movement data to analyze movement patterns. For example, the analysis unit can apply a motion analysis algorithm to movement data to improve performance. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the appropriate analysis algorithm.

[0054] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also evaluate current performance by referring to past data. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. This enables real-time feedback by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0055] The service provider can analyze a player's past performance during feedback to select the optimal feedback method. For example, the service provider can provide feedback that highlights a player's strengths based on past performance data. It can also provide feedback to improve a player's weaknesses based on past performance data. For example, the service provider can provide feedback to promote a player's growth based on past performance data. This allows the service provider to select the optimal feedback method by analyzing past performance. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input past performance data into a generating AI and have the generating AI select the optimal feedback method.

[0056] The feedback system can customize the content of feedback based on the player's current physical condition and environmental conditions. For example, if a player is unwell, the system will provide feedback that takes their physical condition into account. Similarly, if environmental conditions are unfavorable (e.g., high temperature and humidity), the system can provide feedback that takes these conditions into account. For instance, the system adjusts the content of the feedback based on the player's physical condition and environmental conditions. This allows for the provision of more appropriate feedback by adjusting the content based on the player's physical condition and environmental conditions. Some or all of the above-described processes in the feedback system may be performed using AI, or not. For example, the system can input player physical condition data and environmental condition data into a generating AI and have the generating AI customize the content of the feedback.

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

[0058] Step 1: The data collection unit collects real-time data from the athlete. The data collection unit can collect real-time data such as heart rate and movement using, for example, a GPS tracker-integrated chest strap worn by the athlete. The data collection unit measures the athlete's heart rate using a heart rate sensor and collects the data in real time. The data collection unit can also use an accelerometer to detect the athlete's movement. For example, the data collection unit can acquire the athlete's movement data, visualize it, and display it on a dashboard. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can statistically analyze the collected data and evaluate the players' performance. The analysis unit uses machine learning algorithms to analyze the data and identify the players' strengths and areas that need improvement. The analysis unit can also use generative AI to analyze the data and quantitatively evaluate the players' performance. Step 3: The service provider provides feedback based on the analysis results obtained by the analysis unit. The service provider can propose a training plan based on the analysis results. The service provider reports on the athlete's performance evaluation and points out areas for improvement. The service provider can also generate individual training plans based on the analysis results and provide them to the athlete or coach.

[0059] (Example of form 2) The sports coaching system according to an embodiment of the present invention is a system that utilizes generative AI to analyze athletes' performance in detail and provide an optimal training plan. This system aims to solve the problems of a shortage of sports coaches, particularly in junior and senior high schools, and the challenges of sports coaching by teachers without competitive experience. The system uses a GPS tracker-integrated chest strap worn by the athlete to collect real-time data such as heart rate and movement. This allows the system to acquire and visualize the athlete's movement data and display it on a dashboard. Furthermore, it outputs the performance of each athlete in combination with the heart rate sensor. Next, the generative AI quantitatively evaluates training and game performance and provides feedback to the athlete and coach. This makes it possible to identify the athlete's strengths and areas that need improvement and generate an individualized training plan. For example, it can analyze athlete performance in detail and provide an optimal training plan for outdoor sports such as soccer, rugby, tennis, American football, and baseball. This system reduces the burden on coaches and enables high-quality sports coaching. In addition, athletes can track their own progress in real time and make adjustments independently to achieve their goals. This is expected to effectively support sports coaching in educational settings and raise the overall ability of athletes in Japan. This allows sports coaching systems to analyze athletes' performance in detail and provide optimal training plans.

[0060] The sports coaching system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects real-time data of the athlete. The data collection unit can collect real-time data such as heart rate and movement using, for example, a GPS tracker-integrated chest strap worn by the athlete. The data collection unit can measure the athlete's heart rate using, for example, a heart rate sensor and collect the data in real time. The data collection unit can also use an accelerometer to detect the athlete's movement. For example, the data collection unit can acquire the athlete's movement data, visualize it, and display it on a dashboard. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, statistically analyze the collected data and evaluate the athlete's performance. The analysis unit can, for example, analyze the data using a machine learning algorithm to identify the athlete's strengths and areas that need improvement. The analysis unit can also analyze the data using generative AI to quantitatively evaluate the athlete's performance. The data provision unit provides feedback based on the analysis results obtained by the analysis unit. The data provision unit can, for example, propose a training plan based on the analysis results. The data provision unit can, for example, report on the athlete's performance evaluation and point out areas for improvement. Furthermore, the data provider can generate individual training plans based on the analysis results and provide them to athletes and coaches. This allows the sports coaching system according to this embodiment to collect, analyze, and provide feedback on athletes' real-time data, thereby offering optimal training plans.

[0061] The data collection unit collects real-time data from athletes. For example, it can collect real-time data such as heart rate and movement using a GPS tracker-integrated chest strap worn by the athlete. Specifically, a heart rate sensor accurately measures the athlete's heart rate and collects the data in real time. This allows for immediate understanding of the athlete's exercise intensity and fatigue level. Additionally, an accelerometer detects the athlete's movement in three dimensions, providing detailed data such as speed, direction, and acceleration. This enables analysis of the athlete's movement patterns and form. Furthermore, the data collection unit centrally manages this data and transmits it to a cloud server, allowing for real-time data sharing. For example, athlete heart rate and movement data can be visualized on a dedicated dashboard, allowing coaches and trainers to instantly review it. This provides the data collection unit with a foundation for real-time monitoring of athlete performance and for adjusting appropriate guidance and training. The data collection unit can also perform regular calibration and maintenance to ensure data accuracy and reliability. This allows the data collection unit to collect athlete performance data with high accuracy and improve the overall system reliability.

[0062] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can statistically analyze the collected data to evaluate the athlete's performance. Specifically, it uses machine learning algorithms to analyze the data and identify the athlete's strengths and areas for improvement. For example, by analyzing the athlete's heart rate data and analyzing the heart rate variability pattern during exercise, it can evaluate the athlete's endurance and explosive power. It can also analyze movement data obtained from acceleration sensors to evaluate the athlete's form and movement efficiency. Furthermore, the analysis unit can use generative AI to analyze the data and quantitatively evaluate the athlete's performance. The generative AI predicts the athlete's performance based on a large amount of data and proposes an optimal training plan. For example, the generative AI can analyze trends in an athlete's performance based on past training data and match data and predict future performance improvements. This allows the analysis unit to evaluate the athlete's performance from multiple angles and identify specific areas for improvement. Furthermore, the analysis unit visualizes the analysis results so that coaches and trainers can easily understand them. For example, graphs and charts are used to visually show fluctuations in the athlete's performance, making it easier to understand intuitively. This allows the analysis unit to evaluate the players' performance with high accuracy and provide specific areas for improvement.

[0063] The service provider provides feedback based on the analysis results obtained by the analysis provider. Specifically, they can propose training plans based on the analysis results. For example, based on the athlete's heart rate and movement data, they can propose running plans to improve endurance or sprint training to improve explosive power. The service provider also reports on the athlete's performance evaluation and points out areas for improvement. For example, by evaluating the athlete's form and movement efficiency and indicating specific areas for improvement, the athlete can take concrete actions to improve their performance. Furthermore, the service provider can generate individual training plans based on the analysis results and provide them to athletes and coaches. For example, they can create training plans that take into account the athlete's strengths and weaknesses and provide guidelines for the athlete to train efficiently. In this way, the service provider can provide specific feedback to improve the athlete's performance and propose the optimal training plan. Furthermore, the service provider can continuously monitor the effects of the feedback and modify the training plan as needed. For example, by regularly collecting athlete performance data and evaluating the effectiveness of the training plan, they can provide a more effective training plan. In this way, the service provider can provide support to continuously improve the athlete's performance.

[0064] The data collection unit can collect real-time data such as heart rate and movement using a GPS tracker-integrated chest strap worn by the athlete. For example, the data collection unit can measure heart rate using the GPS tracker-integrated chest strap worn by the athlete and collect data in real time. The data collection unit can also use an accelerometer to detect the athlete's movement. For example, the data collection unit can acquire the athlete's movement data, visualize it, and display it on a dashboard. This allows for the accurate collection of real-time data such as the athlete's heart rate and movement. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data acquired from the GPS tracker-integrated chest strap worn by the athlete into a generating AI and have the generating AI perform data analysis.

[0065] The analysis unit can analyze the collected data and evaluate the players' performance. For example, the analysis unit can statistically analyze the collected data to evaluate the players' performance. The analysis unit can also use machine learning algorithms to analyze the data and identify the players' strengths and areas for improvement. For example, the analysis unit can use generative AI to analyze the data and quantitatively evaluate the players' performance. This allows for an accurate evaluation of the players' performance by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0066] The service provider can provide feedback to athletes and coaches based on the analysis results. For example, the service provider can propose training plans based on the analysis results. The service provider can also report on the athletes' performance evaluations and point out areas for improvement. For example, the service provider can generate individual training plans based on the analysis results and provide them to athletes and coaches. This allows athletes and coaches to conduct appropriate training by providing feedback based on the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI perform the generation of feedback.

[0067] The service provider can identify an athlete's strengths and areas for improvement, and generate an individualized training plan. For example, the service provider can identify an athlete's strengths and areas for improvement based on analysis results. The service provider can also generate an individualized training plan and provide it to the athlete or coach. For example, the service provider can analyze an athlete's performance data, identify their strengths and areas for improvement, and create a training plan based on that. This allows for improvement in athlete performance by identifying an athlete's strengths and areas for improvement and providing an individualized training plan. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input analysis results into a generating AI and have the generating AI generate a training plan.

[0068] The service provider enables athletes to track their progress in real time and make adjustments independently to achieve their goals. For example, the service provider collects athlete progress data in real time and displays it on a dashboard. The service provider can also analyze athlete progress data and adjust training plans to achieve goals. For example, the service provider updates training plans based on athlete progress data and provides feedback to the athlete. This allows athletes to track their progress in real time and make adjustments independently to achieve their goals. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input athlete progress data into a generating AI and have the generating AI perform adjustments to the training plan.

[0069] The data collection unit can estimate the athlete's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the athlete is tense, the data collection unit can pause data collection until the athlete relaxes and resume at an appropriate time. The data collection unit can also collect data more frequently if the athlete is focused, to maximize their concentration. For example, if the athlete is tired, the data collection unit can collect data during breaks to monitor their recovery. This allows for the collection of more relevant data by adjusting the timing of data collection based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the athlete's emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0070] The data collection unit can analyze a player's past performance data and select the optimal data collection method. For example, the data collection unit can identify the peak performance time of a player from past data and concentrate data collection during that time. The data collection unit can also identify a player's weaknesses from past data and focus data collection on those areas. For example, the data collection unit can analyze a player's growth pattern from past data and select a data collection method to promote growth. In this way, the optimal data collection method can be selected by analyzing past performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0071] The data collection unit can filter data based on the player's current physical condition and environmental conditions during data collection. For example, if a player is unwell, the data collection unit can temporarily suspend data collection and wait until their condition improves. Similarly, if environmental conditions are unfavorable (e.g., high temperature and humidity), the data collection unit can refrain from collecting data and wait until appropriate environmental conditions are met. For example, the data collection unit can adjust the type and frequency of data collected based on the player's physical condition and environmental conditions. By adjusting data collection based on the player's physical condition and environmental conditions, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input player physical condition data and environmental condition data into a generating AI and have the generating AI perform the data collection filtering.

[0072] The data collection unit can estimate the athlete's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the athlete is tense, the data collection unit may prioritize collecting heart rate data to monitor stress levels. Conversely, if the athlete is relaxed, the data collection unit may prioritize collecting movement data to improve performance. For example, if the athlete is focused, the data collection unit may collect all data equally to perform an overall performance evaluation. This allows for the priority collection of important data by prioritizing data based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the athlete's emotion data into a generative AI and have the generative AI determine the data prioritization.

[0073] The data collection unit can prioritize the collection of highly relevant data by considering the player's geographical location during data collection. For example, if a player is in a specific training area, the data collection unit will collect data specific to that area. Furthermore, if a player is in a match, the data collection unit can prioritize the collection of match-related data. For example, if a player is traveling, the data collection unit will collect travel-related data and monitor performance fluctuations. This allows for the priority collection of highly relevant data by considering the player's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the player's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0074] The data collection unit can analyze the player's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on training content shared by the player on social media. The data collection unit can also adjust the timing of data collection based on the player's physical condition or mood mentioned on social media. For example, the data collection unit can estimate the player's training motivation from their social media activity and determine the priority of data collection. This allows for the collection of relevant data by analyzing the player's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the player's social media data into a generating AI and have the generating AI collect the relevant data.

[0075] The analysis unit can estimate the athlete's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the athlete is tense, the analysis unit provides simple and easy-to-understand analysis results. If the athlete is relaxed, the analysis unit can also provide detailed analysis results to deepen understanding. For example, if the athlete is focused, the analysis unit can provide complex analysis results to improve performance. In this way, by adjusting the presentation of the analysis based on the athlete's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the athlete's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data (e.g., heart rate) to improve performance. It can also perform a simplified analysis on less important data (e.g., distance traveled). For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data to evaluate stress levels. The analysis unit can also apply a tracking algorithm to movement data to analyze movement patterns. For example, the analysis unit can apply a motion analysis algorithm to movement data to improve performance. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the appropriate analysis algorithm.

[0078] The analysis unit can estimate the athlete's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the athlete is in a hurry, the analysis unit can provide a short, concise analysis. If the athlete is relaxed, the analysis unit can provide a detailed analysis to deepen understanding. If the athlete is focused, the analysis unit can provide a complex analysis to improve performance. By adjusting the length of the analysis based on the athlete's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the athlete's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0079] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also evaluate current performance by referring to past data. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. This enables real-time feedback by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and perform an overall performance evaluation. Alternatively, the analysis unit can postpone the analysis of less relevant data and focus on analyzing important data. For example, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0081] The service provider can estimate the athlete's emotions and adjust the feedback method based on the estimated emotions. For example, if the athlete is nervous, the service provider can provide simple and easy-to-understand feedback. If the athlete is relaxed, the service provider can also provide detailed feedback to deepen understanding. For example, if the athlete is focused, the service provider can provide complex feedback to improve performance. In this way, more appropriate feedback can be provided by adjusting the feedback method based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the athlete's emotion data into a generative AI and have the generative AI adjust the feedback method.

[0082] The service provider can analyze a player's past performance during feedback to select the optimal feedback method. For example, the service provider can provide feedback that highlights a player's strengths based on past performance data. It can also provide feedback to improve a player's weaknesses based on past performance data. For example, the service provider can provide feedback to promote a player's growth based on past performance data. This allows the service provider to select the optimal feedback method by analyzing past performance. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input past performance data into a generating AI and have the generating AI select the optimal feedback method.

[0083] The feedback system can customize the content of feedback based on the player's current physical condition and environmental conditions. For example, if a player is unwell, the system will provide feedback that takes their physical condition into account. Similarly, if environmental conditions are unfavorable (e.g., high temperature and humidity), the system can provide feedback that takes these conditions into account. For instance, the system adjusts the content of the feedback based on the player's physical condition and environmental conditions. This allows for the provision of more appropriate feedback by adjusting the content based on the player's physical condition and environmental conditions. Some or all of the above-described processes in the feedback system may be performed using AI, or not. For example, the system can input player physical condition data and environmental condition data into a generating AI and have the generating AI customize the content of the feedback.

[0084] The service provider can estimate the athlete's emotions and prioritize feedback based on the estimated emotions. For example, if the athlete is tense, the service provider will prioritize providing feedback to help them relax. If the athlete is relaxed, the service provider can also prioritize providing feedback to improve their performance. For example, if the athlete is focused, the service provider will provide comprehensive feedback to improve their performance. This allows for the priority of important feedback by prioritizing it based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the athlete's emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0085] The service provider can select the optimal feedback method by considering the player's geographical location information during feedback. For example, if the player is in a specific training area, the service provider can provide feedback tailored to that area. Furthermore, if the player is in a match, the service provider can prioritize providing match-related feedback. For example, if the player is traveling, the service provider can provide travel-related feedback and monitor performance fluctuations. This allows the service provider to select the optimal feedback method by considering the player's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0086] The service provider can analyze the athlete's social media activity and suggest content for feedback. For example, the service provider can provide relevant feedback based on training content shared by the athlete on social media. The service provider can also adjust the content of the feedback based on the athlete's physical condition or mood mentioned on social media. For example, the service provider can estimate the athlete's training motivation from their social media activity and suggest content for feedback. In this way, relevant feedback can be provided by analyzing the athlete's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the athlete's social media data into a generating AI and have the generating AI suggest content for the feedback.

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

[0088] The data collection unit can estimate the athlete's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the athlete is tense, the data collection unit can pause data collection until the athlete relaxes and resume at an appropriate time. The data collection unit can also collect data more frequently if the athlete is focused, to maximize their concentration. For example, if the athlete is tired, the data collection unit can collect data during breaks to monitor their recovery. This allows for the collection of more relevant data by adjusting the timing of data collection based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the athlete's emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0089] The data collection unit can analyze a player's past performance data and select the optimal data collection method. For example, the data collection unit can identify the peak performance time of a player from past data and concentrate data collection during that time. The data collection unit can also identify a player's weaknesses from past data and focus data collection on those areas. For example, the data collection unit can analyze a player's growth pattern from past data and select a data collection method to promote growth. In this way, the optimal data collection method can be selected by analyzing past performance data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0090] The analysis unit can estimate the athlete's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the athlete is tense, the analysis unit provides simple and easy-to-understand analysis results. If the athlete is relaxed, the analysis unit can also provide detailed analysis results to deepen understanding. For example, if the athlete is focused, the analysis unit can provide complex analysis results to improve performance. In this way, by adjusting the presentation of the analysis based on the athlete's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the athlete's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data (e.g., heart rate) to improve performance. It can also perform a simplified analysis on less important data (e.g., distance traveled). For example, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data to evaluate stress levels. The analysis unit can also apply a tracking algorithm to movement data to analyze movement patterns. For example, the analysis unit can apply a motion analysis algorithm to movement data to improve performance. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the appropriate analysis algorithm.

[0093] The analysis unit can estimate the athlete's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the athlete is in a hurry, the analysis unit can provide a short, concise analysis. If the athlete is relaxed, the analysis unit can provide a detailed analysis to deepen understanding. If the athlete is focused, the analysis unit can provide a complex analysis to improve performance. By adjusting the length of the analysis based on the athlete's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the athlete's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0094] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also evaluate current performance by referring to past data. For example, the analysis unit can optimally allocate analysis resources according to the data collection timing. This enables real-time feedback by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0095] The service provider can estimate the athlete's emotions and adjust the feedback method based on the estimated emotions. For example, if the athlete is nervous, the service provider can provide simple and easy-to-understand feedback. If the athlete is relaxed, the service provider can also provide detailed feedback to deepen understanding. For example, if the athlete is focused, the service provider can provide complex feedback to improve performance. In this way, more appropriate feedback can be provided by adjusting the feedback method based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the athlete's emotion data into a generative AI and have the generative AI adjust the feedback method.

[0096] The service provider can analyze a player's past performance during feedback to select the optimal feedback method. For example, the service provider can provide feedback that highlights a player's strengths based on past performance data. It can also provide feedback to improve a player's weaknesses based on past performance data. For example, the service provider can provide feedback to promote a player's growth based on past performance data. This allows the service provider to select the optimal feedback method by analyzing past performance. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input past performance data into a generating AI and have the generating AI select the optimal feedback method.

[0097] The feedback system can customize the content of feedback based on the player's current physical condition and environmental conditions. For example, if a player is unwell, the system will provide feedback that takes their physical condition into account. Similarly, if environmental conditions are unfavorable (e.g., high temperature and humidity), the system can provide feedback that takes these conditions into account. For instance, the system adjusts the content of the feedback based on the player's physical condition and environmental conditions. This allows for the provision of more appropriate feedback by adjusting the content based on the player's physical condition and environmental conditions. Some or all of the above-described processes in the feedback system may be performed using AI, or not. For example, the system can input player physical condition data and environmental condition data into a generating AI and have the generating AI customize the content of the feedback.

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

[0099] Step 1: The data collection unit collects real-time data from the athlete. The data collection unit can collect real-time data such as heart rate and movement using, for example, a GPS tracker-integrated chest strap worn by the athlete. The data collection unit measures the athlete's heart rate using a heart rate sensor and collects the data in real time. The data collection unit can also use an accelerometer to detect the athlete's movement. For example, the data collection unit can acquire the athlete's movement data, visualize it, and display it on a dashboard. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can statistically analyze the collected data and evaluate the players' performance. The analysis unit uses machine learning algorithms to analyze the data and identify the players' strengths and areas that need improvement. The analysis unit can also use generative AI to analyze the data and quantitatively evaluate the players' performance. Step 3: The service provider provides feedback based on the analysis results obtained by the analysis unit. The service provider can propose a training plan based on the analysis results. The service provider reports on the athlete's performance evaluation and points out areas for improvement. The service provider can also generate individual training plans based on the analysis results and provide them to the athlete or coach.

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

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

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

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects real-time data of the athlete using a GPS tracker and heart rate sensor integrated into the chest strap of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which statistically analyzes the collected data and evaluates the athlete's performance. The data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes a training plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects real-time data of the athlete using a GPS tracker and heart rate sensor integrated into the chest strap of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which statistically analyzes the collected data and evaluates the athlete's performance. The data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes a training plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects real-time data of the athlete using a GPS tracker and heart rate sensor integrated into the chest strap of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to statistically analyze the collected data and evaluate the athlete's performance. The data provision unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose a training plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects real-time data of the athlete using a GPS tracker and heart rate sensor integrated into the chest strap of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which statistically analyzes the collected data and evaluates the athlete's performance. The data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes a training plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

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

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The data collection department collects real-time data on the players, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides feedback based on the analysis results obtained by the analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is A GPS tracker-integrated chest strap worn by the athlete is used to collect real-time data such as heart rate and movement. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data and evaluate the players' performance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide feedback to players and coaches based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Identify the player's strengths and areas for improvement, and generate individualized training plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It allows for real-time tracking of athletes' progress and enables them to make adjustments independently to achieve their goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the players' emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the players' past performance data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the player's current physical condition and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the players' emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the players' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze the players' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the players' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the players' emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the player's emotions and adjusts the feedback method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, During feedback sessions, we analyze the player's past performance to select the most appropriate feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing feedback, customize the content of the feedback based on the player's current physical condition and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the player's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback, the optimal feedback method will be selected considering the player's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing feedback, we analyze the player's social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. The data collection department collects real-time data on the players, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides feedback based on the analysis results obtained by the analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is A GPS tracker-integrated chest strap worn by the athlete is used to collect real-time data such as heart rate and movement. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data and evaluate the players' performance. The system according to feature 1.

4. The aforementioned supply unit is, Provide feedback to players and coaches based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Identify the player's strengths and areas for improvement, and generate individualized training plans. The system according to feature 1.

6. The aforementioned supply unit is, It allows for real-time tracking of athletes' progress and enables them to make adjustments independently to achieve their goals. The system according to feature 1.

7. The aforementioned collection unit is We estimate the players' emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the players' past performance data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the player's current physical condition and environmental conditions. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the players' emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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