System

Abstract

An object of a system according to an embodiment is to quickly and accurately detect a concussion in contact sports and take appropriate measures thereafter.SOLUTION: A system includes an image determination unit, a medical interview collection unit, a generation unit, and a presentation unit. The image determination unit analyzes a video during a game to determine the possibility of a concussion. The medical inquiry collecting unit collects a medical inquiry result and personal information. The generation unit generates an optimal treatment protocol based on the information collected by the image determination unit and the medical inquiry collection unit. The presentation unit presents the treatment protocol generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to quickly and accurately detect concussions in contact sports and provide appropriate treatment afterwards.

[0005] The system according to the embodiment aims to detect concussion in contact sports and provide appropriate treatment thereafter quickly and accurately. [Means for solving the problem]

[0006] The system according to the embodiment includes an image assessment unit, an interview collection unit, a generation unit, and a presentation unit. The image assessment unit analyzes video footage of the game to determine the possibility of a concussion. The interview collection unit collects interview results and personal information. The generation unit generates an optimal treatment protocol based on the information collected by the image assessment unit and the interview collection unit. The presentation unit presents the treatment protocol generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately detect concussion in contact sports and provide appropriate treatment thereafter. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The concussion detection and treatment recommendation system according to an embodiment of the present invention analyzes video footage from a game to determine the possibility of a concussion, collects medical interview results and personal information, and uses a generation AI to generate and recommend an optimal treatment protocol. This enables the concussion detection and treatment recommendation system to detect concussion early and provide appropriate treatment.

[0029] A concussion detection and treatment recommendation system according to an embodiment includes an image assessment unit, a medical interview collection unit, a generation unit, and a presentation unit. The image assessment unit analyzes video footage from a game to determine the possibility of a concussion. For example, the image assessment unit analyzes video data from a game in real time, and when a player receives a strong impact to the head, analyzes the player's movements and reactions to detect signs of a concussion. The medical interview collection unit collects medical interview results and personal information. For example, the medical interview collection unit interviews players about symptoms such as headaches, dizziness, and visual impairments and collects the results. The medical interview collection unit also collects personal information such as the player's age and gender. The generation unit generates an optimal treatment protocol based on the information collected by the image assessment unit and the medical interview collection unit. For example, the generation AI recommends the amount of rest the player should take, whether a doctor's examination is necessary, and what type of rehabilitation is appropriate based on the medical interview results, personal information, and the image assessment AI's judgment results. The presentation unit presents the treatment protocol generated by the generation unit. For example, the presentation unit may display specific instructions to athletes, coaches, and medical staff via an application, thereby enabling the concussion detection and treatment presentation system according to the embodiment to provide early detection of concussion and appropriate treatment.

[0030] The image judgment unit learns a player's movement patterns and detects movements that differ from normal movements, allowing it to accurately determine the possibility of a concussion. For example, the image judgment unit learns a player's past movement data and models normal movement patterns. It analyzes a player's movements in real time during a game and determines the possibility of a concussion by detecting movements that differ from normal movements. The image judgment unit also uses past game footage as training data to learn a player's movement patterns. AI detects subtle changes in a player's movements and identifies abnormal movements in real time. The image judgment unit also uses a machine learning algorithm to learn a player's movement patterns and detect movements that differ from normal movements. This allows it to accurately determine signs of a concussion. This improves the accuracy of concussion detection.

[0031] The image judgment unit can analyze changes in a player's facial expression and complexion to detect signs of a concussion. For example, the image judgment unit analyzes a player's facial expression in real time to detect abnormal changes in facial expression. For example, it identifies facial distortions and abnormal eye movements to determine signs of a concussion. The image judgment unit also detects changes in skin tone and blood flow to analyze changes in a player's complexion. This enables early detection of signs of a concussion. The image judgment unit also combines facial recognition technology with a color analysis algorithm to analyze changes in a player's facial expression and complexion. This enables highly accurate detection of signs of a concussion. This enables early detection of signs of a concussion.

[0032] The image judgment unit analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern, thereby determining signs of a concussion. The image judgment unit, for example, analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern. For example, it identifies voice tremors and abnormal vocalizations to determine signs of a concussion. The image judgment unit also combines voice recognition technology and acoustic analysis algorithms to analyze the audio data. This allows for highly accurate detection of changes in a player's voice. The image judgment unit also analyzes audio data during a game in real time and detects changes in a player's voice tone or vocalization pattern. This allows for early detection of signs of a concussion. This makes it possible to determine signs of a concussion based on audio data.

[0033] The image judging unit can analyze a player's vital data in real time and detect signs of a concussion. For example, the image judging unit analyzes a player's vital data in real time and detects abnormalities in heart rate and blood pressure. This allows for early detection of signs of a concussion. The image judging unit also collects data from wearable devices to analyze the vital data and analyzes it in real time. This allows for monitoring the player's health. The image judging unit also uses a machine learning algorithm to analyze a player's vital data. This allows for highly accurate detection of changes in heart rate and blood pressure and for determining signs of a concussion. This allows for early detection of signs of a concussion based on the vital data.

[0034] The medical interview collection unit can collect medical interview results by voice input and automatically convert them into text using natural language processing technology. The medical interview collection unit, for example, builds a system that collects medical interview results by voice input and automatically converts them into text using natural language processing technology. For example, the content of verbal answers given by players is converted into text in real time. The medical interview collection unit also collects medical interview results by voice input and converts them into text using natural language processing technology. This allows the answers of players to be recorded quickly and accurately. The medical interview collection unit also uses a voice recognition algorithm to collect medical interview results by voice input and automatically convert them into text using natural language processing technology. This allows the answers of players to be recorded efficiently. This allows the medical interview results to be recorded quickly and accurately.

[0035] The medical interview collection unit can collect more detailed information by referring to the player's past injuries and medical history when collecting the medical interview results. For example, the medical interview collection unit builds a system that refers to the player's past injuries and medical history when collecting the medical interview results and collects more detailed information. For example, the medical interview collection unit automatically refers to the player's medical records. The medical interview collection unit also refers to the player's past injuries and medical history and collects the medical interview results. This allows for a comprehensive evaluation of the player's health condition. Furthermore, the medical interview collection unit develops a system that links with a medical database to refer to the player's past injuries and medical history when collecting the medical interview results. This allows for efficient collection of detailed information. This allows for a comprehensive evaluation of the player's health condition.

[0036] The generation unit can reference an athlete's past injuries and medical history to generate an individually customized treatment protocol. The generation unit, for example, builds a system in which a generation AI references an athlete's past injuries and medical history to generate an individually customized treatment protocol. For example, the generation unit proposes optimal treatment based on the athlete's past injury type and treatment history. The generation unit also references an athlete's past injuries and medical history to generate an individually customized treatment protocol. This provides optimal treatment according to the athlete's health condition. The generation unit also develops a system that links with a medical database so that the generation AI can reference an athlete's past injuries and medical history. This allows for the generation of individually customized treatment protocols with high accuracy. This makes it possible to provide optimal treatment according to the athlete's health condition.

[0037] The generation unit can refer to the latest medical guidelines and research results to generate the optimal treatment protocol. For example, the generation unit will build a system in which the generation AI refers to the latest medical guidelines and research results to generate the optimal treatment protocol. For example, it will propose treatment based on the latest treatment methods and rehabilitation programs. The generation unit will also automatically collect medical guidelines and research results, and the generation AI will generate the optimal treatment protocol based on these. This will provide treatment that reflects the latest medical knowledge. The generation unit will also develop a system that links with a medical database so that the generation AI can refer to the latest medical guidelines and research results. This will allow it to generate the optimal treatment protocol with high accuracy. This will allow it to provide treatment that reflects the latest medical knowledge.

[0038] The generation unit is capable of generating treatment protocols customized for each sport, taking into account the characteristics of different sports and competitions. For example, the generation unit constructs a system in which the generation AI takes into account the characteristics of different sports and competitions and generates treatment protocols customized for each sport. For example, it proposes treatments that are suited to the characteristics of rugby and soccer. The generation unit also registers the characteristics of each sport in a database, and the generation AI generates the optimal treatment protocol based on that. This allows for treatments specialized for each sport. The generation unit also develops a system that links with a sports medicine database so that the generation AI can take into account the characteristics of different sports and competitions. This allows for the generation of treatment protocols customized for each sport with a high degree of accuracy. This allows for treatments specialized for each sport.

[0039] The generation unit can analyze the player's vital data in real time and dynamically update the treatment protocol. For example, the generation unit builds a system in which the generation AI analyzes the player's vital data in real time and dynamically updates the treatment protocol. For example, the rehabilitation program is adjusted according to changes in heart rate and blood pressure. The generation unit also collects vital data in real time, and the generation AI dynamically updates the treatment protocol based on that data. This allows for the provision of optimal treatment according to the player's health condition. The generation unit also develops a system that works with wearable devices so that the generation AI can analyze the player's vital data in real time. This allows for the provision of dynamically updated treatment protocols with high accuracy. This allows for the provision of optimal treatment according to the player's health condition.

[0040] The presentation unit can use infographics and videos to present the treatment protocol in a visually easy-to-understand manner. The presentation unit, for example, builds a system that uses infographics to present the treatment protocol in a visually easy-to-understand manner. For example, it illustrates the rehabilitation steps. The presentation unit also presents the treatment protocol in videos to make it easier for athletes and coaches to visually understand. For example, it explains specific rehabilitation movements in videos. The presentation unit also develops a system that generates multimedia content to present the treatment protocol in a visually easy-to-understand manner using infographics and videos. This allows athletes and coaches to intuitively understand the protocol. This allows athletes and coaches to intuitively understand the treatment protocol.

[0041] The presentation unit can make the treatment protocol multilingual so that it can be used by international athletes and staff. The presentation unit, for example, builds a system that makes the treatment protocol multilingual so that it can be used by international athletes and staff. For example, the presentation unit provides protocols in multiple languages, such as English, Spanish, and Chinese. The presentation unit also uses a translation algorithm to generate multilingual treatment protocols, so that athletes and staff can understand the protocols in their native languages. The presentation unit also develops a system that works with a translation database to make the treatment protocol multilingual. This provides protocols that can be used by international athletes and staff. This makes it possible to be used by international athletes and staff.

[0042] The presentation unit can display the treatment protocol on a wearable device such as a smartwatch or smartglasses, allowing the athlete to check it at all times. The presentation unit, for example, builds a system that displays the treatment protocol on a smartwatch, allowing the athlete to check it at all times. For example, it displays the rehabilitation schedule and progress status. The presentation unit also displays the treatment protocol on smartglasses, allowing the athlete to visually check it. For example, it visually guides the athlete through specific rehabilitation movements. The presentation unit also develops a system that works with the device to display the treatment protocol on the wearable device. This allows the athlete to check the protocol at all times.

[0043] The presentation unit can present the treatment protocol through a voice assistant, allowing players and staff to receive instructions by voice. The presentation unit, for example, builds a system that presents the treatment protocol through a voice assistant, allowing players and staff to receive instructions by voice. For example, it provides audio guidance for rehabilitation steps. The presentation unit also presents the treatment protocol by voice using the voice assistant. This allows players and staff to receive instructions hands-free. The presentation unit also develops a system that works with voice recognition technology to present the treatment protocol through the voice assistant. This allows players and staff to check the protocol by voice. This allows players and staff to receive instructions hands-free.

[0044] The generation unit monitors the player's recovery status in real time, and the generation AI can automatically update the protocol. The generation unit, for example, builds a system that monitors the player's recovery status in real time, and the generation AI automatically updates the protocol. For example, the protocol is adjusted based on the player's vital data and rehabilitation progress. The generation unit also analyzes data collected in real time, and the generation AI dynamically updates the treatment protocol based on that data. This allows for the provision of optimal treatment according to the player's recovery status. The generation unit also develops a system that works with wearable devices and health apps so that the generation AI can monitor the player's recovery status in real time. This allows for the provision of dynamically updated treatment protocols with high accuracy. This allows for the provision of optimal treatment according to the player's recovery status.

[0045] The generation unit collects player feedback, and the generation AI can improve the protocol based on that feedback. The generation unit, for example, builds a system that collects player feedback and allows the generation AI to improve the protocol based on that feedback. For example, players report the effectiveness of rehabilitation and any areas of dissatisfaction. The generation unit also collects feedback in real time, and the generation AI dynamically updates the treatment protocol based on that feedback. This allows for the provision of optimal treatment that reflects the player's opinions. The generation unit also develops a system that links with a mobile app or online platform so that the generation AI can collect player feedback. This allows for the protocol to be improved with high accuracy based on the feedback. This allows for the provision of optimal treatment that reflects the player's opinions.

[0046] The generation unit can share the recovery status of a player with other players and staff, enabling the entire team to support the player. For example, the generation unit builds a system for sharing the recovery status of a player with other players and staff, enabling the entire team to support the player. For example, the generation unit shares the progress of rehabilitation. The generation unit also shares the recovery status in real time, enabling the entire team to support the player. This promotes the player's recovery. The generation unit also develops a system that links with online platforms and mobile apps to share the recovery status of a player. This allows the entire team to support the player. This allows the entire team to support the player.

[0047] The generation unit can update the protocol while coordinating the recovery status of the player with medical institutions and receiving expert advice. The generation unit, for example, builds a system that coordinates the recovery status of the player with medical institutions and updates the protocol while receiving expert advice. For example, the generation unit adjusts the protocol based on the results of a doctor's examination. The generation unit also coordinates with medical institutions and shares the recovery status of the player in real time. This allows optimal treatment to be provided while receiving expert advice. The generation unit also develops a system that coordinates with a medical database to coordinate the recovery status of the player with medical institutions. This allows the protocol to be updated with high accuracy based on expert advice. This allows optimal treatment to be provided based on expert advice.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The image assessment unit learns players' movement patterns and detects movements that deviate from normal movements, enabling it to accurately determine the possibility of a concussion. For example, it learns players' past movement data and models normal movement patterns. It analyzes players' movements in real time during a game and detects movements that deviate from normal movements to determine the possibility of a concussion. The image assessment unit also uses past game footage as training data to learn players' movement patterns. AI detects subtle changes in players' movements and identifies abnormal movements in real time. The image assessment unit also uses machine learning algorithms to learn players' movement patterns and detect movements that deviate from normal movements. This allows it to accurately determine signs of a concussion. This improves the accuracy of concussion detection.

[0050] The image assessment unit can detect signs of a concussion by analyzing changes in a player's facial expression and complexion. For example, it analyzes a player's facial expression in real time to detect abnormal changes in facial expression. For example, it identifies facial distortions and abnormal eye movements to determine signs of a concussion. The image assessment unit also detects changes in skin tone and blood flow to analyze changes in a player's complexion. This enables early detection of signs of a concussion. The image assessment unit also combines facial recognition technology with a color analysis algorithm to analyze changes in a player's facial expression and complexion. This enables highly accurate detection of signs of a concussion. This enables early detection of signs of a concussion.

[0051] The image adjudication unit analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern, thereby determining signs of a concussion. For example, it analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern. For example, it identifies voice tremors and abnormal vocalizations to determine signs of a concussion. The image adjudication unit also combines voice recognition technology and acoustic analysis algorithms to analyze the audio data. This allows it to detect changes in a player's voice with high accuracy. The image adjudication unit also analyzes audio data during a game in real time and detects changes in a player's voice tone or vocalization pattern. This allows for early detection of signs of a concussion. This makes it possible to determine signs of a concussion based on audio data.

[0052] The image assessment unit can analyze a player's vital data in real time to detect signs of a concussion. For example, it analyzes a player's vital data in real time to detect abnormalities in heart rate and blood pressure. This allows for early detection of signs of a concussion. The image assessment unit also collects data from wearable devices to analyze the vital data and analyzes it in real time. This allows for monitoring of the player's health. The image assessment unit also uses machine learning algorithms to analyze a player's vital data. This allows for highly accurate detection of changes in heart rate and blood pressure and for determining signs of a concussion. This allows for early detection of signs of a concussion based on vital data.

[0053] The medical interview collection unit can collect medical interview results by voice input and automatically convert them into text using natural language processing technology. For example, a system can be constructed that collects medical interview results by voice input and automatically converts them into text using natural language processing technology. For example, verbal responses from athletes are converted into text in real time. The medical interview collection unit also collects medical interview results by voice input and converts them into text using natural language processing technology. This allows athletes' responses to be recorded quickly and accurately. The medical interview collection unit also uses a voice recognition algorithm to collect medical interview results by voice input and automatically converts them into text using natural language processing technology. This allows athletes' responses to be recorded efficiently. This allows medical interview results to be recorded quickly and accurately.

[0054] The generation unit monitors the player's recovery status in real time, and the generation AI can automatically update the protocol. For example, it adjusts the protocol based on the player's vital data and rehabilitation progress. The generation unit also analyzes data collected in real time, and the generation AI dynamically updates the treatment protocol based on that data. This allows the optimal treatment to be provided according to the player's recovery status. The generation unit also develops a system that links with wearable devices and health apps so that the generation AI can monitor the player's recovery status in real time. This allows the generation AI to provide dynamically updated treatment protocols with high accuracy. This allows the optimal treatment to be provided according to the player's recovery status.

[0055] The generation unit collects player feedback and allows the generation AI to improve the protocol based on that feedback. For example, a system is built in which player feedback is collected and the generation AI improves the protocol based on that feedback. For example, players report the effectiveness of their rehabilitation and any areas of dissatisfaction. The generation unit also collects feedback in real time, and the generation AI dynamically updates the treatment protocol based on that feedback. This allows the optimal treatment to be provided, reflecting the player's opinions. The generation unit also develops a system that links with a mobile app or online platform so that the generation AI can collect player feedback. This allows the protocol to be improved with high accuracy based on the feedback. This allows the optimal treatment to be provided, reflecting the player's opinions.

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

[0057] Step 1: The image assessment unit analyzes video footage from the game to determine whether a player has a concussion. For example, if a player receives a strong blow to the head, the image assessment unit analyzes video data from the game in real time to detect signs of a concussion by analyzing their movements and reactions. Step 2: The medical interview collection unit collects the results of the medical interview and personal information. For example, the unit may ask athletes about symptoms such as headaches, dizziness, and visual impairments, and collect the results. It also collects personal information such as the athletes' age and gender. Step 3: The generator generates an optimal treatment protocol based on the information collected by the image evaluation and interview collection units. For example, based on the interview results, personal information, and the results of the image evaluation AI, it will suggest how much rest the athlete should take, whether a doctor's examination is necessary, and what type of rehabilitation is appropriate. Step 4: The presentation unit presents the treatment protocol generated by the generation unit, for example, by displaying specific instructions to the players, coaches, and medical staff through an application.

[0058] (Example 2) The concussion detection and treatment recommendation system according to an embodiment of the present invention analyzes video footage from a game to determine the possibility of a concussion, collects medical interview results and personal information, and uses a generation AI to generate and recommend an optimal treatment protocol. This enables the concussion detection and treatment recommendation system to detect concussion early and provide appropriate treatment.

[0059] A concussion detection and treatment recommendation system according to an embodiment includes an image assessment unit, a medical interview collection unit, a generation unit, and a presentation unit. The image assessment unit analyzes video footage from a game to determine the possibility of a concussion. For example, the image assessment unit analyzes video data from a game in real time, and when a player receives a strong impact to the head, analyzes the player's movements and reactions to detect signs of a concussion. The medical interview collection unit collects medical interview results and personal information. For example, the medical interview collection unit interviews players about symptoms such as headaches, dizziness, and visual impairments and collects the results. The medical interview collection unit also collects personal information such as the player's age and gender. The generation unit generates an optimal treatment protocol based on the information collected by the image assessment unit and the medical interview collection unit. For example, the generation AI recommends the amount of rest the player should take, whether a doctor's examination is necessary, and what type of rehabilitation is appropriate based on the medical interview results, personal information, and the image assessment AI's judgment results. The presentation unit presents the treatment protocol generated by the generation unit. For example, the presentation unit may display specific instructions to athletes, coaches, and medical staff via an application, thereby enabling the concussion detection and treatment presentation system according to the embodiment to provide early detection of concussion and appropriate treatment.

[0060] The image judgment unit learns a player's movement patterns and detects movements that differ from normal movements, allowing it to accurately determine the possibility of a concussion. For example, the image judgment unit learns a player's past movement data and models normal movement patterns. It analyzes a player's movements in real time during a game and determines the possibility of a concussion by detecting movements that differ from normal movements. The image judgment unit also uses past game footage as training data to learn a player's movement patterns. AI detects subtle changes in a player's movements and identifies abnormal movements in real time. The image judgment unit also uses a machine learning algorithm to learn a player's movement patterns and detect movements that differ from normal movements. This allows it to accurately determine signs of a concussion. This improves the accuracy of concussion detection.

[0061] The image judgment unit can analyze changes in a player's facial expression and complexion to detect signs of a concussion. For example, the image judgment unit analyzes a player's facial expression in real time to detect abnormal changes in facial expression. For example, it identifies facial distortions and abnormal eye movements to determine signs of a concussion. The image judgment unit also detects changes in skin tone and blood flow to analyze changes in a player's complexion. This enables early detection of signs of a concussion. The image judgment unit also combines facial recognition technology with a color analysis algorithm to analyze changes in a player's facial expression and complexion. This enables highly accurate detection of signs of a concussion. This enables early detection of signs of a concussion.

[0062] The image assessment unit uses the emotion estimation function to detect changes in emotion from the player's facial expressions and movements, and can make a complementary assessment of the possibility of a concussion. The image assessment unit, for example, analyzes the player's facial expressions and movements, and detects changes in emotion using the emotion estimation function. For example, it identifies facial expressions indicating pain or confusion, and makes a complementary assessment of the possibility of a concussion. The image assessment unit also uses the emotion estimation function to analyze changes in emotion from the player's movements and facial expressions in real time, thereby detecting signs of a concussion early. The image assessment unit also uses the emotion estimation function to use a deep learning algorithm to detect changes in emotion from the player's facial expressions and movements, thereby making a highly accurate assessment of the possibility of a concussion. This makes it possible to make a complementary assessment of the possibility of a concussion based on changes in emotion.

[0063] The image judgment unit analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern, thereby determining signs of a concussion. The image judgment unit, for example, analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern. For example, it identifies voice tremors and abnormal vocalizations to determine signs of a concussion. The image judgment unit also combines voice recognition technology and acoustic analysis algorithms to analyze the audio data. This allows for highly accurate detection of changes in a player's voice. The image judgment unit also analyzes audio data during a game in real time and detects changes in a player's voice tone or vocalization pattern. This allows for early detection of signs of a concussion. This makes it possible to determine signs of a concussion based on audio data.

[0064] The image judging unit can analyze a player's vital data in real time and detect signs of a concussion. For example, the image judging unit analyzes a player's vital data in real time and detects abnormalities in heart rate and blood pressure. This allows for early detection of signs of a concussion. The image judging unit also collects data from wearable devices to analyze the vital data and analyzes it in real time. This allows for monitoring the player's health. The image judging unit also uses a machine learning algorithm to analyze a player's vital data. This allows for highly accurate detection of changes in heart rate and blood pressure and for determining signs of a concussion. This allows for early detection of signs of a concussion based on the vital data.

[0065] The medical interview collection unit can collect medical interview results by voice input and automatically convert them into text using natural language processing technology. The medical interview collection unit, for example, builds a system that collects medical interview results by voice input and automatically converts them into text using natural language processing technology. For example, the content of verbal answers given by players is converted into text in real time. The medical interview collection unit also collects medical interview results by voice input and converts them into text using natural language processing technology. This allows the answers of players to be recorded quickly and accurately. The medical interview collection unit also uses a voice recognition algorithm to collect medical interview results by voice input and automatically convert them into text using natural language processing technology. This allows the answers of players to be recorded efficiently. This allows the medical interview results to be recorded quickly and accurately.

[0066] The medical interview collection unit can collect more detailed information by referring to the player's past injuries and medical history when collecting the medical interview results. For example, the medical interview collection unit builds a system that refers to the player's past injuries and medical history when collecting the medical interview results and collects more detailed information. For example, the medical interview collection unit automatically refers to the player's medical records. The medical interview collection unit also refers to the player's past injuries and medical history and collects the medical interview results. This allows for a comprehensive evaluation of the player's health condition. Furthermore, the medical interview collection unit develops a system that links with a medical database to refer to the player's past injuries and medical history when collecting the medical interview results. This allows for efficient collection of detailed information. This allows for a comprehensive evaluation of the player's health condition.

[0067] The interview collection unit can use the emotion estimation function to analyze the emotions of the players when they answer questions and evaluate the reliability of the answers. The interview collection unit, for example, uses the emotion estimation function to analyze the emotions of the players when they answer questions and build a system to evaluate the reliability of the answers. For example, it analyzes the players' facial expressions and tone of voice. The interview collection unit also uses the emotion estimation function to analyze the emotions of the players when they answer questions. This allows the reliability of the answers to be evaluated and accurate information to be collected. The interview collection unit also uses the emotion estimation function to analyze the emotions of the players when they answer questions in real time and evaluate the reliability of the answers. For example, if the player shows nervousness or anxiety, the reliability of the answers is evaluated low. This allows the reliability of the answers to be evaluated and accurate information to be collected.

[0068] The generation unit can reference an athlete's past injuries and medical history to generate an individually customized treatment protocol. The generation unit, for example, builds a system in which a generation AI references an athlete's past injuries and medical history to generate an individually customized treatment protocol. For example, the generation unit proposes optimal treatment based on the athlete's past injury type and treatment history. The generation unit also references an athlete's past injuries and medical history to generate an individually customized treatment protocol. This provides optimal treatment according to the athlete's health condition. The generation unit also develops a system that links with a medical database so that the generation AI can reference an athlete's past injuries and medical history. This allows for the generation of individually customized treatment protocols with high accuracy. This makes it possible to provide optimal treatment according to the athlete's health condition.

[0069] The generation unit can refer to the latest medical guidelines and research results to generate the optimal treatment protocol. For example, the generation unit will build a system in which the generation AI refers to the latest medical guidelines and research results to generate the optimal treatment protocol. For example, it will propose treatment based on the latest treatment methods and rehabilitation programs. The generation unit will also automatically collect medical guidelines and research results, and the generation AI will generate the optimal treatment protocol based on these. This will provide treatment that reflects the latest medical knowledge. The generation unit will also develop a system that links with a medical database so that the generation AI can refer to the latest medical guidelines and research results. This will allow it to generate the optimal treatment protocol with high accuracy. This will allow it to provide treatment that reflects the latest medical knowledge.

[0070] The generation unit can use the emotion estimation function to generate a treatment protocol that takes into account the player's emotional state. The generation unit, for example, uses the emotion estimation function to build a system that generates a treatment protocol that takes into account the player's emotional state. For example, if a player is feeling anxious or stressed, the generation unit proposes an appropriate rehabilitation program. The generation unit also analyzes the player's emotional state in real time, and the generation AI generates an optimal treatment protocol based on that. This provides treatment that is appropriate for the player's psychological state. The generation unit also uses a deep learning algorithm to generate a treatment protocol that takes into account the player's emotional state using the emotion estimation function. This allows for the generation of optimal treatment that is appropriate for the player's emotional state with high accuracy. This makes it possible to provide treatment that is appropriate for the player's psychological state.

[0071] The generation unit is capable of generating treatment protocols customized for each sport, taking into account the characteristics of different sports and competitions. For example, the generation unit constructs a system in which the generation AI takes into account the characteristics of different sports and competitions and generates treatment protocols customized for each sport. For example, it proposes treatments that are suited to the characteristics of rugby and soccer. The generation unit also registers the characteristics of each sport in a database, and the generation AI generates the optimal treatment protocol based on that. This allows for treatments specialized for each sport. The generation unit also develops a system that links with a sports medicine database so that the generation AI can take into account the characteristics of different sports and competitions. This allows for the generation of treatment protocols customized for each sport with a high degree of accuracy. This allows for treatments specialized for each sport.

[0072] The generation unit can analyze the player's vital data in real time and dynamically update the treatment protocol. For example, the generation unit builds a system in which the generation AI analyzes the player's vital data in real time and dynamically updates the treatment protocol. For example, the rehabilitation program is adjusted according to changes in heart rate and blood pressure. The generation unit also collects vital data in real time, and the generation AI dynamically updates the treatment protocol based on that data. This allows for the provision of optimal treatment according to the player's health condition. The generation unit also develops a system that works with wearable devices so that the generation AI can analyze the player's vital data in real time. This allows for the provision of dynamically updated treatment protocols with high accuracy. This allows for the provision of optimal treatment according to the player's health condition.

[0073] The generation unit can use the emotion estimation function to analyze the emotional state of a player in real time and generate a treatment protocol according to the emotion. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of a player in real time and generates a treatment protocol according to the emotion. For example, if a player is feeling stressed, it proposes a rehabilitation program that corresponds to that. The generation unit also analyzes the emotional state of a player in real time, and the generation AI generates an optimal treatment protocol based on that. This provides treatment according to the player's psychological state. The generation unit also uses a deep learning algorithm to analyze the emotional state of a player in real time using the emotion estimation function. This allows for the generation of optimal treatment according to the emotional state with high accuracy. This makes it possible to provide treatment according to the player's psychological state.

[0074] The presentation unit can use infographics and videos to present the treatment protocol in a visually easy-to-understand manner. The presentation unit, for example, builds a system that uses infographics to present the treatment protocol in a visually easy-to-understand manner. For example, it illustrates the rehabilitation steps. The presentation unit also presents the treatment protocol in videos to make it easier for athletes and coaches to visually understand. For example, it explains specific rehabilitation movements in videos. The presentation unit also develops a system that generates multimedia content to present the treatment protocol in a visually easy-to-understand manner using infographics and videos. This allows athletes and coaches to intuitively understand the protocol. This allows athletes and coaches to intuitively understand the treatment protocol.

[0075] The presentation unit can make the treatment protocol multilingual so that it can be used by international athletes and staff. The presentation unit, for example, builds a system that makes the treatment protocol multilingual so that it can be used by international athletes and staff. For example, the presentation unit provides protocols in multiple languages, such as English, Spanish, and Chinese. The presentation unit also uses a translation algorithm to generate multilingual treatment protocols, so that athletes and staff can understand the protocols in their native languages. The presentation unit also develops a system that works with a translation database to make the treatment protocol multilingual. This provides protocols that can be used by international athletes and staff. This makes it possible to be used by international athletes and staff.

[0076] The presentation unit can use the emotion estimation function to present a protocol in a communication method that corresponds to the emotional state of the players and staff. The presentation unit, for example, uses the emotion estimation function to build a system that presents a protocol in a communication method that corresponds to the emotional state of the players and staff. For example, if a player is feeling anxious, a message that gives a sense of security is displayed. The presentation unit also analyzes the emotional state of the players and staff in real time and presents a protocol in a communication method that corresponds to that state. This makes it easier for the players and staff to accept the protocol. The presentation unit also uses a deep learning algorithm to present a protocol in a communication method that corresponds to the emotional state of the players and staff using the emotion estimation function. This provides optimal communication that corresponds to the emotional state. This makes it possible to provide optimal communication that corresponds to the emotional state.

[0077] The presentation unit can display the treatment protocol on a wearable device such as a smartwatch or smartglasses, allowing the athlete to check it at all times. The presentation unit, for example, builds a system that displays the treatment protocol on a smartwatch, allowing the athlete to check it at all times. For example, it displays the rehabilitation schedule and progress status. The presentation unit also displays the treatment protocol on smartglasses, allowing the athlete to visually check it. For example, it visually guides the athlete through specific rehabilitation movements. The presentation unit also develops a system that works with the device to display the treatment protocol on the wearable device. This allows the athlete to check the protocol at all times.

[0078] The presentation unit can present the treatment protocol through a voice assistant, allowing players and staff to receive instructions by voice. The presentation unit, for example, builds a system that presents the treatment protocol through a voice assistant, allowing players and staff to receive instructions by voice. For example, it provides audio guidance for rehabilitation steps. The presentation unit also presents the treatment protocol by voice using the voice assistant. This allows players and staff to receive instructions hands-free. The presentation unit also develops a system that works with voice recognition technology to present the treatment protocol through the voice assistant. This allows players and staff to check the protocol by voice. This allows players and staff to receive instructions hands-free.

[0079] The presentation unit can use the emotion estimation function to present a protocol in a communication method that corresponds to the emotional state of the players and staff. The presentation unit, for example, uses the emotion estimation function to build a system that presents a protocol in a communication method that corresponds to the emotional state of the players and staff. For example, if a player is feeling anxious, a message that gives a sense of security is displayed. The presentation unit also analyzes the emotional state of the players and staff in real time and presents a protocol in a communication method that corresponds to that state. This makes it easier for the players and staff to accept the protocol. The presentation unit also uses a deep learning algorithm to present a protocol in a communication method that corresponds to the emotional state of the players and staff using the emotion estimation function. This provides optimal communication that corresponds to the emotional state. This makes it possible to provide optimal communication that corresponds to the emotional state.

[0080] The generation unit monitors the player's recovery status in real time, and the generation AI can automatically update the protocol. The generation unit, for example, builds a system that monitors the player's recovery status in real time, and the generation AI automatically updates the protocol. For example, the protocol is adjusted based on the player's vital data and rehabilitation progress. The generation unit also analyzes data collected in real time, and the generation AI dynamically updates the treatment protocol based on that data. This allows for the provision of optimal treatment according to the player's recovery status. The generation unit also develops a system that works with wearable devices and health apps so that the generation AI can monitor the player's recovery status in real time. This allows for the provision of dynamically updated treatment protocols with high accuracy. This allows for the provision of optimal treatment according to the player's recovery status.

[0081] The generation unit collects player feedback, and the generation AI can improve the protocol based on that feedback. The generation unit, for example, builds a system that collects player feedback and allows the generation AI to improve the protocol based on that feedback. For example, players report the effectiveness of rehabilitation and any areas of dissatisfaction. The generation unit also collects feedback in real time, and the generation AI dynamically updates the treatment protocol based on that feedback. This allows for the provision of optimal treatment that reflects the player's opinions. The generation unit also develops a system that links with a mobile app or online platform so that the generation AI can collect player feedback. This allows for the protocol to be improved with high accuracy based on the feedback. This allows for the provision of optimal treatment that reflects the player's opinions.

[0082] The generation unit can use the emotion estimation function to monitor the emotional state of the player and update the protocol according to the emotion. For example, the generation unit uses the emotion estimation function to build a system that monitors the emotional state of the player and updates the protocol according to the emotion. For example, if the player is feeling stressed, it proposes an appropriate rehabilitation program. The generation unit also analyzes the player's emotional state in real time, and the generation AI generates an optimal treatment protocol based on that. This provides treatment according to the player's psychological state. The generation unit also uses the emotion estimation function to use a deep learning algorithm to analyze the player's emotional state in real time. This allows for the generation of optimal treatment according to the emotional state with high accuracy. This makes it possible to provide optimal treatment according to the player's psychological state.

[0083] The generation unit can share the recovery status of a player with other players and staff, enabling the entire team to support the player. For example, the generation unit builds a system for sharing the recovery status of a player with other players and staff, enabling the entire team to support the player. For example, the generation unit shares the progress of rehabilitation. The generation unit also shares the recovery status in real time, enabling the entire team to support the player. This promotes the player's recovery. The generation unit also develops a system that links with online platforms and mobile apps to share the recovery status of a player. This allows the entire team to support the player. This allows the entire team to support the player.

[0084] The generation unit can update the protocol while coordinating the recovery status of the player with medical institutions and receiving expert advice. The generation unit, for example, builds a system that coordinates the recovery status of the player with medical institutions and updates the protocol while receiving expert advice. For example, the generation unit adjusts the protocol based on the results of a doctor's examination. The generation unit also coordinates with medical institutions and shares the recovery status of the player in real time. This allows optimal treatment to be provided while receiving expert advice. The generation unit also develops a system that coordinates with a medical database to coordinate the recovery status of the player with medical institutions. This allows the protocol to be updated with high accuracy based on expert advice. This allows optimal treatment to be provided based on expert advice.

[0085] The generation unit can use the emotion estimation function to monitor the emotional state of the player and update the protocol according to the emotion. For example, the generation unit uses the emotion estimation function to build a system that monitors the emotional state of the player and updates the protocol according to the emotion. For example, if the player is feeling stressed, it proposes an appropriate rehabilitation program. The generation unit also analyzes the player's emotional state in real time, and the generation AI generates an optimal treatment protocol based on that. This provides treatment according to the player's psychological state. The generation unit also uses the emotion estimation function to use a deep learning algorithm to analyze the player's emotional state in real time. This allows for the generation of optimal treatment according to the emotional state with high accuracy. This makes it possible to provide optimal treatment according to the player's psychological state.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The image assessment unit learns players' movement patterns and detects movements that deviate from normal movements, enabling it to accurately determine the possibility of a concussion. For example, it learns players' past movement data and models normal movement patterns. It analyzes players' movements in real time during a game and detects movements that deviate from normal movements to determine the possibility of a concussion. The image assessment unit also uses past game footage as training data to learn players' movement patterns. AI detects subtle changes in players' movements and identifies abnormal movements in real time. The image assessment unit also uses machine learning algorithms to learn players' movement patterns and detect movements that deviate from normal movements. This allows it to accurately determine signs of a concussion. This improves the accuracy of concussion detection.

[0088] The image assessment unit can detect signs of a concussion by analyzing changes in a player's facial expression and complexion. For example, it analyzes a player's facial expression in real time to detect abnormal changes in facial expression. For example, it identifies facial distortions and abnormal eye movements to determine signs of a concussion. The image assessment unit also detects changes in skin tone and blood flow to analyze changes in a player's complexion. This enables early detection of signs of a concussion. The image assessment unit also combines facial recognition technology with a color analysis algorithm to analyze changes in a player's facial expression and complexion. This enables highly accurate detection of signs of a concussion. This enables early detection of signs of a concussion.

[0089] The image adjudication unit analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern, thereby determining signs of a concussion. For example, it analyzes audio data during a game and detects changes in a player's voice tone or vocalization pattern. For example, it identifies voice tremors and abnormal vocalizations to determine signs of a concussion. The image adjudication unit also combines voice recognition technology and acoustic analysis algorithms to analyze the audio data. This allows it to detect changes in a player's voice with high accuracy. The image adjudication unit also analyzes audio data during a game in real time and detects changes in a player's voice tone or vocalization pattern. This allows for early detection of signs of a concussion. This makes it possible to determine signs of a concussion based on audio data.

[0090] The image assessment unit can analyze a player's vital data in real time to detect signs of a concussion. For example, it analyzes a player's vital data in real time to detect abnormalities in heart rate and blood pressure. This allows for early detection of signs of a concussion. The image assessment unit also collects data from wearable devices to analyze the vital data and analyzes it in real time. This allows for monitoring of the player's health. The image assessment unit also uses machine learning algorithms to analyze a player's vital data. This allows for highly accurate detection of changes in heart rate and blood pressure and for determining signs of a concussion. This allows for early detection of signs of a concussion based on vital data.

[0091] The medical interview collection unit can collect medical interview results by voice input and automatically convert them into text using natural language processing technology. For example, a system can be constructed that collects medical interview results by voice input and automatically converts them into text using natural language processing technology. For example, verbal responses from athletes are converted into text in real time. The medical interview collection unit also collects medical interview results by voice input and converts them into text using natural language processing technology. This allows athletes' responses to be recorded quickly and accurately. The medical interview collection unit also uses a voice recognition algorithm to collect medical interview results by voice input and automatically converts them into text using natural language processing technology. This allows athletes' responses to be recorded efficiently. This allows medical interview results to be recorded quickly and accurately.

[0092] The interview collection unit can use the emotion estimation function to analyze the emotions of the players when they answer questions and evaluate the reliability of the answers. For example, a system is constructed that uses the emotion estimation function to analyze the emotions of the players when they answer questions and evaluate the reliability of the answers. For example, the emotion estimation function is used to analyze the players' facial expressions and tone of voice. The interview collection unit also uses the emotion estimation function to analyze the emotions of the players when they answer questions. This allows the reliability of the answers to be evaluated and accurate information to be collected. The interview collection unit also uses the emotion estimation function to analyze the emotions of the players when they answer questions in real time and evaluate the reliability of the answers. For example, if the player shows nervousness or anxiety, the reliability of the answers is evaluated low. This allows the reliability of the answers to be evaluated and accurate information to be collected.

[0093] The generation unit can use the emotion estimation function to generate a treatment protocol that takes into account the player's emotional state. For example, a system can be constructed that uses the emotion estimation function to generate a treatment protocol that takes into account the player's emotional state. For example, if a player is feeling anxious or stressed, an appropriate rehabilitation program can be proposed. The generation unit also analyzes the player's emotional state in real time, and the generation AI generates the optimal treatment protocol based on that. This provides treatment that is appropriate for the player's psychological state. The generation unit also uses a deep learning algorithm to generate a treatment protocol that takes into account the player's emotional state using the emotion estimation function. This allows for the generation of optimal treatment that is appropriate for the player's emotional state with high accuracy. This makes it possible to provide treatment that is appropriate for the player's psychological state.

[0094] The presentation unit can use the emotion estimation function to present a protocol in a communication method that corresponds to the emotional state of the players and staff. For example, a system can be constructed that uses the emotion estimation function to present a protocol in a communication method that corresponds to the emotional state of the players and staff. For example, if a player is feeling anxious, a message that gives a sense of security is displayed. The presentation unit also analyzes the emotional state of the players and staff in real time and presents a protocol in a communication method that corresponds to that state. This makes it easier for the players and staff to accept the protocol. The presentation unit also uses a deep learning algorithm to present a protocol in a communication method that corresponds to the emotional state of the players and staff using the emotion estimation function. This provides optimal communication that corresponds to the emotional state. This makes it possible to provide optimal communication that corresponds to the emotional state.

[0095] The generation unit monitors the player's recovery status in real time, and the generation AI can automatically update the protocol. For example, it adjusts the protocol based on the player's vital data and rehabilitation progress. The generation unit also analyzes data collected in real time, and the generation AI dynamically updates the treatment protocol based on that data. This allows the optimal treatment to be provided according to the player's recovery status. The generation unit also develops a system that links with wearable devices and health apps so that the generation AI can monitor the player's recovery status in real time. This allows the generation AI to provide dynamically updated treatment protocols with high accuracy. This allows the optimal treatment to be provided according to the player's recovery status.

[0096] The generation unit collects player feedback and allows the generation AI to improve the protocol based on that feedback. For example, a system is built in which player feedback is collected and the generation AI improves the protocol based on that feedback. For example, players report the effectiveness of their rehabilitation and any areas of dissatisfaction. The generation unit also collects feedback in real time, and the generation AI dynamically updates the treatment protocol based on that feedback. This allows the optimal treatment to be provided, reflecting the player's opinions. The generation unit also develops a system that links with a mobile app or online platform so that the generation AI can collect player feedback. This allows the protocol to be improved with high accuracy based on the feedback. This allows the optimal treatment to be provided, reflecting the player's opinions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The image assessment unit analyzes video footage from the game to determine whether a player has a concussion. For example, if a player receives a strong blow to the head, the image assessment unit analyzes video data from the game in real time to detect signs of a concussion by analyzing their movements and reactions. Step 2: The medical interview collection unit collects the results of the medical interview and personal information. For example, the unit may ask athletes about symptoms such as headaches, dizziness, and visual impairments, and collect the results. It also collects personal information such as the athletes' age and gender. Step 3: The generator generates an optimal treatment protocol based on the information collected by the image evaluation and interview collection units. For example, based on the interview results, personal information, and the results of the image evaluation AI, it will suggest how much rest the athlete should take, whether a doctor's examination is necessary, and what type of rehabilitation is appropriate. Step 4: The presentation unit presents the treatment protocol generated by the generation unit, for example, by displaying specific instructions to the players, coaches, and medical staff through an application.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An image assessment unit that analyzes footage from the game to determine the possibility of a concussion; a medical interview collection unit that collects medical interview results and personal information; a generating unit that generates an optimal treatment protocol based on the information collected by the image evaluation unit and the medical interview collection unit; a presentation unit that presents the treatment protocol generated by the generation unit. A system characterized by:

2. The image determination unit By learning the movement patterns of athletes and detecting movements that differ from normal movements, the possibility of concussion can be determined with high accuracy.

2. The system of claim 1.

3. The image determination unit Analyzing the player's facial expressions and changes in complexion to detect signs of concussion 2. The system of claim 1.

4. The image determination unit Detecting changes in emotions from the player's facial expressions and movements to supplement the assessment of the possibility of a concussion 2. The system of claim 1.

5. The image determination unit The system analyzes audio data during a match and detects changes in the player's tone of voice and speech patterns to identify signs of concussion.

2. The system of claim 1.

6. The image determination unit Analyzing the player's vital data in real time to detect signs of concussion 2. The system of claim 1.

Images