system

Abstract

The system according to this embodiment aims to evaluate the psychological state of athletes and provide appropriate support in real time. [Solution] The system according to the embodiment comprises an evaluation unit, a support unit, a relaxation unit, and an education unit. The evaluation unit evaluates the psychological state of the athlete. The support unit provides support in real time based on the psychological state evaluated by the evaluation unit. The relaxation unit provides a relaxation program based on the support provided by the support unit. The education unit provides educational resources for coaches and educators based on the relaxation program provided by the relaxation 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the mental state of an athlete has not been sufficiently evaluated in real time and appropriate support has not been provided, leaving room for improvement.

[0005] The system according to the embodiment aims to evaluate the mental state of an athlete and provide appropriate support in real time.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an evaluation unit, a support unit, a relaxation unit, and an education unit. The evaluation unit evaluates the psychological state of the athlete. The support unit provides support in real time based on the psychological state evaluated by the evaluation unit. The relaxation unit provides a relaxation program based on the support provided by the support unit. The education unit provides educational resources for coaches and educators based on the relaxation program provided by the relaxation unit. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the psychological state of athletes and provide appropriate support in real time. [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 numbered 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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent service according to an embodiment of the present invention is a system that provides 24 / 7 support to enable athletes and student athletes to care for their mental health. This system provides the functions of psychological assessment, real-time support, relaxation programs, and educational resources. For example, the psychological assessment function periodically evaluates the athlete's psychological state and provides feedback for stress management. Next, the real-time support function provides 24 / 7 support via chat and voice, creating an environment where athletes can consult whenever they feel stressed. Furthermore, the relaxation program function provides meditation and mindfulness programs to promote mental and physical relaxation. It also provides educational resources on mental health care for coaches and educators to raise awareness. For example, let's describe the psychological assessment function. The AI ​​agent periodically evaluates the athlete's psychological state and understands their stress level and psychological health status. For example, it evaluates the stress and anxiety that athletes feel after daily training or matches and provides appropriate feedback based on the results. This allows athletes to objectively understand their own psychological state and receive specific advice for stress management. Next, let's describe the real-time support function. The AI ​​agent provides 24 / 7 support via chat and voice. The goal is to create an environment where athletes can seek advice anytime they feel stressed or have mental health concerns. For example, when feeling nervous or pressured before a match, they can consult an AI agent to receive appropriate advice and relaxation techniques. Furthermore, the relaxation program function will be explained. The AI ​​agent will provide meditation and mindfulness programs to promote relaxation of the athlete's mind and body. For example, by implementing the relaxation program during post-match recovery or between daily training sessions, athletes can restore balance to their mind and body. This allows athletes to compete in a relaxed state, which can lead to improved performance. In addition, educational resources on mental health care will be provided for coaches and educators.The AI ​​agent provides coaches and educators with information on the importance of mental health care and specific care methods, raising awareness. This will lead to more effective mental health care for athletes. Thus, the AI ​​agent service of this invention provides 24 / 7 support to enable athletes and student athletes to care for their mental health, and comprehensively supports athletes' mental health through functions such as psychological assessment, real-time support, relaxation programs, and coach / educational support. In this way, the AI ​​agent service can comprehensively support the mental health of athletes.

[0029] The AI ​​agent service according to this embodiment comprises an evaluation unit, a support unit, a relaxation unit, and an education unit. The evaluation unit evaluates the psychological state of the athlete. The evaluation unit, for example, grasps the athlete's stress level and psychological health status. The evaluation unit, for example, evaluates the stress and anxiety that the athlete feels after daily training or matches, and provides appropriate feedback based on the results. The evaluation unit, for example, provides specific advice to the athlete for stress management based on the results of the psychological evaluation. The support unit provides support in real time based on the psychological state evaluated by the evaluation unit. The support unit, for example, provides 24 / 7 chat and voice support. The support unit, for example, creates an environment where athletes can consult anytime they feel stressed or have concerns about their mental health. The support unit, for example, provides appropriate advice and relaxation methods when the athlete feels tension or pressure before a match. The relaxation unit provides relaxation programs based on the support provided by the support unit. The relaxation unit, for example, provides meditation and mindfulness programs. The relaxation unit implements relaxation programs, for example, during post-match recovery or in between daily training sessions. The Relaxation Department provides, for example, specific relaxation methods to restore balance to mind and body. The Education Department provides educational resources for coaches and educators based on the relaxation programs provided by the Relaxation Department. The Education Department provides, for example, educational resources on mental health care. The Education Department provides, for example, information to coaches and educators on the importance of mental health care and specific care methods. The Education Department provides educational resources to make athlete mental health care more effective. As a result, the AI ​​agent service according to the embodiment can comprehensively support the mental health of athletes.

[0030] The evaluation department assesses the psychological state of athletes. For example, it understands athletes' stress levels and psychological health. Specifically, the evaluation department collects feedback from athletes during their daily training and after competitions, and analyzes this data using AI. The AI ​​integrates diverse data such as the athlete's heart rate, sleep patterns, dietary records, and self-reported stress levels to conduct a comprehensive psychological assessment. For example, it provides a detailed assessment of the stress athletes experience during training and anxiety after competitions, and provides individualized feedback based on the results. Based on the results of the psychological assessment, the evaluation department provides athletes with specific advice for stress management. For example, if an athlete shows a high stress level, the evaluation department proposes relaxation methods and specific action plans for mental health care. This allows athletes to objectively understand their own psychological state and take appropriate measures. Furthermore, the evaluation department conducts regular psychological assessments and continuously monitors changes in athletes' mental health. This enables long-term mental health care and contributes to improving athletes' performance.

[0031] The support department provides real-time support based on the psychological state assessed by the evaluation department. For example, the support department offers 24 / 7 support via chat and voice. Specifically, it creates an environment where athletes can seek advice anytime they feel stressed or have mental health concerns. The support department utilizes AI to analyze athletes' consultations and provide appropriate advice and relaxation methods. For instance, when an athlete feels tension or pressure before a match, the AI ​​provides optimal relaxation methods and mental health care advice based on the athlete's past data and current psychological state. The support department also leverages an extensive database to quickly provide athletes with the information and resources they need. This ensures athletes receive the necessary support at any time, helping them maintain their mental health. Furthermore, the support department collects athlete feedback to continuously improve the quality of support provided. For example, by evaluating the effectiveness of the support received and reviewing the support content based on the results, more effective support can be provided. In this way, the support department comprehensively supports athletes' mental health and contributes to performance improvement.

[0032] The Relaxation Department provides relaxation programs based on the support provided by the Support Department. For example, the Relaxation Department offers meditation and mindfulness programs. Specifically, the Relaxation Department designs individually customized relaxation programs according to the athlete's psychological state and stress level. For example, relaxation programs are implemented during post-competition recovery or between daily training sessions. The Relaxation Department provides specific relaxation methods to help athletes restore mental and physical balance. For example, it creates a relaxing environment for athletes using deep breathing exercises, yoga, and relaxation music. The Relaxation Department also provides guided sessions and video tutorials to help athletes effectively implement the relaxation programs. This allows athletes to implement the programs anywhere, such as at home or at training facilities. Furthermore, the Relaxation Department continuously improves the content of the relaxation programs based on athlete feedback. For example, if an athlete finds a particular relaxation method highly effective, that method can be incorporated into the program to provide a more effective relaxation program. In this way, the Relaxation Department helps athletes restore mental and physical balance and contributes to improved performance.

[0033] The Education Department provides educational resources for coaches and educators based on relaxation programs offered by the Relaxation Department. For example, the Education Department provides educational resources on mental health care. Specifically, it provides coaches and educators with information on the importance of mental health care and specific care methods. For instance, it provides educational resources to help athletes' mental health care be more effective. The Education Department provides training on the effectiveness and implementation methods of relaxation programs to help coaches and educators provide appropriate support to athletes. Furthermore, the Education Department provides information based on the latest mental health care research and trends to ensure that coaches and educators always have the latest knowledge to support athletes. For example, it regularly updates and provides educational resources that include the latest research findings and practical advice. The Education Department also holds workshops and seminars to help coaches and educators improve their skills in athlete mental health care. Through these efforts, the Education Department provides coaches and educators with the knowledge and skills to effectively provide athlete mental health care, contributing to improved athlete performance.

[0034] The evaluation unit can periodically assess the athlete's mental state. For example, the evaluation unit can assess the athlete's mental state daily. For example, the evaluation unit can assess the athlete's mental state weekly. For example, the evaluation unit can assess the athlete's mental state monthly. This enables continuous mental health care by periodically assessing the athlete's mental state. Periodic assessments include, but are not limited to, daily, weekly, and monthly assessments. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input prompts to a generating AI to evaluate the athlete's mental state, and the generating AI can evaluate the mental state.

[0035] The support department can provide chat and voice support 24 hours a day, 365 days a year. The support department can provide support on a shift basis, for example, 24 hours a day, 365 days a year. The support department can also provide support using an AI chatbot, for example, 24 hours a day, 365 days a year. The support department can also provide support using platforms such as text chat, video calls, and voice calls, for example, 24 hours a day, 365 days a year. This creates an environment where athletes can consult at any time by providing chat and voice support 24 hours a day, 365 days a year. 24 / 7 support includes, but is not limited to, shift work and the use of an AI chatbot, for example. 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 athlete's consultation content into a generating AI, which can then provide appropriate advice.

[0036] The relaxation department can provide meditation and mindfulness programs. For example, the relaxation department can provide guided meditation. The relaxation department can also provide mindfulness meditation. The relaxation department can also provide mindfulness programs such as breathing exercises and body scans. This promotes mental and physical relaxation of athletes by providing meditation and mindfulness programs. Meditation includes, but is not limited to, guided meditation and mindfulness meditation. Some or all of the above processes in the relaxation department may be performed using, for example, AI, or not using AI. For example, the relaxation department can input the athlete's psychological state into a generating AI, which can then provide an appropriate relaxation program.

[0037] The Ministry of Education may provide educational resources on mental health care. For example, the Ministry of Education may provide video materials. The Ministry of Education may also provide online courses. The Ministry of Education may also provide workshops. This will raise awareness among coaches and educators by providing educational resources on mental health care. Educational resources include, but are not limited to, video materials, online courses, and workshops. Some or all of the above processes by the Ministry of Education may be carried out using AI, for example, or not using AI. For example, the Ministry of Education may input the needs of coaches and educators into a generative AI, which can then provide appropriate educational resources.

[0038] The evaluation unit can analyze an athlete's past psychological evaluation data and select the optimal evaluation method. For example, the evaluation unit can identify the most effective evaluation method from the athlete's past evaluation data and reflect it in future evaluations. The evaluation unit can also track changes in the athlete's psychological state and modify the evaluation method as needed. For example, the evaluation unit can provide individually customized evaluation methods based on the athlete's past evaluation data. This allows for the selection of the optimal evaluation method and improvement of evaluation accuracy by analyzing past psychological evaluation data. Past psychological evaluation data includes, but is not limited to, the history of evaluation results and data storage methods. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the athlete's past evaluation data into a generating AI, which can then select the optimal evaluation method.

[0039] The evaluation unit can customize evaluation items during psychological evaluation based on the athlete's current training status and competition schedule. For example, the evaluation unit can add evaluation items related to stress and fatigue during periods of high training load for the athlete. For example, the evaluation unit can focus on evaluating items related to tension and pressure before a competition. The evaluation unit can also dynamically change evaluation items and provide appropriate feedback according to the progress of training. This allows for more appropriate evaluations by customizing evaluation items based on training status and competition schedule. Training status includes, but is not limited to, training type, intensity, and frequency. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the athlete's training status and competition schedule into a generating AI, which can then customize the evaluation items.

[0040] The evaluation unit can prioritize evaluating highly relevant evaluation items by considering the athlete's geographical location information during psychological evaluation. For example, if the athlete is at a competition venue, the evaluation unit will prioritize evaluation items related to the competition. For example, if the athlete is at a training facility, the evaluation unit can also prioritize evaluation items related to training. For example, if the athlete is at home, the evaluation unit can also prioritize evaluation items related to relaxation and rest. This allows for the prioritization of highly relevant evaluation items by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the athlete's geographical location information into a generating AI, which can then select highly relevant evaluation items.

[0041] The evaluation unit can analyze an athlete's social media activity during psychological evaluation and assess their related psychological state. For example, the evaluation unit can detect signs of stress or anxiety from an athlete's social media posts and reflect them in the evaluation. The evaluation unit can also analyze the frequency and content of an athlete's social media activity and assess their psychological state. For example, the evaluation unit can assess social stress and support levels based on an athlete's social media interactions. This allows for a more accurate assessment of an athlete's psychological state by analyzing their social media activity. Social media activity includes, but is not limited to, posts, comments, and the number of likes. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input an athlete's social media activity data into a generating AI, which can then evaluate the athlete's psychological state.

[0042] The support unit can apply different support algorithms depending on the athlete's category when providing support. For example, the support unit can provide support specifically focused on improving athletic performance for professional athletes. For example, the support unit can also provide support related to balancing academics and athletics for student athletes. For example, the support unit can also provide support related to maintaining health and improving motivation for amateur athletes. By applying different support algorithms depending on the category, more appropriate support can be provided. Athlete categories include, but are not limited to, sports discipline and competition level. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input athlete category data into a generating AI, which can then select an appropriate support algorithm.

[0043] The support unit can prioritize support based on the athlete's stress level when providing support. For example, if an athlete's stress level is high, the support unit may prioritize support related to stress reduction. If an athlete's stress level is low, the support unit may also provide support related to performance improvement. If an athlete's stress level is moderate, the support unit may also provide balanced support. This allows for more effective support by prioritizing support based on stress level. Stress levels include, but are not limited to, stress tests and self-reports. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the athlete's stress level data into a generating AI, which can then determine the support priorities.

[0044] The support unit can adjust the order of support based on the athlete's relevance when providing support. For example, the support unit can prioritize providing the most relevant support based on the athlete's current training status. The support unit can also prioritize providing support needed before and after matches based on the athlete's match schedule. The support unit can also prioritize providing the most effective support based on the athlete's psychological state. By adjusting the order of support based on relevance, more effective support can be provided. The athlete's relevance includes, but is not limited to, the importance of the competition and personal goals. 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 athlete relevance data into a generating AI, which can then adjust the order of support.

[0045] The relaxation department can adjust the level of detail in a relaxation program based on the athlete's psychological state when providing the program. For example, if the athlete is experiencing high stress, the relaxation department can provide a detailed relaxation program. If the athlete is relaxed, the relaxation department can also provide a concise relaxation program. If the athlete's psychological state is unstable, the relaxation department can adjust the level of detail in the program as needed. This allows for the provision of a more appropriate relaxation program by adjusting the level of detail based on the athlete's psychological state. The level of detail in the program includes, but is not limited to, the depth and specificity of information. Some or all of the above processing in the relaxation department may be performed using AI, for example, or without AI. For example, the relaxation department can input the athlete's psychological state data into a generating AI, which can then adjust the level of detail in the program.

[0046] The Relaxation Department can apply different relaxation programs depending on the athlete's category when providing them. For example, the Relaxation Department can provide a relaxation program specifically designed to improve athletic performance for professional athletes. For example, the Relaxation Department can also provide a relaxation program for student athletes that focuses on balancing academics and athletics. For example, the Relaxation Department can also provide a relaxation program for amateur athletes that focuses on maintaining health and improving motivation. By applying different relaxation programs according to category, it is possible to provide more appropriate relaxation. Athlete categories include, for example, sport and competition level, but are not limited to these examples. Some or all of the above processing in the Relaxation Department may be performed using, for example, AI, or not using AI. For example, the Relaxation Department can input athlete category data into a generating AI, and the generating AI can select an appropriate relaxation program.

[0047] The relaxation department can prioritize relaxation programs based on the athlete's stress level when providing them. For example, if an athlete's stress level is high, the relaxation department can prioritize providing relaxation programs specifically designed for stress reduction. If an athlete's stress level is low, the relaxation department can also provide relaxation programs specifically designed for performance improvement. If an athlete's stress level is moderate, the relaxation department can also provide a balanced relaxation program. By prioritizing programs based on stress level, more effective relaxation can be provided. Stress level includes, but is not limited to, stress tests and self-reports. Some or all of the above processing in the relaxation department may be performed using, for example, AI, or not. For example, the relaxation department can input the athlete's stress level data into a generating AI, which can then determine the program priorities.

[0048] The relaxation department can adjust the order of relaxation programs based on the athlete's relevance when providing them. For example, the relaxation department can prioritize providing the most relevant relaxation programs based on the athlete's current training status. The relaxation department can also prioritize providing relaxation programs needed before and after competitions based on the athlete's competition schedule. The relaxation department can also prioritize providing the most effective relaxation programs based on the athlete's psychological state. By adjusting the order of programs based on relevance, more effective relaxation can be provided. The athlete's relevance includes, but is not limited to, the importance of the competition and personal goals. Some or all of the above processing in the relaxation department may be performed using AI, for example, or without AI. For example, the relaxation department can input athlete relevance data into a generating AI, which can then adjust the order of the programs.

[0049] The Ministry of Education can provide the most suitable educational resources by referring to the past educational history of coaches and educators when providing educational resources. For example, the Ministry of Education can identify and provide the most effective educational resources based on the past educational history of coaches and educators. The Ministry of Education can also provide individually customized educational resources based on the educational history of coaches and educators. For example, the Ministry of Education can analyze the educational history of coaches and educators and provide the most suitable resources according to the progress of their education. This allows for the provision of optimal educational resources by referring to past educational history. Past educational history includes, but is not limited to, courses taken and qualifications obtained. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input coach and educator educational history data into a generating AI, which can then select the most suitable educational resources.

[0050] The Ministry of Education can apply different educational resources depending on the category of the coach or educator when providing them. For example, the Ministry of Education can provide coaches of professional athletes with educational resources specifically focused on improving athletic performance. For example, the Ministry of Education can also provide educators of student athletes with educational resources on balancing academics and athletics. For example, the Ministry of Education can also provide coaches of amateur athletes with educational resources on maintaining health and improving motivation. By applying different educational resources according to the category, more appropriate educational resources can be provided. The categories of coaches and educators include, but are not limited to, coaching experience and areas of expertise. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input coach and educator category data into a generating AI, which can then select appropriate educational resources.

[0051] The Ministry of Education can provide optimal educational resources by considering the geographical location information of coaches and educators when providing educational resources. For example, the Ministry of Education can provide optimal educational resources according to the characteristics of the region where the coaches and educators are located. For example, the Ministry of Education can also provide educational resources tailored to the local culture and customs based on geographical location information. For example, the Ministry of Education can provide educational resources that meet the needs of the region by considering the geographical location information of coaches and educators. In this way, optimal educational resources can be provided by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input geographical location information data of coaches and educators into a generating AI, and the generating AI can select the optimal educational resources.

[0052] The Ministry of Education can analyze the social media activities of coaches and educators when providing educational resources and provide relevant resources. For example, the Ministry of Education can detect educational needs from coaches' and educators' social media posts and provide resources. The Ministry of Education can also analyze the frequency and content of coaches' and educators' social media activities and provide optimal educational resources. The Ministry of Education can also provide educational resources based on coaches' and educators' social media interactions. By analyzing social media activities, the Ministry can provide more appropriate educational resources. Social media activities include, but are not limited to, posts, comments, and the number of likes. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input coaches' and educators' social media activity data into a generating AI, which can then select relevant educational resources.

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

[0054] The evaluation department can utilize athlete biometric data when assessing an athlete's psychological state. For example, it can collect physiological data such as heart rate, skin electrical activity, and respiratory patterns and incorporate them into the psychological state assessment. This allows for a more accurate understanding of the athlete's psychological state and the provision of appropriate feedback. The evaluation department can also monitor athlete biometric data over the long term and track changes in their psychological state. Furthermore, the evaluation department can use biometric data to assess an athlete's stress level and fatigue level in real time and provide immediate support as needed.

[0055] The support department can create personalized support plans based on the athlete's psychological state. For example, it can provide individually customized support plans based on the athlete's past consultation history and psychological evaluation data. This ensures that the athlete receives the support best suited to their needs. The support department can also dynamically adjust the support plan in response to changes in the athlete's psychological state. Furthermore, the support department can provide short-term and long-term support plans according to the athlete's goals and needs.

[0056] The Relaxation Department can adjust the timing of relaxation programs based on the athlete's psychological state. For example, if an athlete is experiencing high levels of stress, a relaxation program can be implemented after training or before a competition. This allows the athlete to relax at the appropriate time, reducing their psychological burden. The Relaxation Department can also adjust the frequency of relaxation programs in response to changes in the athlete's psychological state. Furthermore, the Relaxation Department can customize the content of relaxation programs to meet the individual needs of each athlete.

[0057] The Ministry of Education can adjust the way educational resources are provided based on the athlete's psychological state. For example, if an athlete is experiencing high levels of stress, video materials on stress management can be provided. This allows the athlete to learn at their own pace and reduce their psychological burden. The Ministry of Education can also adjust the frequency of educational resource provision in response to changes in the athlete's psychological state. Furthermore, the Ministry of Education can customize the content of educational resources to meet the individual needs of the athlete.

[0058] The evaluation department can utilize athlete sleep data when assessing their psychological state. For example, it can monitor athletes' sleep patterns and sleep quality and incorporate this into the assessment of their psychological state. This allows for a more accurate understanding of the athlete's psychological state and the provision of appropriate feedback. The evaluation department can also monitor athletes' sleep data over the long term and track changes in their psychological state. Furthermore, the evaluation department can use sleep data to assess athletes' stress levels and fatigue in real time and provide immediate support as needed.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The evaluation department assesses the athlete's psychological state. For example, they assess the athlete's stress level and psychological health, and evaluate the stress and anxiety they experience during daily training and after competitions. Based on the results, they provide appropriate feedback and specific advice for stress management. Step 2: The support department provides real-time support based on the psychological state assessed by the evaluation department. For example, they provide 24 / 7 chat and voice support, creating an environment where athletes can seek help anytime they feel stressed or have mental health concerns. They also provide appropriate advice and relaxation techniques when athletes feel nervous or pressured before a match. Step 3: The Relaxation Department provides relaxation programs based on the support provided by the Support Department. For example, they offer meditation and mindfulness programs, and implement relaxation programs for post-match recovery and during breaks in daily training. They provide specific relaxation methods to restore balance to mind and body. Step 4: The Education Department provides educational resources for coaches and educators based on the relaxation programs provided by the Relaxation Department. For example, it provides educational resources on mental health care, informing coaches and educators about the importance of mental health care and specific care methods. It provides educational resources to make athlete mental health care more effective.

[0061] (Example of form 2) The AI ​​agent service according to an embodiment of the present invention is a system that provides 24 / 7 support to enable athletes and student athletes to care for their mental health. This system provides the functions of psychological assessment, real-time support, relaxation programs, and educational resources. For example, the psychological assessment function periodically evaluates the athlete's psychological state and provides feedback for stress management. Next, the real-time support function provides 24 / 7 support via chat and voice, creating an environment where athletes can consult whenever they feel stressed. Furthermore, the relaxation program function provides meditation and mindfulness programs to promote mental and physical relaxation. It also provides educational resources on mental health care for coaches and educators to raise awareness. For example, let's describe the psychological assessment function. The AI ​​agent periodically evaluates the athlete's psychological state and understands their stress level and psychological health status. For example, it evaluates the stress and anxiety that athletes feel after daily training or matches and provides appropriate feedback based on the results. This allows athletes to objectively understand their own psychological state and receive specific advice for stress management. Next, let's describe the real-time support function. The AI ​​agent provides 24 / 7 support via chat and voice. The goal is to create an environment where athletes can seek advice anytime they feel stressed or have mental health concerns. For example, when feeling nervous or pressured before a match, they can consult an AI agent to receive appropriate advice and relaxation techniques. Furthermore, the relaxation program function will be explained. The AI ​​agent will provide meditation and mindfulness programs to promote relaxation of the athlete's mind and body. For example, by implementing the relaxation program during post-match recovery or between daily training sessions, athletes can restore balance to their mind and body. This allows athletes to compete in a relaxed state, which can lead to improved performance. In addition, educational resources on mental health care will be provided for coaches and educators.The AI ​​agent provides coaches and educators with information on the importance of mental health care and specific care methods, raising awareness. This will lead to more effective mental health care for athletes. Thus, the AI ​​agent service of this invention provides 24 / 7 support to enable athletes and student athletes to care for their mental health, and comprehensively supports athletes' mental health through functions such as psychological assessment, real-time support, relaxation programs, and coach / educational support. In this way, the AI ​​agent service can comprehensively support the mental health of athletes.

[0062] The AI ​​agent service according to this embodiment comprises an evaluation unit, a support unit, a relaxation unit, and an education unit. The evaluation unit evaluates the psychological state of the athlete. The evaluation unit, for example, grasps the athlete's stress level and psychological health status. The evaluation unit, for example, evaluates the stress and anxiety that the athlete feels after daily training or matches, and provides appropriate feedback based on the results. The evaluation unit, for example, provides specific advice to the athlete for stress management based on the results of the psychological evaluation. The support unit provides support in real time based on the psychological state evaluated by the evaluation unit. The support unit, for example, provides 24 / 7 chat and voice support. The support unit, for example, creates an environment where athletes can consult anytime they feel stressed or have concerns about their mental health. The support unit, for example, provides appropriate advice and relaxation methods when the athlete feels tension or pressure before a match. The relaxation unit provides relaxation programs based on the support provided by the support unit. The relaxation unit, for example, provides meditation and mindfulness programs. The relaxation unit implements relaxation programs, for example, during post-match recovery or in between daily training sessions. The Relaxation Department provides, for example, specific relaxation methods to restore balance to mind and body. The Education Department provides educational resources for coaches and educators based on the relaxation programs provided by the Relaxation Department. The Education Department provides, for example, educational resources on mental health care. The Education Department provides, for example, information to coaches and educators on the importance of mental health care and specific care methods. The Education Department provides educational resources to make athlete mental health care more effective. As a result, the AI ​​agent service according to the embodiment can comprehensively support the mental health of athletes.

[0063] The evaluation department assesses the psychological state of athletes. For example, it understands athletes' stress levels and psychological health. Specifically, the evaluation department collects feedback from athletes during their daily training and after competitions, and analyzes this data using AI. The AI ​​integrates diverse data such as the athlete's heart rate, sleep patterns, dietary records, and self-reported stress levels to conduct a comprehensive psychological assessment. For example, it provides a detailed assessment of the stress athletes experience during training and anxiety after competitions, and provides individualized feedback based on the results. Based on the results of the psychological assessment, the evaluation department provides athletes with specific advice for stress management. For example, if an athlete shows a high stress level, the evaluation department proposes relaxation methods and specific action plans for mental health care. This allows athletes to objectively understand their own psychological state and take appropriate measures. Furthermore, the evaluation department conducts regular psychological assessments and continuously monitors changes in athletes' mental health. This enables long-term mental health care and contributes to improving athletes' performance.

[0064] The support department provides real-time support based on the psychological state assessed by the evaluation department. For example, the support department offers 24 / 7 support via chat and voice. Specifically, it creates an environment where athletes can seek advice anytime they feel stressed or have mental health concerns. The support department utilizes AI to analyze athletes' consultations and provide appropriate advice and relaxation methods. For instance, when an athlete feels tension or pressure before a match, the AI ​​provides optimal relaxation methods and mental health care advice based on the athlete's past data and current psychological state. The support department also leverages an extensive database to quickly provide athletes with the information and resources they need. This ensures athletes receive the necessary support at any time, helping them maintain their mental health. Furthermore, the support department collects athlete feedback to continuously improve the quality of support provided. For example, by evaluating the effectiveness of the support received and reviewing the support content based on the results, more effective support can be provided. In this way, the support department comprehensively supports athletes' mental health and contributes to performance improvement.

[0065] The Relaxation Department provides relaxation programs based on the support provided by the Support Department. For example, the Relaxation Department offers meditation and mindfulness programs. Specifically, the Relaxation Department designs individually customized relaxation programs according to the athlete's psychological state and stress level. For example, relaxation programs are implemented during post-competition recovery or between daily training sessions. The Relaxation Department provides specific relaxation methods to help athletes restore mental and physical balance. For example, it creates a relaxing environment for athletes using deep breathing exercises, yoga, and relaxation music. The Relaxation Department also provides guided sessions and video tutorials to help athletes effectively implement the relaxation programs. This allows athletes to implement the programs anywhere, such as at home or at training facilities. Furthermore, the Relaxation Department continuously improves the content of the relaxation programs based on athlete feedback. For example, if an athlete finds a particular relaxation method highly effective, that method can be incorporated into the program to provide a more effective relaxation program. In this way, the Relaxation Department helps athletes restore mental and physical balance and contributes to improved performance.

[0066] The Education Department provides educational resources for coaches and educators based on relaxation programs offered by the Relaxation Department. For example, the Education Department provides educational resources on mental health care. Specifically, it provides coaches and educators with information on the importance of mental health care and specific care methods. For instance, it provides educational resources to help athletes' mental health care be more effective. The Education Department provides training on the effectiveness and implementation methods of relaxation programs to help coaches and educators provide appropriate support to athletes. Furthermore, the Education Department provides information based on the latest mental health care research and trends to ensure that coaches and educators always have the latest knowledge to support athletes. For example, it regularly updates and provides educational resources that include the latest research findings and practical advice. The Education Department also holds workshops and seminars to help coaches and educators improve their skills in athlete mental health care. Through these efforts, the Education Department provides coaches and educators with the knowledge and skills to effectively provide athlete mental health care, contributing to improved athlete performance.

[0067] The evaluation unit can periodically assess the athlete's mental state. For example, the evaluation unit can assess the athlete's mental state daily. For example, the evaluation unit can assess the athlete's mental state weekly. For example, the evaluation unit can assess the athlete's mental state monthly. This enables continuous mental health care by periodically assessing the athlete's mental state. Periodic assessments include, but are not limited to, daily, weekly, and monthly assessments. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input prompts to a generating AI to evaluate the athlete's mental state, and the generating AI can evaluate the mental state.

[0068] The support department can provide chat and voice support 24 hours a day, 365 days a year. The support department can provide support on a shift basis, for example, 24 hours a day, 365 days a year. The support department can also provide support using an AI chatbot, for example, 24 hours a day, 365 days a year. The support department can also provide support using platforms such as text chat, video calls, and voice calls, for example, 24 hours a day, 365 days a year. This creates an environment where athletes can consult at any time by providing chat and voice support 24 hours a day, 365 days a year. 24 / 7 support includes, but is not limited to, shift work and the use of an AI chatbot, for example. 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 athlete's consultation content into a generating AI, which can then provide appropriate advice.

[0069] The relaxation department can provide meditation and mindfulness programs. For example, the relaxation department can provide guided meditation. The relaxation department can also provide mindfulness meditation. The relaxation department can also provide mindfulness programs such as breathing exercises and body scans. This promotes mental and physical relaxation of athletes by providing meditation and mindfulness programs. Meditation includes, but is not limited to, guided meditation and mindfulness meditation. Some or all of the above processes in the relaxation department may be performed using, for example, AI, or not using AI. For example, the relaxation department can input the athlete's psychological state into a generating AI, which can then provide an appropriate relaxation program.

[0070] The Ministry of Education may provide educational resources on mental health care. For example, the Ministry of Education may provide video materials. The Ministry of Education may also provide online courses. The Ministry of Education may also provide workshops. This will raise awareness among coaches and educators by providing educational resources on mental health care. Educational resources include, but are not limited to, video materials, online courses, and workshops. Some or all of the above processes by the Ministry of Education may be carried out using AI, for example, or not using AI. For example, the Ministry of Education may input the needs of coaches and educators into a generative AI, which can then provide appropriate educational resources.

[0071] The evaluation unit can estimate the athlete's emotions and adjust the frequency of psychological evaluations based on the estimated emotions. For example, if the athlete is experiencing high stress, the evaluation unit can increase the frequency of psychological evaluations and provide more detailed feedback. For example, if the athlete is relaxed, the evaluation unit can also decrease the frequency of psychological evaluations and perform evaluations only when necessary. For example, if the athlete's emotions are unstable, the evaluation unit can dynamically adjust the frequency of evaluations in accordance with emotional fluctuations. This allows for evaluations to be performed at more appropriate times by adjusting the frequency of psychological evaluations according to the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input the athlete's emotional data into a generative AI, the generative AI can estimate the emotions, and the frequency of psychological evaluations can be adjusted based on the results.

[0072] The evaluation unit can analyze an athlete's past psychological evaluation data and select the optimal evaluation method. For example, the evaluation unit can identify the most effective evaluation method from the athlete's past evaluation data and reflect it in future evaluations. The evaluation unit can also track changes in the athlete's psychological state and modify the evaluation method as needed. For example, the evaluation unit can provide individually customized evaluation methods based on the athlete's past evaluation data. This allows for the selection of the optimal evaluation method and improvement of evaluation accuracy by analyzing past psychological evaluation data. Past psychological evaluation data includes, but is not limited to, the history of evaluation results and data storage methods. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the athlete's past evaluation data into a generating AI, which can then select the optimal evaluation method.

[0073] The evaluation unit can customize evaluation items during psychological evaluation based on the athlete's current training status and competition schedule. For example, the evaluation unit can add evaluation items related to stress and fatigue during periods of high training load for the athlete. For example, the evaluation unit can focus on evaluating items related to tension and pressure before a competition. The evaluation unit can also dynamically change evaluation items and provide appropriate feedback according to the progress of training. This allows for more appropriate evaluations by customizing evaluation items based on training status and competition schedule. Training status includes, but is not limited to, training type, intensity, and frequency. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the athlete's training status and competition schedule into a generating AI, which can then customize the evaluation items.

[0074] The evaluation unit can estimate the athlete's emotions and determine the priority of evaluation results based on the estimated emotions. For example, if the athlete is experiencing high stress, the evaluation unit will prioritize displaying stress-related evaluation results. For example, if the athlete is relaxed, the evaluation unit may also display evaluation results for the overall psychological state. For example, if the athlete's emotions are unstable, the evaluation unit may also prioritize displaying evaluation results related to emotional fluctuations. This allows for the prioritization of important evaluation results by determining the priority of evaluation results based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the athlete's emotional data into a generative AI, the generative AI can estimate the emotions, and the evaluation unit can determine the priority of evaluation results based on the results.

[0075] The evaluation unit can prioritize evaluating highly relevant evaluation items by considering the athlete's geographical location information during psychological evaluation. For example, if the athlete is at a competition venue, the evaluation unit will prioritize evaluation items related to the competition. For example, if the athlete is at a training facility, the evaluation unit can also prioritize evaluation items related to training. For example, if the athlete is at home, the evaluation unit can also prioritize evaluation items related to relaxation and rest. This allows for the prioritization of highly relevant evaluation items by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the athlete's geographical location information into a generating AI, which can then select highly relevant evaluation items.

[0076] The evaluation unit can analyze an athlete's social media activity during psychological evaluation and assess their related psychological state. For example, the evaluation unit can detect signs of stress or anxiety from an athlete's social media posts and reflect them in the evaluation. The evaluation unit can also analyze the frequency and content of an athlete's social media activity and assess their psychological state. For example, the evaluation unit can assess social stress and support levels based on an athlete's social media interactions. This allows for a more accurate assessment of an athlete's psychological state by analyzing their social media activity. Social media activity includes, but is not limited to, posts, comments, and the number of likes. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input an athlete's social media activity data into a generating AI, which can then evaluate the athlete's psychological state.

[0077] The support unit can estimate the athlete's emotions and adjust the way it expresses support based on the estimated emotions. For example, if the athlete is stressed, the support unit can provide support using gentle language. For example, if the athlete is relaxed, the support unit can also provide support in a friendly tone. For example, if the athlete is nervous, the support unit can also provide support in a calm tone. This allows for more appropriate support to be provided by adjusting the way support is expressed based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input the athlete's emotion data into a generative AI, the generative AI can estimate the emotions, and the way support is expressed can be adjusted based on the result.

[0078] The support unit can adjust the level of detail of support provided based on the athlete's psychological state. For example, if the athlete is experiencing high stress, the support unit can provide detailed support information. If the athlete is relaxed, the support unit can also provide concise support information. If the athlete's psychological state is unstable, the support unit can adjust the level of detail of support according to the situation. This allows for the provision of more appropriate support by adjusting the level of detail of support based on the athlete's psychological state. The level of detail of support includes, but is not limited to, the depth and specificity of information. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the athlete's psychological state data into a generating AI, which can then adjust the level of detail of support.

[0079] The support unit can apply different support algorithms depending on the athlete's category when providing support. For example, the support unit can provide support specifically focused on improving athletic performance for professional athletes. For example, the support unit can also provide support related to balancing academics and athletics for student athletes. For example, the support unit can also provide support related to maintaining health and improving motivation for amateur athletes. By applying different support algorithms depending on the category, more appropriate support can be provided. Athlete categories include, but are not limited to, sports discipline and competition level. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input athlete category data into a generating AI, which can then select an appropriate support algorithm.

[0080] The support unit can estimate the athlete's emotions and adjust the length of support based on the estimated emotions. For example, if the athlete is stressed, the support unit can provide a longer support session. For example, if the athlete is relaxed, the support unit can also provide a shorter support session. For example, if the athlete's emotions are unstable, the support unit can adjust the length of support according to the fluctuations in emotions. This allows for more appropriate support to be provided by adjusting the length of support based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the athlete's emotion data into a generative AI, the generative AI can estimate the emotions, and the length of support can be adjusted based on the result.

[0081] The support unit can prioritize support based on the athlete's stress level when providing support. For example, if an athlete's stress level is high, the support unit may prioritize support related to stress reduction. If an athlete's stress level is low, the support unit may also provide support related to performance improvement. If an athlete's stress level is moderate, the support unit may also provide balanced support. This allows for more effective support by prioritizing support based on stress level. Stress levels include, but are not limited to, stress tests and self-reports. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can input the athlete's stress level data into a generating AI, which can then determine the support priorities.

[0082] The support unit can adjust the order of support based on the athlete's relevance when providing support. For example, the support unit can prioritize providing the most relevant support based on the athlete's current training status. The support unit can also prioritize providing support needed before and after matches based on the athlete's match schedule. The support unit can also prioritize providing the most effective support based on the athlete's psychological state. By adjusting the order of support based on relevance, more effective support can be provided. The athlete's relevance includes, but is not limited to, the importance of the competition and personal goals. 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 athlete relevance data into a generating AI, which can then adjust the order of support.

[0083] The relaxation unit can estimate the athlete's emotions and adjust the content of the relaxation program based on the estimated emotions. For example, if the athlete is stressed, the relaxation unit can provide a highly relaxing meditation program. If the athlete is relaxed, the relaxation unit can also provide light stretching or breathing exercises. If the athlete's emotions are unstable, the relaxation unit can also adjust the content of the relaxation program according to the fluctuations in emotions. By adjusting the content of the relaxation program based on the athlete's emotions, a more effective relaxation 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 relaxation unit may be performed using AI, for example, or without AI. For example, the relaxation unit can input the athlete's emotion data into a generative AI, the generative AI can estimate the emotions, and the content of the relaxation program can be adjusted based on the results.

[0084] The relaxation department can adjust the level of detail in a relaxation program based on the athlete's psychological state when providing the program. For example, if the athlete is experiencing high stress, the relaxation department can provide a detailed relaxation program. If the athlete is relaxed, the relaxation department can also provide a concise relaxation program. If the athlete's psychological state is unstable, the relaxation department can adjust the level of detail in the program as needed. This allows for the provision of a more appropriate relaxation program by adjusting the level of detail based on the athlete's psychological state. The level of detail in the program includes, but is not limited to, the depth and specificity of information. Some or all of the above processing in the relaxation department may be performed using AI, for example, or without AI. For example, the relaxation department can input the athlete's psychological state data into a generating AI, which can then adjust the level of detail in the program.

[0085] The Relaxation Department can apply different relaxation programs depending on the athlete's category when providing them. For example, the Relaxation Department can provide a relaxation program specifically designed to improve athletic performance for professional athletes. For example, the Relaxation Department can also provide a relaxation program for student athletes that focuses on balancing academics and athletics. For example, the Relaxation Department can also provide a relaxation program for amateur athletes that focuses on maintaining health and improving motivation. By applying different relaxation programs according to category, it is possible to provide more appropriate relaxation. Athlete categories include, for example, sport and competition level, but are not limited to these examples. Some or all of the above processing in the Relaxation Department may be performed using, for example, AI, or not using AI. For example, the Relaxation Department can input athlete category data into a generating AI, and the generating AI can select an appropriate relaxation program.

[0086] The relaxation unit can estimate the athlete's emotions and adjust the length of the relaxation program based on the estimated emotions. For example, if the athlete is stressed, the relaxation unit can provide a longer relaxation program. For example, if the athlete is relaxed, the relaxation unit can also provide a shorter relaxation program. For example, if the athlete's emotions are unstable, the relaxation unit can adjust the length of the relaxation program according to the fluctuations in emotions. This allows for more effective relaxation by adjusting the length of the relaxation program based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the relaxation unit may be performed using AI or not using AI. For example, the relaxation unit can input the athlete's emotion data into a generative AI, the generative AI can estimate the emotions, and the length of the relaxation program can be adjusted based on the result.

[0087] The relaxation department can prioritize relaxation programs based on the athlete's stress level when providing them. For example, if an athlete's stress level is high, the relaxation department can prioritize providing relaxation programs specifically designed for stress reduction. If an athlete's stress level is low, the relaxation department can also provide relaxation programs specifically designed for performance improvement. If an athlete's stress level is moderate, the relaxation department can also provide a balanced relaxation program. By prioritizing programs based on stress level, more effective relaxation can be provided. Stress level includes, but is not limited to, stress tests and self-reports. Some or all of the above processing in the relaxation department may be performed using, for example, AI, or not. For example, the relaxation department can input the athlete's stress level data into a generating AI, which can then determine the program priorities.

[0088] The relaxation department can adjust the order of relaxation programs based on the athlete's relevance when providing them. For example, the relaxation department can prioritize providing the most relevant relaxation programs based on the athlete's current training status. The relaxation department can also prioritize providing relaxation programs needed before and after competitions based on the athlete's competition schedule. The relaxation department can also prioritize providing the most effective relaxation programs based on the athlete's psychological state. By adjusting the order of programs based on relevance, more effective relaxation can be provided. The athlete's relevance includes, but is not limited to, the importance of the competition and personal goals. Some or all of the above processing in the relaxation department may be performed using AI, for example, or without AI. For example, the relaxation department can input athlete relevance data into a generating AI, which can then adjust the order of the programs.

[0089] The Ministry of Education can estimate an athlete's emotions and adjust the content of educational resources based on the estimated emotions. For example, if an athlete is stressed, the Ministry of Education can provide educational resources on stress management. If an athlete is relaxed, the Ministry of Education can also provide educational resources on performance improvement. If an athlete's emotions are unstable, the Ministry of Education can adjust the content of educational resources according to emotional fluctuations. By adjusting the content of educational resources based on the athlete's emotions, more appropriate educational resources 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 by the Ministry of Education may be performed using AI or not using AI. For example, the Ministry of Education can input athlete emotion data into a generative AI, the generative AI can estimate emotions, and the content of educational resources can be adjusted based on the results.

[0090] The Ministry of Education can provide the most suitable educational resources by referring to the past educational history of coaches and educators when providing educational resources. For example, the Ministry of Education can identify and provide the most effective educational resources based on the past educational history of coaches and educators. The Ministry of Education can also provide individually customized educational resources based on the educational history of coaches and educators. For example, the Ministry of Education can analyze the educational history of coaches and educators and provide the most suitable resources according to the progress of their education. This allows for the provision of optimal educational resources by referring to past educational history. Past educational history includes, but is not limited to, courses taken and qualifications obtained. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input coach and educator educational history data into a generating AI, which can then select the most suitable educational resources.

[0091] The Ministry of Education can apply different educational resources depending on the category of the coach or educator when providing them. For example, the Ministry of Education can provide coaches of professional athletes with educational resources specifically focused on improving athletic performance. For example, the Ministry of Education can also provide educators of student athletes with educational resources on balancing academics and athletics. For example, the Ministry of Education can also provide coaches of amateur athletes with educational resources on maintaining health and improving motivation. By applying different educational resources according to the category, more appropriate educational resources can be provided. The categories of coaches and educators include, but are not limited to, coaching experience and areas of expertise. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input coach and educator category data into a generating AI, which can then select appropriate educational resources.

[0092] The Ministry of Education can estimate an athlete's emotions and prioritize educational resources based on the estimated emotions. For example, if an athlete is stressed, the Ministry of Education can prioritize providing educational resources related to stress reduction. If an athlete is relaxed, the Ministry of Education can also provide educational resources related to performance improvement. If an athlete's emotions are unstable, the Ministry of Education can adjust the priority of educational resources according to emotional fluctuations. This allows for the provision of more effective educational resources by prioritizing them based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input athlete emotion data into a generative AI, the generative AI can estimate emotions, and the Ministry of Education can prioritize educational resources based on the results.

[0093] The Ministry of Education can provide optimal educational resources by considering the geographical location information of coaches and educators when providing educational resources. For example, the Ministry of Education can provide optimal educational resources according to the characteristics of the region where the coaches and educators are located. For example, the Ministry of Education can also provide educational resources tailored to the local culture and customs based on geographical location information. For example, the Ministry of Education can provide educational resources that meet the needs of the region by considering the geographical location information of coaches and educators. In this way, optimal educational resources can be provided by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input geographical location information data of coaches and educators into a generating AI, and the generating AI can select the optimal educational resources.

[0094] The Ministry of Education can analyze the social media activities of coaches and educators when providing educational resources and provide relevant resources. For example, the Ministry of Education can detect educational needs from coaches' and educators' social media posts and provide resources. The Ministry of Education can also analyze the frequency and content of coaches' and educators' social media activities and provide optimal educational resources. The Ministry of Education can also provide educational resources based on coaches' and educators' social media interactions. By analyzing social media activities, the Ministry can provide more appropriate educational resources. Social media activities include, but are not limited to, posts, comments, and the number of likes. Some or all of the above processing by the Ministry of Education may be performed using AI, for example, or not. For example, the Ministry of Education can input coaches' and educators' social media activity data into a generating AI, which can then select relevant educational resources.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The evaluation department can utilize athlete biometric data when assessing an athlete's psychological state. For example, it can collect physiological data such as heart rate, skin electrical activity, and respiratory patterns and incorporate them into the psychological state assessment. This allows for a more accurate understanding of the athlete's psychological state and the provision of appropriate feedback. The evaluation department can also monitor athlete biometric data over the long term and track changes in their psychological state. Furthermore, the evaluation department can use biometric data to assess an athlete's stress level and fatigue level in real time and provide immediate support as needed.

[0097] The support department can create personalized support plans based on the athlete's psychological state. For example, it can provide individually customized support plans based on the athlete's past consultation history and psychological evaluation data. This ensures that the athlete receives the support best suited to their needs. The support department can also dynamically adjust the support plan in response to changes in the athlete's psychological state. Furthermore, the support department can provide short-term and long-term support plans according to the athlete's goals and needs.

[0098] The Relaxation Department can adjust the timing of relaxation programs based on the athlete's psychological state. For example, if an athlete is experiencing high levels of stress, a relaxation program can be implemented after training or before a competition. This allows the athlete to relax at the appropriate time, reducing their psychological burden. The Relaxation Department can also adjust the frequency of relaxation programs in response to changes in the athlete's psychological state. Furthermore, the Relaxation Department can customize the content of relaxation programs to meet the individual needs of each athlete.

[0099] The Ministry of Education can adjust the way educational resources are provided based on the athlete's psychological state. For example, if an athlete is experiencing high levels of stress, video materials on stress management can be provided. This allows the athlete to learn at their own pace and reduce their psychological burden. The Ministry of Education can also adjust the frequency of educational resource provision in response to changes in the athlete's psychological state. Furthermore, the Ministry of Education can customize the content of educational resources to meet the individual needs of the athlete.

[0100] The evaluation department can utilize athlete sleep data when assessing their psychological state. For example, it can monitor athletes' sleep patterns and sleep quality and incorporate this into the assessment of their psychological state. This allows for a more accurate understanding of the athlete's psychological state and the provision of appropriate feedback. The evaluation department can also monitor athletes' sleep data over the long term and track changes in their psychological state. Furthermore, the evaluation department can use sleep data to assess athletes' stress levels and fatigue in real time and provide immediate support as needed.

[0101] The evaluation unit can estimate the athlete's emotions and customize the content of the psychological evaluation based on the estimated emotions. For example, if an athlete is experiencing high stress, the evaluation unit will focus on stress-related evaluation items. This allows for an appropriate evaluation tailored to the athlete's emotions. The evaluation unit can also dynamically adjust the evaluation items in response to changes in the athlete's emotions. Furthermore, the evaluation unit can use the athlete's emotional data to provide individually customized evaluation feedback.

[0102] The support department can estimate the athlete's emotions and adjust the timing of support based on those estimates. For example, if an athlete is experiencing high levels of stress, support can be provided immediately. This ensures that the athlete receives support at the appropriate time, reducing their psychological burden. The support department can also adjust the frequency of support in response to changes in the athlete's emotions. Furthermore, the support department can customize the content of support to meet the individual needs of each athlete.

[0103] The relaxation department can estimate an athlete's emotions and adjust the implementation of the relaxation program based on those estimates. For example, if an athlete is experiencing high stress, it can provide a program with a high relaxation effect. This allows the athlete to relax in an appropriate way and reduce their psychological burden. The relaxation department can also adjust the frequency of the relaxation program in response to changes in the athlete's emotions. Furthermore, the relaxation department can customize the content of the relaxation program to meet the individual needs of the athlete.

[0104] The Ministry of Education can estimate an athlete's emotions and adjust the timing of educational resource provision based on those estimates. For example, if an athlete is experiencing high levels of stress, educational resources on stress management can be provided immediately. This allows the athlete to learn at the appropriate time and reduce their psychological burden. The Ministry of Education can also adjust the frequency of educational resource provision in response to changes in the athlete's emotions. Furthermore, the Ministry of Education can customize the content of educational resources to meet the individual needs of each athlete.

[0105] The evaluation unit can estimate the athlete's emotions and adjust the feedback method of the evaluation results based on the estimated emotions of the athlete. For example, if an athlete is experiencing high stress, feedback can be provided in gentle language. This allows the athlete to receive the evaluation results while reducing their psychological burden. The evaluation unit can also adjust the level of detail of the feedback in response to changes in the athlete's emotions. Furthermore, the evaluation unit can customize the content of the feedback according to the individual needs of the athlete.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The evaluation department assesses the athlete's psychological state. For example, they assess the athlete's stress level and psychological health, and evaluate the stress and anxiety they experience during daily training and after competitions. Based on the results, they provide appropriate feedback and specific advice for stress management. Step 2: The support department provides real-time support based on the psychological state assessed by the evaluation department. For example, they provide 24 / 7 chat and voice support, creating an environment where athletes can seek help anytime they feel stressed or have mental health concerns. They also provide appropriate advice and relaxation techniques when athletes feel nervous or pressured before a match. Step 3: The Relaxation Department provides relaxation programs based on the support provided by the Support Department. For example, they offer meditation and mindfulness programs, and implement relaxation programs for post-match recovery and during breaks in daily training. They provide specific relaxation methods to restore balance to mind and body. Step 4: The Education Department provides educational resources for coaches and educators based on the relaxation programs provided by the Relaxation Department. For example, it provides educational resources on mental health care, informing coaches and educators about the importance of mental health care and specific care methods. It provides educational resources to make athlete mental health care more effective.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the evaluation unit, support unit, relaxation unit, and education unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the evaluation unit uses the camera 42 and microphone 38B of the smart device 14 to evaluate the athlete's psychological state, and the control unit 46A grasps the stress level and psychological health status. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides real-time chat and voice support. The relaxation unit provides meditation and mindfulness programs by, for example, the control unit 46A of the smart device 14 to promote mental and physical relaxation. The education unit provides educational resources on mental health care by, for example, the specific processing unit 290 of the data processing unit 12, and provides information to coaches and educators. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the evaluation unit, support unit, relaxation unit, and education unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit uses the camera 42 and microphone 238 of the smart glasses 214 to evaluate the athlete's psychological state, and the control unit 46A grasps the stress level and psychological health status. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides real-time chat and voice support. The relaxation unit provides, for example, meditation and mindfulness programs by the control unit 46A of the smart glasses 214 to promote mental and physical relaxation. The education unit provides educational resources on mental health care by the specific processing unit 290 of the data processing unit 12, and provides information to coaches and educators. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the evaluation unit, support unit, relaxation unit, and education unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit uses the camera 42 and microphone 238 of the headset terminal 314 to evaluate the athlete's psychological state, and the control unit 46A grasps the stress level and psychological health status. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides real-time chat and voice support. The relaxation unit provides meditation and mindfulness programs by, for example, the control unit 46A of the headset terminal 314 to promote mental and physical relaxation. The education unit provides educational resources on mental health care by, for example, the specific processing unit 290 of the data processing unit 12, and provides information to coaches and educators. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In 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.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 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.

[0160] Each of the multiple elements described above, including the evaluation unit, support unit, relaxation unit, and education unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit uses the camera 42 and microphone 238 of the robot 414 to evaluate the athlete's psychological state, and the control unit 46A grasps the stress level and psychological health status. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides real-time chat and voice support. The relaxation unit provides meditation and mindfulness programs by, for example, the control unit 46A of the robot 414 to promote mental and physical relaxation. The education unit provides educational resources on mental health care by, for example, the specific processing unit 290 of the data processing unit 12, and provides information to coaches and educators. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) The evaluation department assesses the psychological state of athletes, A support unit that provides support in real time based on the psychological state evaluated by the aforementioned evaluation unit, A relaxation unit that provides a relaxation program based on the support provided by the aforementioned support unit, The Education Department provides educational resources for coaches and educators based on the relaxation programs provided by the Relaxation Department. A system characterized by the following features. (Note 2) The evaluation unit described above, Regularly assess the psychological state of athletes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is We provide 24 / 7 chat and voice support. The system described in Appendix 1, characterized by the features described herein. (Note 4) The relaxation section is, Offering meditation and mindfulness programs The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Ministry of Education, We provide educational resources on mental health care. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit described above, The system estimates the athlete's emotions and adjusts the frequency of psychological assessments based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The evaluation unit described above, We analyze past psychological evaluation data of athletes and select the optimal evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The evaluation unit described above, During psychological assessments, evaluation items are customized based on the athlete's current training status and competition schedule. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit described above, The system estimates the athlete's emotions and prioritizes evaluation results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit described above, During psychological assessments, the athlete's geographical location is taken into consideration, and highly relevant assessment items are prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit described above, During psychological assessment, we analyze the athlete's social media activity and evaluate the associated psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned support unit is The system estimates the athlete's emotions and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned support unit is When providing support, we adjust the level of detail based on the athlete's psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit is When providing support, different support algorithms are applied depending on the athlete's category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit is The system estimates the athlete's emotions and adjusts the length of support based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is When providing support, we prioritize support based on the athlete's stress level. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is When providing support, we adjust the order of support based on the athlete's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The relaxation section is, The system estimates the athlete's emotions and adjusts the content of the relaxation program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The relaxation section is, When providing relaxation programs, we adjust the level of detail in the program based on the athlete's psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 20) The relaxation section is, When providing relaxation programs, different programs will be applied depending on the athlete's category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The relaxation section is, The system estimates the athlete's emotions and adjusts the length of the relaxation program based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 22) The relaxation section is, When providing relaxation programs, we prioritize the programs based on the athlete's stress level. The system described in Appendix 1, characterized by the features described herein. (Note 23) The relaxation section is, When providing relaxation programs, the order of the programs is adjusted based on the athlete's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Ministry of Education, The system estimates the emotions of athletes and adjusts the content of educational resources based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Ministry of Education, When providing educational resources, we refer to the past teaching experience of coaches and educators to provide the most suitable resources. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Ministry of Education, When providing educational resources, apply different resources depending on the category of the coach or educator. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Ministry of Education, Estimate the emotions of athletes and prioritize educational resources based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Ministry of Education, When providing educational resources, we consider the geographical location of coaches and educators to provide the most suitable resources. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Ministry of Education, When providing educational resources, we analyze the social media activity of coaches and educators and provide relevant resources. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 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 evaluation department assesses the psychological state of athletes, A support unit that provides support in real time based on the psychological state evaluated by the aforementioned evaluation unit, A relaxation unit that provides a relaxation program based on the support provided by the aforementioned support unit, The Education Department provides educational resources for coaches and educators based on the relaxation programs provided by the Relaxation Department. A system characterized by the following features.

2. The evaluation unit, Regularly assess the psychological state of athletes. The system according to feature 1.

3. The aforementioned support unit is We provide 24 / 7 chat and voice support. The system according to feature 1.

4. The relaxation section is, Offering meditation and mindfulness programs The system according to feature 1.

5. The aforementioned Ministry of Education, We provide educational resources on mental health care. The system according to feature 1.

6. The evaluation unit, The system estimates the athlete's emotions and adjusts the frequency of psychological assessments based on the estimated emotions. The system according to feature 1.

7. The evaluation unit, We analyze past psychological evaluation data of athletes and select the optimal evaluation method. The system according to feature 1.

8. The evaluation unit, During psychological assessments, evaluation items are customized based on the athlete's current training status and competition schedule. The system according to feature 1.

9. The evaluation unit, The system estimates the athlete's emotions and prioritizes evaluation results based on the estimated emotions. The system according to feature 1.

10. The evaluation unit, During psychological assessments, the athlete's geographical location is taken into consideration, and highly relevant assessment items are prioritized. The system according to feature 1.

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