system

Abstract

The system according to this embodiment aims to support athletes by individually optimizing their training, further education, and job placement. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a creation unit, and a support unit. The collection unit collects data on the athletes. The analysis unit analyzes the data collected by the collection unit. The creation unit creates a training plan based on the analysis results obtained by the analysis unit. The support unit provides support for further education and employment based on the training plan created by the creation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the training of athletes and the support for升学就业 (I'm not sure what this exactly means, it might be "further education and employment") are not individually optimized, and there is room for improvement.

[0005] The system according to the embodiment aims to individually optimize and support the training of athletes and further education and employment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data creation unit, and a support unit. The data collection unit collects data on athletes. The analysis unit analyzes the data collected by the data collection unit. The data creation unit creates a training plan based on the analysis results obtained by the analysis unit. The data creation unit provides support for further education and employment based on the training plan created by the data creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide individually optimized support for athletes' training, further education, and employment. [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 multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that supports athletes' sports lives from their student days through to their lifetime. This AI agent system achieves skill improvement and performance maximization through individually optimized training and feedback based on the latest data analysis. Specifically, the AI ​​agent system analyzes the athlete's play and training data in detail and aims to improve their abilities. Next, by inputting the athlete's performance data, the AI ​​creates an optimal training plan tailored to each individual player. Furthermore, it specifically indicates areas for improvement in play and supports the acquisition of new techniques. This provides accurate advice to efficiently acquire techniques in competition and enhance excellence in game strategy. It also thoroughly supports injury prevention, rehabilitation support, and mental care for active athletes, ensuring that athletes can always compete in the best possible condition. In addition, at important life events such as entering higher education, finding employment, or turning professional, the AI ​​handles cumbersome procedures and supports the selection of educational institutions and career development based on individual data. This helps athletes utilize the experience and skills cultivated in sports at each stage of their lives. The AI ​​agent system independently determines the optimal task for each athlete and executes it efficiently, accurately, and autonomously. For example, the AI ​​agent system offers three dedicated modes tailored to the user's life stage: student-athlete mode, college entrance mode, and job-seeking / professional transition mode, providing optimal support. This allows the AI ​​agent system to efficiently collect and analyze athlete data, create training plans, and provide support for college entrance and job-seeking.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a data creation unit, and a support unit. The data collection unit collects data on the player. This data includes, but is not limited to, physical data, performance data, and psychological data. The data collection unit collects, for example, data on the player's play and practice. The data collection unit can collect video of matches, practice records, performance measurement data, etc. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data in detail using, for example, statistical analysis and machine learning algorithms. The analysis unit performs the analysis based on the type of data, the depth of the analysis, and the tools and algorithms used. The data creation unit creates a training plan based on the analysis results obtained by the analysis unit. The data creation unit creates an optimal training plan based on, for example, the player's goals, fitness level, and training frequency. The data creation unit creates a training plan considering the training menu, duration, intensity, etc. The data creation unit provides support for further education and employment based on the training plan created by the data creation unit. The data creation unit provides, for example, support for selecting schools, resume writing, and interview preparation. The support department handles procedures for further education and employment, and provides assistance in selecting educational institutions and career development based on individual data. This allows the AI ​​agent system, as described in the embodiment, to efficiently collect and analyze athlete data, create training plans, and provide support for further education and employment.

[0030] The data collection unit collects athlete data. This data includes, but is not limited to, physical, performance, and psychological data. Specifically, physical data includes basic body measurements such as height, weight, body fat percentage, and muscle mass. This data is collected from regular physical examinations and medical tests. Performance data includes motion analysis data from matches and training sessions, as well as speed, stamina, and reaction time. This data is obtained from video analysis of matches and training sessions and from wearable devices. Psychological data includes the athlete's mental health status, stress levels, and motivation, and is collected through questionnaires and psychological tests. The data collection unit can centrally manage and update this data in real time. For example, match videos are filmed with high-resolution cameras, and training records are automatically collected using a dedicated tracking system. Performance measurement data is acquired in real time from sensors and GPS devices worn by the athlete and transmitted to a cloud server. This allows the data collection unit to efficiently collect diverse athlete data and make it available to the analysis and creation units. Furthermore, the data collection unit performs data quality control, including filtering and error checking to provide accurate and reliable data. This allows the data collection unit to contribute to improving athlete performance and optimizing training.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to analyze the data in detail. Specifically, in statistical analysis, it calculates basic statistical indicators such as the mean, standard deviation, and correlation coefficient based on player performance data to evaluate the player's current state. Using machine learning algorithms enables more advanced data analysis. For example, it uses deep learning-based image recognition technology to automatically analyze player movements from match videos to identify efficiency and areas for improvement. It also uses recurrent neural networks (RNNs) to perform time-series analysis of player performance data and extract trends and patterns. Furthermore, the analysis unit uses natural language processing (NLP) technology to analyze psychological data, analyzing the results of questionnaires and psychological tests. This allows for a quantitative evaluation of players' mental health and stress levels. Based on these analysis results, the analysis unit clarifies players' strengths and weaknesses and uses this information to create training plans. Additionally, by comparing current data with past data, the analysis unit evaluates player growth and progress and provides feedback for long-term performance improvement. This allows the analysis unit to analyze athlete data from multiple perspectives, contributing to the optimization of training and improvement of performance.

[0032] The creation department creates training plans based on the analysis results obtained by the analysis department. The creation department creates optimal training plans based on factors such as the athlete's goals, fitness level, and training frequency. Specifically, it sets short-term and long-term goals according to the athlete's objectives and constructs training menus based on these. For example, short-term goals might include improving performance or acquiring skills for a specific match. Long-term goals might include improving fitness throughout the season and preventing injuries. The creation department evaluates the athlete's fitness level and adjusts the intensity and frequency of training. For example, recovery training or low-intensity training is recommended for athletes with declining fitness, while high-intensity training or acquiring new skills is recommended for athletes with improving fitness. The training menu includes specific exercises, drills, and practice schedules, designed to allow athletes to train efficiently. Furthermore, the creation department monitors training progress and modifies the plan as needed. For example, if an athlete's performance does not improve or the risk of injury increases, the training menu is reviewed and appropriate adjustments are made. This allows the creation department to provide optimal training plans tailored to the individual needs of each athlete and support performance improvement.

[0033] The Support Department provides support for further education and employment based on the training plans created by the Creation Department. For example, the Support Department assists with selecting educational institutions, resume writing, and interview preparation. Specifically, in selecting educational institutions, they comprehensively evaluate the athlete's academic performance, athletic achievements, and future career goals to propose the most suitable institution. Resume writing assistance involves providing advice on effectively highlighting the athlete's strengths and achievements, creating a professional resume. Interview preparation includes conducting mock interviews to help athletes approach interviews with confidence. Furthermore, the Support Department handles the procedures for further education and employment, ensuring a smooth transition for athletes. For example, they assist with submitting application documents for educational institutions, applying for scholarships, and providing job information and application support. The Support Department also supports educational institution selection and career development based on individual athlete data. For example, they propose the most suitable educational institution and career path based on the athlete's performance and psychological data. This allows the support department to comprehensively assist athletes with their further education and employment, providing a foundation for their future career success. Furthermore, the support department continues to provide ongoing support after athletes enter higher education or employment, offering advice and assistance to help them succeed in their new environments. In this way, the support department can provide long-term support for athletes' career development and support their success.

[0034] The data collection unit can collect player play and training data. For example, the data collection unit can collect match videos. The data collection unit can also collect training records. The data collection unit can also collect performance measurement data. By collecting player play and training data, detailed data analysis becomes possible. 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 match videos into a generating AI and have the generating AI extract play data from the videos.

[0035] The analysis unit can analyze the collected data in detail with the aim of improving performance. The analysis unit can analyze the data using, for example, statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also perform detailed analysis based on the type of data, the depth of the analysis, and the tools and algorithms used. This will support the improvement of players' performance through detailed data analysis. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform a detailed analysis of the data.

[0036] The creation unit can create optimal training plans tailored to individual players. For example, the creation unit can create training plans based on a player's goals. The creation unit can also create training plans based on fitness levels. The creation unit can also create training plans based on training frequency. This enables efficient training by providing each player with an optimal training plan. Some or all of the above processes in the creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the creation unit can input the player's goals and fitness level into a generative AI and have the generative AI create an optimal training plan.

[0037] The creation unit can specifically indicate areas for improvement in gameplay and support the acquisition of new techniques. For example, the creation unit can indicate technical shortcomings. The creation unit can also indicate tactical mistakes. The creation unit can also indicate methods for providing feedback. This allows for efficient acquisition of new techniques by indicating areas for improvement in gameplay. Some or all of the above-described processes in the creation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the creation unit can input areas for improvement in gameplay into a generative AI and have the generative AI execute how to specifically indicate those areas for improvement.

[0038] The support department can handle procedures for further education and employment, and can assist in selecting educational institutions and career development based on individual data. For example, the support department can select educational institutions. The support department can also assist in resume creation. The support department can also provide interview preparation. In this way, by handling procedures for further education and employment, it efficiently supports the career development of athletes. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the selection of educational institutions into a generating AI and have the generating AI perform the selection of educational institutions.

[0039] The support department can provide injury prevention, rehabilitation support, and mental care. For example, the support department can provide injury prevention measures. The support department can also provide rehabilitation programs. The support department can also provide mental care methods. By providing injury prevention, rehabilitation support, and mental care, athletes can compete in their best condition. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input injury prevention measures into a generating AI and have the generating AI provide the preventive measures.

[0040] The support unit can provide three dedicated modes: a college entrance mode, a job-seeking / professional transition mode, and a college entrance mode. The support unit can, for example, provide a college entrance mode. The support unit can also provide a job-seeking mode. The support unit can also provide a professional transition mode. By providing dedicated modes, it becomes possible to provide optimal support tailored to the life stage of the player. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the provision of the college entrance mode into a generating AI and have the generating AI execute the provision of the college entrance mode.

[0041] 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 time period in which a player performed best from past performance data and collect data during that time period. If, based on past data, a player has shown a good response to a particular training method, the data collection unit can also prioritize data collection for that method. Based on past performance data, the data collection unit can also select a data collection method to reinforce a player's weaknesses. 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.

[0042] The data collection unit can filter data based on the player's current physical condition and training status during data collection. For example, if the player is in good physical condition, the data collection unit will perform normal data collection. If the player is fatigued, the data collection unit can reduce data collection and collect only the minimum necessary data. If the player is injured, the data collection unit can also filter the data considering the effects of the injury. This allows for more accurate data collection by filtering the data based on the player's physical condition and training status. 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 physical condition data into a generating AI and have the generating AI perform data filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the athletes during data collection. For example, if an athlete is training at high altitude, the data collection unit will prioritize the collection of performance data at high altitude. If an athlete is training under different climatic conditions, the data collection unit can also prioritize the collection of data related to those climatic conditions. If an athlete is training at a specific stadium, the data collection unit can also prioritize the collection of data related to that stadium. In this way, by considering the geographical location of the athletes, highly relevant data can be prioritized. 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 athlete's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0044] The data collection unit can analyze the athlete'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 athlete on social media. The data collection unit can also adjust the content of data collection based on the athlete's physical condition or mood mentioned on social media. The data collection unit can also collect data by referring to the training methods of other athletes that the athlete follows on social media. In this way, relevant data can be collected by analyzing the athlete's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the athlete's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0045] 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 highly important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also perform an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. 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 perform the adjustment of the level of detail of the analysis based on the importance.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm specialized in improving physical ability to physical data. The analysis unit can also apply an analysis algorithm specialized in improving psychological state to mental data. The analysis unit can also apply an analysis algorithm specialized in improving technical skills to technical data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible. 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 an analysis algorithm appropriate to the category.

[0047] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the analysis of current data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This enables efficient data analysis by determining the priority of analysis based on the data collection period. 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 collection period into a generating AI and have the generating AI determine the priority of analysis based on the collection period.

[0048] 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 may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance next. It may also analyze data with low relevance last. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance.

[0049] The creation unit can create an optimal training plan by referring to the athlete's past training history. For example, the creation unit can incorporate the training methods that were most effective for the athlete from their past training history. The creation unit can also create a plan that strengthens the athlete's weaknesses based on their past training history. The creation unit can also create a plan that is tailored to the athlete's growth by referring to their past training history. In this way, an optimal training plan can be created by referring to their past training history. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input past training history into a generating AI and have the generating AI create an optimal training plan.

[0050] The creation unit can customize training plans based on the athlete's current physical condition and goals. For example, if the athlete is in good physical condition, the creation unit will create a standard training plan. If the athlete is fatigued, the creation unit can also create a lighter training plan. If the athlete has specific goals, the creation unit can also create a training plan tailored to those goals. By customizing the plan based on the athlete's current physical condition and goals, more effective training becomes possible. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the athlete's physical condition and goal data into a generating AI and have the generating AI perform the plan customization.

[0051] The creation unit can create an optimal training plan by considering the athlete's geographical location information. For example, if an athlete is training at high altitude, the creation unit will create a training plan suitable for high altitude. If an athlete is training under different climatic conditions, the creation unit can also create a training plan suitable for those conditions. If an athlete is training at a specific stadium, the creation unit can also create a training plan suitable for that stadium. In this way, an optimal training plan can be created by considering the athlete's geographical location information. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the athlete's geographical location information into a generating AI and have the generating AI create an optimal training plan.

[0052] The creation unit can analyze an athlete's social media activity and adjust the training plan when creating it. For example, the creation unit can create a relevant training plan based on training content shared by the athlete on social media. The creation unit can also adjust the training plan based on the athlete's physical condition or mood mentioned on social media. The creation unit can also create a training plan by referring to the training methods of other athletes that the athlete follows on social media. This allows for the provision of a more appropriate training plan by analyzing the athlete's social media activity. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the athlete's social media activity data into a generating AI and have the generating AI perform the adjustment of the plan.

[0053] The support department can provide optimal support by referring to the athlete's past educational and employment history. For example, the support department can suggest suitable educational institutions based on the athlete's past educational history. The support department can also suggest suitable employment opportunities based on the athlete's past employment history. The support department can also support the athlete's career development by referring to their past educational and employment history. This allows for the provision of optimal support by referring to past educational and employment history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past educational and employment history into a generating AI and have the generating AI perform the task of providing optimal support.

[0054] The support unit can customize the support provided based on the athlete's current living situation. For example, if the athlete's living situation is stable, the support unit will provide standard support. If the athlete is experiencing difficulties in their daily life, the support unit can also provide support specifically tailored to their needs. If the athlete is adapting to a new environment, the support unit can also provide support tailored to that environment. By customizing the support based on the athlete's current living situation, more appropriate support can be provided. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input the athlete's living situation data into a generating AI and have the generating AI perform the customization of the support.

[0055] The support unit can provide optimal support by considering the athlete's geographical location information during support. For example, if an athlete is training at high altitude, the support unit can provide support suitable for high altitude. If an athlete is training under different climatic conditions, the support unit can also provide support suitable for those conditions. If an athlete is training at a specific stadium, the support unit can also provide support suitable for that stadium. In this way, optimal support can be provided by considering the athlete's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the athlete's geographical location information into a generating AI and have the generating AI perform the task of providing optimal support.

[0056] The support department can analyze the athlete's social media activity and adjust the support provided during the support process. For example, the support department can provide relevant support based on the athlete's lifestyle shared on social media. The support department can also adjust the support based on the athlete's physical condition or mood mentioned on social media. The support department can also provide support by referring to the lifestyles of other athletes the athlete follows on social media. This allows for the provision of more appropriate support by analyzing the athlete's social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the athlete's social media activity data into a generating AI and have the generating AI adjust the support content.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can collect athletes' sleep data and use it to create training plans. For example, if an athlete is not getting enough sleep, the unit can adjust the intensity of training and prioritize the athlete's recovery. If an athlete's sleep quality is poor, the unit can also suggest training that promotes relaxation. It can also suggest the optimal training time based on the athlete's sleep patterns. In this way, by utilizing athletes' sleep data, more effective training plans can be provided.

[0059] The training development department can collect athletes' nutritional data and incorporate it into the creation of training plans. For example, if an athlete's nutritional intake is insufficient, the department can create a training plan that takes nutritional supplementation into consideration. It can also adjust the timing and content of training based on the athlete's diet. By analyzing the athlete's nutritional data, if a specific nutrient is deficient, the department can provide dietary guidance to supplement that nutrient. In this way, by utilizing athletes' nutritional data, it is possible to provide healthier and more effective training plans.

[0060] The data collection unit can collect athletes' heart rate data and use it to create training plans. For example, if an athlete's heart rate is high, the unit can adjust the training intensity and create a plan that takes the athlete's physical condition into consideration. If the athlete's heart rate is stable, the unit can also provide a standard training plan. It is also possible to analyze the athlete's heart rate data and evaluate the impact of specific training on heart rate. This allows for the provision of more effective training plans by utilizing the athlete's heart rate data.

[0061] The data collection unit can collect athletes' body temperature data and use it to create training plans. For example, if an athlete's body temperature is high, the unit can adjust the training intensity and create a plan that takes the athlete's physical condition into consideration. If an athlete's body temperature is low, the unit can provide a training plan that emphasizes warm-up. It is also possible to analyze the athletes' body temperature data and evaluate the impact of specific training on body temperature. In this way, by utilizing athletes' body temperature data, more effective training plans can be provided.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects player data. This data includes physical data, performance data, and psychological data. The data collection unit can collect player play and training data, as well as game videos, training records, and performance measurement data. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data in detail using statistical analysis and machine learning algorithms. The analysis is performed based on the type of data, the depth of the analysis, and the tools and algorithms used. Step 3: The creation unit creates a training plan based on the analysis results obtained by the analysis unit. The creation unit creates an optimal training plan based on the athlete's goals, fitness level, training frequency, etc. The training plan is created considering the training menu, duration, intensity, etc. Step 4: The Support Department provides support for further education and employment based on the training plan created by the Creation Department. The Support Department assists with selecting educational institutions, resume writing, and interview preparation. They handle the procedures for further education and employment on behalf of students and provide support for selecting educational institutions and career development based on individual data.

[0064] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that supports athletes' sports lives from their student days through to their lifetime. This AI agent system achieves skill improvement and performance maximization through individually optimized training and feedback based on the latest data analysis. Specifically, the AI ​​agent system analyzes the athlete's play and training data in detail and aims to improve their abilities. Next, by inputting the athlete's performance data, the AI ​​creates an optimal training plan tailored to each individual player. Furthermore, it specifically indicates areas for improvement in play and supports the acquisition of new techniques. This provides accurate advice to efficiently acquire techniques in competition and enhance excellence in game strategy. It also thoroughly supports injury prevention, rehabilitation support, and mental care for active athletes, ensuring that athletes can always compete in the best possible condition. In addition, at important life events such as entering higher education, finding employment, or turning professional, the AI ​​handles cumbersome procedures and supports the selection of educational institutions and career development based on individual data. This helps athletes utilize the experience and skills cultivated in sports at each stage of their lives. The AI ​​agent system independently determines the optimal task for each athlete and executes it efficiently, accurately, and autonomously. For example, the AI ​​agent system offers three dedicated modes tailored to the user's life stage: student-athlete mode, college entrance mode, and job-seeking / professional transition mode, providing optimal support. This allows the AI ​​agent system to efficiently collect and analyze athlete data, create training plans, and provide support for college entrance and job-seeking.

[0065] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a data creation unit, and a support unit. The data collection unit collects data on the player. This data includes, but is not limited to, physical data, performance data, and psychological data. The data collection unit collects, for example, data on the player's play and practice. The data collection unit can collect video of matches, practice records, performance measurement data, etc. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data in detail using, for example, statistical analysis and machine learning algorithms. The analysis unit performs the analysis based on the type of data, the depth of the analysis, and the tools and algorithms used. The data creation unit creates a training plan based on the analysis results obtained by the analysis unit. The data creation unit creates an optimal training plan based on, for example, the player's goals, fitness level, and training frequency. The data creation unit creates a training plan considering the training menu, duration, intensity, etc. The data creation unit provides support for further education and employment based on the training plan created by the data creation unit. The data creation unit provides, for example, support for selecting schools, resume writing, and interview preparation. The support department handles procedures for further education and employment, and provides assistance in selecting educational institutions and career development based on individual data. This allows the AI ​​agent system, as described in the embodiment, to efficiently collect and analyze athlete data, create training plans, and provide support for further education and employment.

[0066] The data collection unit collects athlete data. This data includes, but is not limited to, physical, performance, and psychological data. Specifically, physical data includes basic body measurements such as height, weight, body fat percentage, and muscle mass. This data is collected from regular physical examinations and medical tests. Performance data includes motion analysis data from matches and training sessions, as well as speed, stamina, and reaction time. This data is obtained from video analysis of matches and training sessions and from wearable devices. Psychological data includes the athlete's mental health status, stress levels, and motivation, and is collected through questionnaires and psychological tests. The data collection unit can centrally manage and update this data in real time. For example, match videos are filmed with high-resolution cameras, and training records are automatically collected using a dedicated tracking system. Performance measurement data is acquired in real time from sensors and GPS devices worn by the athlete and transmitted to a cloud server. This allows the data collection unit to efficiently collect diverse athlete data and make it available to the analysis and creation units. Furthermore, the data collection unit performs data quality control, including filtering and error checking to provide accurate and reliable data. This allows the data collection unit to contribute to improving athlete performance and optimizing training.

[0067] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to analyze the data in detail. Specifically, in statistical analysis, it calculates basic statistical indicators such as the mean, standard deviation, and correlation coefficient based on player performance data to evaluate the player's current state. Using machine learning algorithms enables more advanced data analysis. For example, it uses deep learning-based image recognition technology to automatically analyze player movements from match videos to identify efficiency and areas for improvement. It also uses recurrent neural networks (RNNs) to perform time-series analysis of player performance data and extract trends and patterns. Furthermore, the analysis unit uses natural language processing (NLP) technology to analyze psychological data, analyzing the results of questionnaires and psychological tests. This allows for a quantitative evaluation of players' mental health and stress levels. Based on these analysis results, the analysis unit clarifies players' strengths and weaknesses and uses this information to create training plans. Additionally, by comparing current data with past data, the analysis unit evaluates player growth and progress and provides feedback for long-term performance improvement. This allows the analysis unit to analyze athlete data from multiple perspectives, contributing to the optimization of training and improvement of performance.

[0068] The creation department creates training plans based on the analysis results obtained by the analysis department. The creation department creates optimal training plans based on factors such as the athlete's goals, fitness level, and training frequency. Specifically, it sets short-term and long-term goals according to the athlete's objectives and constructs training menus based on these. For example, short-term goals might include improving performance or acquiring skills for a specific match. Long-term goals might include improving fitness throughout the season and preventing injuries. The creation department evaluates the athlete's fitness level and adjusts the intensity and frequency of training. For example, recovery training or low-intensity training is recommended for athletes with declining fitness, while high-intensity training or acquiring new skills is recommended for athletes with improving fitness. The training menu includes specific exercises, drills, and practice schedules, designed to allow athletes to train efficiently. Furthermore, the creation department monitors training progress and modifies the plan as needed. For example, if an athlete's performance does not improve or the risk of injury increases, the training menu is reviewed and appropriate adjustments are made. This allows the creation department to provide optimal training plans tailored to the individual needs of each athlete and support performance improvement.

[0069] The Support Department provides support for further education and employment based on the training plans created by the Creation Department. For example, the Support Department assists with selecting educational institutions, resume writing, and interview preparation. Specifically, in selecting educational institutions, they comprehensively evaluate the athlete's academic performance, athletic achievements, and future career goals to propose the most suitable institution. Resume writing assistance involves providing advice on effectively highlighting the athlete's strengths and achievements, creating a professional resume. Interview preparation includes conducting mock interviews to help athletes approach interviews with confidence. Furthermore, the Support Department handles the procedures for further education and employment, ensuring a smooth transition for athletes. For example, they assist with submitting application documents for educational institutions, applying for scholarships, and providing job information and application support. The Support Department also supports educational institution selection and career development based on individual athlete data. For example, they propose the most suitable educational institution and career path based on the athlete's performance and psychological data. This allows the support department to comprehensively assist athletes with their further education and employment, providing a foundation for their future career success. Furthermore, the support department continues to provide ongoing support after athletes enter higher education or employment, offering advice and assistance to help them succeed in their new environments. In this way, the support department can provide long-term support for athletes' career development and support their success.

[0070] The data collection unit can collect player play and training data. For example, the data collection unit can collect match videos. The data collection unit can also collect training records. The data collection unit can also collect performance measurement data. By collecting player play and training data, detailed data analysis becomes possible. 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 match videos into a generating AI and have the generating AI extract play data from the videos.

[0071] The analysis unit can analyze the collected data in detail with the aim of improving performance. The analysis unit can analyze the data using, for example, statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also perform detailed analysis based on the type of data, the depth of the analysis, and the tools and algorithms used. This will support the improvement of players' performance through detailed data analysis. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform a detailed analysis of the data.

[0072] The creation unit can create optimal training plans tailored to individual players. For example, the creation unit can create training plans based on a player's goals. The creation unit can also create training plans based on fitness levels. The creation unit can also create training plans based on training frequency. This enables efficient training by providing each player with an optimal training plan. Some or all of the above processes in the creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the creation unit can input the player's goals and fitness level into a generative AI and have the generative AI create an optimal training plan.

[0073] The creation unit can specifically indicate areas for improvement in gameplay and support the acquisition of new techniques. For example, the creation unit can indicate technical shortcomings. The creation unit can also indicate tactical mistakes. The creation unit can also indicate methods for providing feedback. This allows for efficient acquisition of new techniques by indicating areas for improvement in gameplay. Some or all of the above-described processes in the creation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the creation unit can input areas for improvement in gameplay into a generative AI and have the generative AI execute how to specifically indicate those areas for improvement.

[0074] The support department can handle procedures for further education and employment, and can assist in selecting educational institutions and career development based on individual data. For example, the support department can select educational institutions. The support department can also assist in resume creation. The support department can also provide interview preparation. In this way, by handling procedures for further education and employment, it efficiently supports the career development of athletes. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the selection of educational institutions into a generating AI and have the generating AI perform the selection of educational institutions.

[0075] The support department can provide injury prevention, rehabilitation support, and mental care. For example, the support department can provide injury prevention measures. The support department can also provide rehabilitation programs. The support department can also provide mental care methods. By providing injury prevention, rehabilitation support, and mental care, athletes can compete in their best condition. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input injury prevention measures into a generating AI and have the generating AI provide the preventive measures.

[0076] The support unit can provide three dedicated modes: a college entrance mode, a job-seeking / professional transition mode, and a college entrance mode. The support unit can, for example, provide a college entrance mode. The support unit can also provide a job-seeking mode. The support unit can also provide a professional transition mode. By providing dedicated modes, it becomes possible to provide optimal support tailored to the life stage of the player. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the provision of the college entrance mode into a generating AI and have the generating AI execute the provision of the college entrance mode.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing to collect data when the user is relaxed. If the user is focused, the data collection unit can also leverage that focus to collect data quickly. If the user is tired, the data collection unit can adjust the timing to collect data after the user has rested. By adjusting the timing of data collection based on the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of data collection timing based on emotions.

[0078] 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 time period in which a player performed best from past performance data and collect data during that time period. If, based on past data, a player has shown a good response to a particular training method, the data collection unit can also prioritize data collection for that method. Based on past performance data, the data collection unit can also select a data collection method to reinforce a player's weaknesses. 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.

[0079] The data collection unit can filter data based on the player's current physical condition and training status during data collection. For example, if the player is in good physical condition, the data collection unit will perform normal data collection. If the player is fatigued, the data collection unit can reduce data collection and collect only the minimum necessary data. If the player is injured, the data collection unit can also filter the data considering the effects of the injury. This allows for more accurate data collection by filtering the data based on the player's physical condition and training status. 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 physical condition data into a generating AI and have the generating AI perform data filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data related to stress reduction. If the user is relaxed, the data collection unit may also prioritize collecting normal training data. If the user is focused, the data collection unit may also prioritize collecting data related to skill improvement. This allows for more effective data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority based on emotions.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of the athletes during data collection. For example, if an athlete is training at high altitude, the data collection unit will prioritize the collection of performance data at high altitude. If an athlete is training under different climatic conditions, the data collection unit can also prioritize the collection of data related to those climatic conditions. If an athlete is training at a specific stadium, the data collection unit can also prioritize the collection of data related to that stadium. In this way, by considering the geographical location of the athletes, highly relevant data can be prioritized. 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 athlete's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0082] The data collection unit can analyze the athlete'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 athlete on social media. The data collection unit can also adjust the content of data collection based on the athlete's physical condition or mood mentioned on social media. The data collection unit can also collect data by referring to the training methods of other athletes that the athlete follows on social media. In this way, relevant data can be collected by analyzing the athlete's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the athlete's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. By adjusting the presentation of the analysis based on the user'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 user emotion data into the generative AI and have the generative AI perform adjustments to the presentation of the analysis based on emotions.

[0084] 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 highly important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also perform an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. 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 perform the adjustment of the level of detail of the analysis based on the importance.

[0085] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm specialized in improving physical ability to physical data. The analysis unit can also apply an analysis algorithm specialized in improving psychological state to mental data. The analysis unit can also apply an analysis algorithm specialized in improving technical skills to technical data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible. 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 an analysis algorithm appropriate to the category.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit may provide a display method with calm colors. If the user is relaxed, the analysis unit may also provide a display method with bright colors. If the user is in a hurry, the analysis unit may also provide a concise and highly visible display method. By adjusting the display method of the analysis results based on the user'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 a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the display method of the analysis results based on emotions.

[0087] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the analysis of current data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This enables efficient data analysis by determining the priority of analysis based on the data collection period. 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 collection period into a generating AI and have the generating AI determine the priority of analysis based on the collection period.

[0088] 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 may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance next. It may also analyze data with low relevance last. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance.

[0089] The creation unit can estimate the user's emotions and adjust the content of the training plan based on the estimated emotions. For example, if the user is feeling stressed, the creation unit can create a relaxing training plan. If the user is relaxed, the creation unit can also create a normal training plan. If the user is focused, the creation unit can also create a training plan specifically for skill improvement. This allows for the provision of a more appropriate training plan by adjusting the content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input user emotion data into a generative AI and have the generative AI adjust the content of the training plan based on the emotions.

[0090] The creation unit can create an optimal training plan by referring to the athlete's past training history. For example, the creation unit can incorporate the training methods that were most effective for the athlete from their past training history. The creation unit can also create a plan that strengthens the athlete's weaknesses based on their past training history. The creation unit can also create a plan that is tailored to the athlete's growth by referring to their past training history. In this way, an optimal training plan can be created by referring to their past training history. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input past training history into a generating AI and have the generating AI create an optimal training plan.

[0091] The creation unit can customize training plans based on the athlete's current physical condition and goals. For example, if the athlete is in good physical condition, the creation unit will create a standard training plan. If the athlete is fatigued, the creation unit can also create a lighter training plan. If the athlete has specific goals, the creation unit can also create a training plan tailored to those goals. By customizing the plan based on the athlete's current physical condition and goals, more effective training becomes possible. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the athlete's physical condition and goal data into a generating AI and have the generating AI perform the plan customization.

[0092] The creation unit can estimate the user's emotions and determine the priority of training plans based on the estimated emotions. For example, if the user is stressed, the creation unit will prioritize training related to stress reduction. If the user is relaxed, the creation unit may also prioritize normal training. If the user is focused, the creation unit may also prioritize training related to skill improvement. This allows for more effective training by prioritizing training plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 creation unit may be performed using AI or not using AI. For example, the creation unit can input user emotion data into a generative AI and have the generative AI perform the determination of priority training plans based on emotions.

[0093] The creation unit can create an optimal training plan by considering the athlete's geographical location information. For example, if an athlete is training at high altitude, the creation unit will create a training plan suitable for high altitude. If an athlete is training under different climatic conditions, the creation unit can also create a training plan suitable for those conditions. If an athlete is training at a specific stadium, the creation unit can also create a training plan suitable for that stadium. In this way, an optimal training plan can be created by considering the athlete's geographical location information. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the athlete's geographical location information into a generating AI and have the generating AI create an optimal training plan.

[0094] The creation unit can analyze an athlete's social media activity and adjust the training plan when creating it. For example, the creation unit can create a relevant training plan based on training content shared by the athlete on social media. The creation unit can also adjust the training plan based on the athlete's physical condition or mood mentioned on social media. The creation unit can also create a training plan by referring to the training methods of other athletes that the athlete follows on social media. This allows for the provision of a more appropriate training plan by analyzing the athlete's social media activity. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can input the athlete's social media activity data into a generating AI and have the generating AI perform the adjustment of the plan.

[0095] The support unit can estimate the user's emotions and adjust the support content based on the estimated emotions. For example, if the user is stressed, the support unit can provide relaxing support. If the user is relaxed, the support unit can also provide standard support. If the user is focused, the support unit can also provide support specifically focused on improving their skills. This allows for more appropriate support to be provided by adjusting the support content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of support content based on emotions.

[0096] The support department can provide optimal support by referring to the athlete's past educational and employment history. For example, the support department can suggest suitable educational institutions based on the athlete's past educational history. The support department can also suggest suitable employment opportunities based on the athlete's past employment history. The support department can also support the athlete's career development by referring to their past educational and employment history. This allows for the provision of optimal support by referring to past educational and employment history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past educational and employment history into a generating AI and have the generating AI perform the task of providing optimal support.

[0097] The support unit can customize the support provided based on the athlete's current living situation. For example, if the athlete's living situation is stable, the support unit will provide standard support. If the athlete is experiencing difficulties in their daily life, the support unit can also provide support specifically tailored to their needs. If the athlete is adapting to a new environment, the support unit can also provide support tailored to that environment. By customizing the support based on the athlete's current living situation, more appropriate support can be provided. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input the athlete's living situation data into a generating AI and have the generating AI perform the customization of the support.

[0098] The support unit can estimate the user's emotions and determine support priorities based on those emotions. For example, if the user is stressed, the support unit will prioritize support related to stress reduction. If the user is relaxed, the support unit may also prioritize normal support. If the user is focused, the support unit may also prioritize support related to skill improvement. This allows for more effective support by prioritizing support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine support priorities based on emotions.

[0099] The support unit can provide optimal support by considering the athlete's geographical location information during support. For example, if an athlete is training at high altitude, the support unit can provide support suitable for high altitude. If an athlete is training under different climatic conditions, the support unit can also provide support suitable for those conditions. If an athlete is training at a specific stadium, the support unit can also provide support suitable for that stadium. In this way, optimal support can be provided by considering the athlete's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the athlete's geographical location information into a generating AI and have the generating AI perform the task of providing optimal support.

[0100] The support department can analyze the athlete's social media activity and adjust the support provided during the support process. For example, the support department can provide relevant support based on the athlete's lifestyle shared on social media. The support department can also adjust the support based on the athlete's physical condition or mood mentioned on social media. The support department can also provide support by referring to the lifestyles of other athletes the athlete follows on social media. This allows for the provision of more appropriate support by analyzing the athlete's social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the athlete's social media activity data into a generating AI and have the generating AI adjust the support content.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The analysis unit can estimate the athlete's emotions and adjust the feedback method of the analysis results based on those estimated emotions. For example, if an athlete is feeling down, the analysis unit can emphasize positive feedback to boost the athlete's motivation. If an athlete is confident, the analysis unit can provide detailed specific areas for improvement to encourage further skill development. If an athlete is feeling anxious, the analysis unit can provide concise and to-the-point feedback to enable a quick response. This maximizes the effectiveness of training by providing appropriate feedback tailored to the athlete's emotions.

[0103] The data collection unit can collect athletes' sleep data and use it to create training plans. For example, if an athlete is not getting enough sleep, the unit can adjust the intensity of training and prioritize the athlete's recovery. If an athlete's sleep quality is poor, the unit can also suggest training that promotes relaxation. It can also suggest the optimal training time based on the athlete's sleep patterns. In this way, by utilizing athletes' sleep data, more effective training plans can be provided.

[0104] The training development department can collect athletes' nutritional data and incorporate it into the creation of training plans. For example, if an athlete's nutritional intake is insufficient, the department can create a training plan that takes nutritional supplementation into consideration. It can also adjust the timing and content of training based on the athlete's diet. By analyzing the athlete's nutritional data, if a specific nutrient is deficient, the department can provide dietary guidance to supplement that nutrient. In this way, by utilizing athletes' nutritional data, it is possible to provide healthier and more effective training plans.

[0105] The support department can estimate the athlete's emotions and adjust the support provided for further education or employment based on those estimates. For example, if an athlete is feeling anxious, the support department can provide information about schools or jobs that will give them a sense of security. If an athlete is confident, the support department can suggest challenging schools or jobs to encourage their growth. If an athlete is undecided, the support department can also provide specific advice to narrow down their options. In this way, by providing appropriate support tailored to the athlete's emotions, the department can help them succeed in further education or employment.

[0106] The analysis unit can estimate the athlete's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the athlete is nervous, the analysis unit can provide a display method with calm colors. If the athlete is relaxed, it can provide a display method with bright colors. If the athlete is in a hurry, it can provide a concise and highly visible display method. In this way, by adjusting the display method of the analysis results based on the athlete's emotions, more appropriate analysis results can be provided.

[0107] The data collection unit can collect athletes' heart rate data and use it to create training plans. For example, if an athlete's heart rate is high, the unit can adjust the training intensity and create a plan that takes the athlete's physical condition into consideration. If the athlete's heart rate is stable, the unit can also provide a standard training plan. It is also possible to analyze the athlete's heart rate data and evaluate the impact of specific training on heart rate. This allows for the provision of more effective training plans by utilizing the athlete's heart rate data.

[0108] The creation unit can estimate the athlete's emotions and adjust the training plan based on those estimates. For example, if an athlete is stressed, it can create a training plan designed to promote relaxation. If the athlete is relaxed, it can create a standard training plan. If the athlete is focused, it can create a training plan specifically focused on skill improvement. By adjusting the training plan based on the athlete's emotions, it is possible to provide a more appropriate training plan.

[0109] The support team can estimate the athlete's emotions and adjust the support content based on those estimates. For example, if an athlete is feeling stressed, it can provide support that promotes relaxation. If the athlete is relaxed, it can provide standard support. If the athlete is focused, it can provide support specifically tailored to improving their skills. By adjusting the support content based on the athlete's emotions, it is possible to provide more appropriate support.

[0110] The data collection unit can collect athletes' body temperature data and use it to create training plans. For example, if an athlete's body temperature is high, the unit can adjust the training intensity and create a plan that takes the athlete's physical condition into consideration. If an athlete's body temperature is low, the unit can provide a training plan that emphasizes warm-up. It is also possible to analyze the athletes' body temperature data and evaluate the impact of specific training on body temperature. In this way, by utilizing athletes' body temperature data, more effective training plans can be provided.

[0111] The system can estimate the athlete's emotions and prioritize training plans based on those estimates. For example, if an athlete is stressed, training related to stress reduction can be prioritized. If an athlete is relaxed, regular training can be prioritized. If an athlete is focused, training related to skill improvement can be prioritized. This allows for more effective training by prioritizing training plans based on the athlete's emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects player data. This data includes physical data, performance data, and psychological data. The data collection unit can collect player play and training data, as well as game videos, training records, and performance measurement data. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data in detail using statistical analysis and machine learning algorithms. The analysis is performed based on the type of data, the depth of the analysis, and the tools and algorithms used. Step 3: The creation unit creates a training plan based on the analysis results obtained by the analysis unit. The creation unit creates an optimal training plan based on the athlete's goals, fitness level, training frequency, etc. The training plan is created considering the training menu, duration, intensity, etc. Step 4: The Support Department provides support for further education and employment based on the training plan created by the Creation Department. The Support Department assists with selecting educational institutions, resume writing, and interview preparation. They handle the procedures for further education and employment on behalf of students and provide support for selecting educational institutions and career development based on individual data.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects athlete data using the camera 42 and microphone 38B of the smart device 14 and manages the data with the control unit 46A. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The creation unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and creates a training plan based on the analysis results. The support unit is implemented in detail by the control unit 46A of the smart device 14 and provides support for further education and employment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects athlete data using the camera 42 and microphone 238 of the smart glasses 214 and manages the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in detail. The creation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and creates a training plan based on the analysis results. The support unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides support for further education and employment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects athlete data using the camera 42 and microphone 238 of the headset terminal 314 and manages the data with the control unit 46A. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The creation unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and creates a training plan based on the analysis results. The support unit is implemented in detail by the control unit 46A of the headset terminal 314 and provides support for further education and employment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects athlete data using the camera 42 and microphone 238 of the robot 414 and manages the data with the control unit 46A. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The creation unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and creates a training plan based on the analysis results. The support unit is implemented in detail by the control unit 46A of the robot 414 and provides support for further education and employment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) The data collection department collects player data, An analysis unit analyzes the data collected by the aforementioned collection unit, A creation unit creates a training plan based on the analysis results obtained by the aforementioned analysis unit, The support department provides support for further education and employment based on the training plan created by the aforementioned creation department, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect player performance and training data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We will analyze the collected data in detail and aim to improve our capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, We create optimal training plans tailored to each individual player. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, It provides specific areas for improvement in gameplay and supports the acquisition of new techniques. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We handle the procedures for further education and employment, and provide support for selecting educational institutions and career development based on individual data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit is We provide injury prevention, rehabilitation support, and mental care. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned support unit is It offers three dedicated modes: College Entrance Mode, Job Hunting / Professional Career Mode. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is When collecting data, filtering is performed based on the player's current physical condition and training status. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) 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 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) 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 21) The aforementioned creation unit, The system estimates the user's emotions and adjusts the training plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, When creating a training plan, we refer to the athlete's past training history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating a training plan, customize it based on the athlete's current physical condition and goals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, It estimates the user's emotions and prioritizes training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creation unit, When creating a training plan, we take the player's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creation unit, When creating training plans, we analyze the players' social media activity and adjust the plans accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is The system estimates the user's emotions and adjusts the support provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is When providing support, we refer to the athlete's past educational and employment history to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing support, customize the support content based on the athlete's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is When providing support, we take into account the player's geographical location to provide optimal support. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is During support, we analyze the player's social media activity and adjust the support accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 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 player data, An analysis unit analyzes the data collected by the aforementioned collection unit, A creation unit creates a training plan based on the analysis results obtained by the aforementioned analysis unit, The support department provides support for further education and employment based on the training plan created by the aforementioned creation department, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect player performance and training data. The system according to feature 1.

3. The aforementioned analysis unit, We will analyze the collected data in detail and aim to improve our capabilities. The system according to feature 1.

4. The aforementioned creation unit, We create optimal training plans tailored to each individual player. The system according to feature 1.

5. The aforementioned creation unit, It provides specific areas for improvement in gameplay and supports the acquisition of new techniques. The system according to feature 1.

6. The aforementioned support unit is We handle the procedures for further education and employment, and provide support for selecting educational institutions and career development based on individual data. The system according to feature 1.

7. The aforementioned support unit is We provide injury prevention, rehabilitation support, and mental care. The system according to feature 1.

8. The aforementioned support unit is It offers three dedicated modes: College Entrance Mode, Job Hunting / Professional Career Mode. The system according to feature 1.

Images