system

Abstract

The system according to this embodiment aims to detect head impacts in real time, assess the risk of concussion, and automatically generate an appropriate protocol. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects video data. The analysis unit analyzes the video data collected by the collection unit in real time and detects head impacts. The generation unit evaluates the risk of concussion based on the head impacts detected by the analysis unit and automatically generates a head injury assessment protocol.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to accurately detect an impact on the head and perform treatment based on an appropriate protocol, and there is room for improvement.

[0005] The system according to the embodiment aims to detect an impact on the head in real time, evaluate the risk of concussion, and automatically generate an appropriate protocol.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects video data. The analysis unit analyzes the video data collected by the collection unit in real time and detects head impacts. The generation unit evaluates the risk of concussion based on the head impacts detected by the analysis unit and automatically generates a head injury assessment protocol. [Effects of the Invention]

[0007] The system according to this embodiment can detect impacts to the head in real time, assess the risk of concussion, and automatically generate an appropriate protocol. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018]

[0019] The data processing device 12 includes a computer as 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The head impact detection system according to an embodiment of the present invention is a system that accurately detects impacts to the head, assesses the risk of concussion in real time, and provides an appropriate protocol. The head impact detection system uses an AI agent to analyze video footage of sports or driving in real time and warns of the potential risk of concussion the moment a head impact occurs. If a concussion is suspected, the system automatically generates and presents a Health Impact Assessment (HIA) protocol. For example, if an athlete receives a head impact during a sports match, the AI ​​detects the moment and assesses the risk of concussion. Next, if a concussion is suspected, the system automatically generates and presents an HIA protocol. The HIA protocol assesses the risk of concussion and suggests appropriate treatment. For example, if an athlete receives a head impact, the AI ​​generates an HIA protocol and suggests that the athlete take a rest. Furthermore, the head impact detection system adds a custom risk assessment function that utilizes individual history data. This improves the accuracy of providing protocols tailored to each user and the diagnosis. For example, a more stringent protocol can be provided to athletes who have previously experienced concussions to reduce the risk of recurrence. This system makes it possible to accurately assess the risk of concussion in situations such as sports and traffic accidents, and to provide appropriate treatment. This allows for the provision of rapid and appropriate measures, improving safety and health. As a result, the head impact detection system can accurately detect impacts to the head, assess the risk of concussion in real time, and provide appropriate protocols.

[0029] The head impact detection system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects video data. The collection unit can collect video in real time, for example, during sports or driving. The collection unit acquires video data using cameras and sensors. For example, the collection unit tracks the movements of players during a sports match and captures the moment an impact to the head occurs. The collection unit can also collect video in real time while driving and capture the moment of a traffic accident. The collection unit can also analyze video data using AI to detect the occurrence of a head impact. The analysis unit analyzes the video data collected by the collection unit in real time and detects head impacts. The analysis unit, for example, analyzes video data using AI to evaluate the intensity and direction of the impact to the head. The analysis unit can use AI to detect the occurrence of a head impact in real time. For example, if a player receives an impact to the head during a sports match, the analysis unit can detect the moment and evaluate the risk of concussion. The analysis unit can also analyze video while driving in real time and detect the moment of a traffic accident. The generation unit evaluates the risk of concussion based on the head impact detected by the analysis unit and automatically generates an HIA protocol. The generation unit generates a protocol that, for example, uses AI to evaluate the risk of concussion and proposes appropriate treatment. The generation unit can use AI to evaluate the risk of concussion in real time and automatically generate an HIA protocol. For example, if an athlete receives a head impact during a sports match, the generation unit generates an HIA protocol and suggests that the athlete take a rest. The generation unit can also analyze video footage while driving in real time, detect the moment of a traffic accident, and generate an HIA protocol. As a result, the head impact detection system according to this embodiment can accurately detect impacts to the head, evaluate the risk of concussion in real time, and provide an appropriate protocol.

[0030] The data collection unit collects video data. For example, the data collection unit can collect video footage of sports or driving in real time. The data collection unit acquires video data using cameras and sensors. Specifically, to track the movements of athletes during a sports match, multiple high-resolution cameras are installed to capture the athletes' movements from various angles. This allows for the accurate capture of the moment when an impact to the head occurs. In addition, to collect video footage while driving, dashcams and in-car cameras are installed in vehicles to record the driver's movements and surrounding conditions in real time. This allows for the capture of the moment of a traffic accident. The data collection unit can also use AI to analyze video data and detect the occurrence of head impacts. The AI ​​analyzes the movements of athletes and drivers from the video data and identifies abnormal movements and moments of impact. For example, if an athlete receives an impact to the head, the moment is captured and the intensity and direction of the impact are evaluated. It also analyzes video footage while driving to identify the moment of a traffic accident and evaluate the intensity and direction of the impact. As a result, the data collection unit can accurately detect head impacts during sports and driving and collect data in real time. Furthermore, the data collection unit can send the collected data to a cloud server and integrate with other systems and departments. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes video data collected by the collection unit in real time to detect head impacts. For example, the analysis unit uses AI to analyze video data and evaluate the intensity and direction of head impacts. Specifically, the AI ​​uses image recognition technology to identify the position of the player's or driver's head from the video data and detect the moment of impact. The AI ​​analyzes the movements of the player's or driver from the video data and identifies abnormal movements or moments of impact. For example, if a player receives a head impact, the moment is captured and the intensity and direction of the impact are evaluated. It also analyzes video footage of driving to identify the moment of a traffic accident and evaluate the intensity and direction of the impact. This allows the analysis unit to accurately detect head impacts during sports and driving and analyze the data in real time. Furthermore, the analysis unit can also utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past impact data, it can predict fluctuations in risk under specific situations and conditions and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The generation unit automatically generates a HIA protocol based on head impacts detected by the analysis unit, assessing the risk of concussion. For example, the generation unit uses AI to assess the risk of concussion and generate a protocol suggesting appropriate treatment. Specifically, the AI ​​calculates the risk of concussion based on collected data, evaluating the intensity and direction of the impact, and the condition of the athlete or driver. The AI ​​utilizes past data and medical knowledge to accurately assess the risk of concussion and generate a protocol suggesting appropriate treatment. For example, if an athlete receives a head impact during a sports match, the AI ​​assesses the intensity and direction of the impact and the athlete's condition to calculate the risk of concussion. If the AI ​​determines that the risk of concussion is high, it generates a protocol suggesting that the athlete take a rest. It can also analyze driving footage in real time, detect the moment of a traffic accident, assess the risk of concussion, and generate a protocol suggesting appropriate treatment. This allows the generation unit to accurately detect head impacts, assess the risk of concussion in real time, and provide appropriate protocols. Furthermore, the generation unit can store the generated protocols on a cloud server and integrate with other systems and departments. This allows the generation unit to efficiently and effectively generate protocols, thereby improving the overall system performance.

[0033] The evaluation unit can collect individual historical data and perform individual risk assessments. For example, the evaluation unit can collect a user's medical history and exercise history to assess the risk of concussion. The evaluation unit can use AI to analyze individual historical data and perform risk assessments. For example, the evaluation unit can reduce the risk of recurrence by providing a more stringent protocol to users who have previously experienced concussions. The evaluation unit can also assess the risk of concussion based on a user's exercise history and provide an appropriate protocol. This allows for the provision of personalized protocols and improved diagnostic accuracy by utilizing individual historical data. Some or all of the above-described processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input a user's medical history into a generating AI and have the generating AI perform the risk assessment.

[0034] The providing unit can provide the HIA protocol generated by the generating unit to the user. For example, the providing unit can send the generated HIA protocol to the user's device and suggest appropriate action. The providing unit can use AI to provide the generated protocol to the user. For example, if an athlete suffers a head injury during a sports match, the providing unit can generate an HIA protocol and suggest that the athlete take a rest. The providing unit can also analyze driving footage in real time, detect the moment of a traffic accident, and generate an HIA protocol. By providing the generated HIA protocol to the user, appropriate action can be taken quickly. Some or all of the above processing in the providing unit may be performed using AI or not. For example, the providing unit can input the generated HIA protocol into a generating AI and have the generating AI perform the protocol provision.

[0035] The custom protocol unit can provide a protocol tailored to each user based on the risk assessed by the evaluation unit. For example, the custom protocol unit can generate individual protocols based on the user's medical history and exercise history. The custom protocol unit can also use AI to generate protocols tailored to each user. For example, the custom protocol unit can reduce the risk of recurrence by providing a more stringent protocol to a user who has previously experienced a concussion. Furthermore, the custom protocol unit can assess the risk of concussion based on the user's exercise history and provide an appropriate protocol. This improves the accuracy of diagnosis by providing protocols tailored to each user. Some or all of the above-described processes in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's medical history into a generation AI and have the generation AI perform protocol generation.

[0036] The data collection unit can analyze past collected data and select an efficient collection method. For example, the data collection unit can identify the most effective collection timing from past collected data and reflect this in future collections. The data collection unit can also analyze past collected data to find areas for improvement in the collection method and optimize it. Based on past collected data, the data collection unit can customize the collection method and provide the optimal collection method for each user. This allows for the optimization of the collection method and efficient data collection by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select a collection method.

[0037] The data collection unit can filter video data based on the user's current activity and environment. For example, if the user is playing sports, the unit will prioritize collecting scenes with high activity. If the user is driving, the unit can also collect data considering road conditions and traffic information. If the user is going about their daily life, the unit can filter and collect data related to specific activities. This allows for the efficient collection of highly relevant data by filtering data according to the user's activity and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user activity and environment data into a generating AI and have the generating AI perform the filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting video data. For example, if the user is participating in a specific sporting event, the data collection unit will prioritize the collection of data related to that event. If the user is in a specific region, the data collection unit can also prioritize the collection of data related to that region. If the user is traveling, the data collection unit can also prioritize the collection of data related to tourist attractions and landmarks. This allows for the efficient collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the data.

[0039] The data collection unit can analyze the user's social media activity and collect relevant data when collecting video data. For example, the data collection unit can collect data related to events shared by the user on social media. The data collection unit can also collect data related to accounts followed by the user on social media. The data collection unit can also collect data related to topics the user has shown interest in on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the video data during the analysis. For example, the analysis unit can perform a detailed analysis on important scenes. The analysis unit can also perform a simplified analysis on general scenes. The analysis unit can also perform a special analysis on scenes related to specific events. This allows for efficient analysis by adjusting the level of detail according to the importance of the video data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the video data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the video data during analysis. For example, the analysis unit can apply a sports-specific analysis algorithm to sports video data. The analysis unit can also apply a driving-specific analysis algorithm to driving video data. The analysis unit can also apply a general analysis algorithm to everyday life video data. By applying the appropriate analysis algorithm according to the category of the video data, the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the video data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on when the video data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also prioritize the analysis of data related to a specific event. The analysis unit may also prioritize the analysis of current data while referring to past data. This allows for efficient analysis by determining the priority of analysis based on when the video data was collected. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the video data collection dates into a generating AI and have the generating AI perform the priority determination.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the video data during the analysis. For example, the analysis unit can prioritize the analysis of important scenes. The analysis unit can also postpone the analysis of general scenes. The analysis unit can also prioritize the analysis of scenes related to specific events. This allows for the prioritization of important scenes by adjusting the order of analysis based on the relevance of the video data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the video data into a generating AI and have the generating AI execute the order of analysis.

[0044] The generation unit can adjust the level of detail of the protocol based on the degree of concussion risk during generation. For example, in the case of high risk, the generation unit generates a detailed protocol. In the case of medium risk, the generation unit can also generate a general protocol. In the case of low risk, the generation unit can also generate a simplified protocol. This allows for the provision of an appropriate protocol by adjusting the level of detail of the protocol according to the degree of concussion risk. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the degree of concussion risk into a generation AI and have the generation AI perform the adjustment of the level of detail of the protocol.

[0045] The generation unit can apply different protocol generation algorithms depending on the concussion category during generation. For example, for concussions caused by sports, the generation unit can apply a protocol generation algorithm specifically for sports. For concussions caused by traffic accidents, the generation unit can also apply a protocol generation algorithm specifically for traffic accidents. For concussions caused by daily life, the generation unit can also apply a general protocol generation algorithm. This allows for the generation of the optimal protocol according to the concussion category. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the concussion category into a generation AI and have the generation AI execute the application of the protocol generation algorithm.

[0046] The generation unit can determine the priority of protocols based on the timing of concussion occurrence during generation. For example, the generation unit will prioritize the generation of protocols for recently occurring concussions. The generation unit can also generate general protocols for concussions that have occurred in the past. The generation unit can also generate special protocols for concussions related to specific events. This allows for a rapid response by prioritizing protocols based on the timing of concussion occurrence. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the timing of concussion occurrence into a generation AI and have the generation AI perform the determination of protocol priority.

[0047] The generation unit can adjust the order of protocols based on the relevance of the concussion during generation. For example, the generation unit can prioritize the generation of protocols for critical concussions. The generation unit can also postpone the generation of protocols for general concussions. The generation unit can also generate special protocols for concussions related to specific events. This allows for the priority provision of important protocols by adjusting the order of protocols based on the relevance of the concussion. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of the concussion into a generation AI and have the generation AI execute the protocol order.

[0048] The evaluation unit can optimize its evaluation algorithm by referring to past risk assessment data during the evaluation process. For example, the evaluation unit can adjust the evaluation algorithm based on past risk assessment data to improve accuracy. The evaluation unit can also analyze past risk assessment data to identify areas for improvement in the evaluation algorithm and optimize it. The evaluation unit can also customize the evaluation algorithm while referring to past risk assessment data to provide the optimal evaluation for each user. In this way, by referring to past risk assessment data, the evaluation algorithm can be optimized and accuracy improved. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past risk assessment data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0049] The evaluation unit can improve the accuracy of risk assessment based on the user's health history during the evaluation process. For example, the evaluation unit can refer to the user's past health history to improve the accuracy of risk assessment. The evaluation unit can also analyze the user's health history to identify areas for improvement in risk assessment and optimize it. The evaluation unit can also customize risk assessment based on the user's health history and provide an optimal assessment for each user. This allows for the provision of a more accurate risk assessment by improving the accuracy of risk assessment based on the user's health history. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's health history into a generating AI and have the generating AI perform the task of improving the accuracy of risk assessment.

[0050] The evaluation unit can perform risk assessments while considering the user's geographical location information. For example, if the user is in a specific region, the evaluation unit can perform risk assessments related to that region. If the user is traveling, the evaluation unit can also perform risk assessments related to the travel destination. If the user is participating in a specific event, the evaluation unit can also perform risk assessments related to that event. By considering the user's geographical location information, it is possible to provide highly relevant risk assessments. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform the risk assessment.

[0051] The evaluation unit can improve the accuracy of risk assessments by analyzing users' social media activity during the evaluation process. For example, the evaluation unit can improve the accuracy of risk assessments based on information shared by users on social media. The evaluation unit can also perform risk assessments related to accounts that users follow on social media. The evaluation unit can also perform risk assessments related to topics that users have shown interest in on social media. In this way, the accuracy of risk assessments can be improved by analyzing users' social media activity. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of risk assessments.

[0052] The service provider can select the optimal service delivery method by referring to the user's past protocol history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's past protocol history. The service provider can also analyze the user's past protocol history to identify areas for improvement in the service delivery method and optimize it. The service provider can also customize the service delivery method while referring to the user's past protocol history and provide the optimal service delivery method for each user. This allows the service provider to select the optimal service delivery method by referring to the user's past protocol history and deliver protocols efficiently. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past protocol history into a generating AI and have the generating AI perform the selection of the service delivery method.

[0053] The service provider can customize the protocol's content based on the user's current health status at the time of delivery. For example, the service provider can refer to the user's current health status and customize the protocol's content. The service provider can also analyze the user's health status, identify areas for improvement in the content, and optimize it. Based on the user's health status, the service provider can customize the protocol's content to provide the most suitable content for each user. This allows the service provider to provide the most suitable protocol for each user by customizing the protocol's content based on the user's current health status. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the user's current health status into a generating AI and have the generating AI perform the customization of the content.

[0054] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can also provide a delivery method optimized for a larger screen. If the user is using a smartwatch, the delivery unit can also provide a concise and highly visible delivery method. This allows the delivery unit to select the optimal delivery method and efficiently deliver the protocol by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI perform the selection of the delivery method.

[0055] The service provider can analyze the user's social media activity and customize the content of the protocol at the time of delivery. For example, the service provider can customize the content of the protocol based on information shared by the user on social media. The service provider can also provide protocols related to accounts that the user follows on social media. The service provider can also provide protocols related to topics that the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the service provider can customize the content of the protocol and provide the most suitable protocol for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content of the service.

[0056] The custom protocol unit can generate the optimal protocol by referring to the user's past health history when generating a custom protocol. For example, the custom protocol unit generates the optimal custom protocol based on the user's past health history. The custom protocol unit can also analyze the user's health history, find areas for improvement in the custom protocol, and optimize it. The custom protocol unit can also customize the custom protocol while referring to the user's health history, and provide the optimal protocol for each user. In this way, by referring to the user's past health history, the optimal custom protocol can be generated and the optimal protocol for the user can be provided. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's past health history into a generation AI and have the generation AI execute the protocol generation.

[0057] The custom protocol unit can customize the content of a custom protocol based on the user's current living situation when generating it. For example, the custom protocol unit can refer to the user's current living situation and customize the content of the custom protocol. The custom protocol unit can also analyze the user's living situation, find areas for improvement in the custom protocol, and optimize it. Based on the user's living situation, the custom protocol unit can customize the content of the custom protocol and provide the optimal protocol for each user. This allows the system to provide the optimal protocol for each user by customizing the protocol content based on the user's current living situation. Some or all of the above-described processes in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's current living situation into a generation AI and have the generation AI perform the customization of the protocol content.

[0058] The custom protocol unit can generate the optimal protocol by considering the user's geographical location information when generating a custom protocol. For example, if the user is in a specific region, the custom protocol unit can generate a custom protocol related to that region. If the user is traveling, the custom protocol unit can also generate a custom protocol for the travel destination. If the user is participating in a specific event, the custom protocol unit can also generate a custom protocol related to that event. In this way, by considering the user's geographical location information, the optimal custom protocol can be generated and the most suitable protocol for the user can be provided. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's geographical location information into a generation AI and have the generation AI perform the protocol generation.

[0059] The custom protocol unit can analyze a user's social media activity and customize the content of the protocol when generating a custom protocol. For example, the custom protocol unit can customize the content of the custom protocol based on information shared by the user on social media. The custom protocol unit can also provide custom protocols related to accounts that the user follows on social media. The custom protocol unit can also provide custom protocols related to topics that the user has shown interest in on social media. This allows the protocol content to be customized by analyzing the user's social media activity, providing the optimal protocol for the user. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's social media activity data into a generation AI and have the generation AI perform the customization of the protocol content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit collects the user's biometric data and can adjust the timing of video data collection based on the collected biometric data. For example, the data collection unit can monitor the user's heart rate and body temperature, and increase the collection frequency if an abnormality is detected. The data collection unit can also slow down the collection frequency if the user's biometric data is stable. This allows for data collection without missing important moments by adjusting the collection timing according to the user's biometric data.

[0062] The data collection unit collects user activity data and can adjust the timing of video data collection based on the collected activity data. For example, if the user is exercising, the data collection unit will increase the frequency of collection to collect detailed data. If the user is resting, the data collection unit can also decrease the frequency of collection. This allows for data collection of important moments without missing any important events by adjusting the collection timing according to the user's activity data.

[0063] The analysis department can determine the priority of analysis based on when the video data was collected. For example, the analysis department might prioritize analyzing recently collected data. The analysis department could also prioritize analyzing data related to a specific event. This allows for efficient analysis by prioritizing analysis based on when the video data was collected.

[0064] The generation unit can adjust the level of detail of the protocol based on the degree of concussion risk. For example, in the case of high risk, the generation unit generates a detailed protocol. In the case of moderate risk, the generation unit can also generate a general protocol. This allows for the provision of an appropriate protocol by adjusting the level of detail according to the degree of concussion risk.

[0065] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, the service provider will provide a delivery method that matches the screen size. If the user is using a tablet, the service provider can also provide a delivery method optimized for the larger screen. This allows the service provider to select the optimal delivery method by considering the user's device information and deliver the protocol efficiently.

[0066] The evaluation unit can improve the accuracy of risk assessments based on the user's health history. For example, the evaluation unit can refer to the user's past health history to improve the accuracy of risk assessments. The evaluation unit can also analyze the user's health history to identify areas for improvement in risk assessments and optimize them. This allows for the provision of more accurate risk assessments by improving the accuracy of risk assessments based on the user's health history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects video data. The collection unit can, for example, collect video in real time during sports or while driving. The collection unit acquires video data using cameras and sensors. For example, the collection unit can track the movements of athletes during a sports match and capture the moment an impact to the head occurs. The collection unit can also collect video in real time while driving and capture the moment of a traffic accident. Step 2: The analysis unit analyzes the video data collected by the collection unit in real time to detect head impacts. The analysis unit, for example, uses AI to analyze the video data and evaluate the intensity and direction of the impact to the head. The analysis unit can use AI to detect the occurrence of head impacts in real time. For example, if an athlete is hit in the head during a sports match, the analysis unit can detect the moment and assess the risk of concussion. The analysis unit can also analyze video footage while driving in real time to detect the moment of a traffic accident. Step 3: The generation unit assesses the risk of concussion based on the head impact detected by the analysis unit and automatically generates a Head Injury Assessment Protocol (HIA protocol). For example, the generation unit generates a protocol that uses AI to assess the risk of concussion and proposes appropriate treatment. The generation unit can use AI to assess the risk of concussion in real time and automatically generate an HIA protocol. For example, if an athlete suffers a head impact during a sports match, the generation unit can generate an HIA protocol and suggest that the athlete take a rest. The generation unit can also analyze driving footage in real time, detect the moment of a traffic accident, and generate an HIA protocol.

[0069] (Example of form 2) The head impact detection system according to an embodiment of the present invention is a system that accurately detects impacts to the head, assesses the risk of concussion in real time, and provides an appropriate protocol. The head impact detection system uses an AI agent to analyze video footage of sports or driving in real time and warns of the potential risk of concussion the moment a head impact occurs. If a concussion is suspected, the system automatically generates and presents a Health Impact Assessment (HIA) protocol. For example, if an athlete receives a head impact during a sports match, the AI ​​detects the moment and assesses the risk of concussion. Next, if a concussion is suspected, the system automatically generates and presents an HIA protocol. The HIA protocol assesses the risk of concussion and suggests appropriate treatment. For example, if an athlete receives a head impact, the AI ​​generates an HIA protocol and suggests that the athlete take a rest. Furthermore, the head impact detection system adds a custom risk assessment function that utilizes individual history data. This improves the accuracy of providing protocols tailored to each user and the diagnosis. For example, a more stringent protocol can be provided to athletes who have previously experienced concussions to reduce the risk of recurrence. This system makes it possible to accurately assess the risk of concussion in situations such as sports and traffic accidents, and to provide appropriate treatment. This allows for the provision of rapid and appropriate measures, improving safety and health. As a result, the head impact detection system can accurately detect impacts to the head, assess the risk of concussion in real time, and provide appropriate protocols.

[0070] The head impact detection system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects video data. The collection unit can collect video in real time, for example, during sports or driving. The collection unit acquires video data using cameras and sensors. For example, the collection unit tracks the movements of players during a sports match and captures the moment an impact to the head occurs. The collection unit can also collect video in real time while driving and capture the moment of a traffic accident. The collection unit can also analyze video data using AI to detect the occurrence of a head impact. The analysis unit analyzes the video data collected by the collection unit in real time and detects head impacts. The analysis unit, for example, analyzes video data using AI to evaluate the intensity and direction of the impact to the head. The analysis unit can use AI to detect the occurrence of a head impact in real time. For example, if a player receives an impact to the head during a sports match, the analysis unit can detect the moment and evaluate the risk of concussion. The analysis unit can also analyze video while driving in real time and detect the moment of a traffic accident. The generation unit evaluates the risk of concussion based on the head impact detected by the analysis unit and automatically generates an HIA protocol. The generation unit generates a protocol that, for example, uses AI to evaluate the risk of concussion and proposes appropriate treatment. The generation unit can use AI to evaluate the risk of concussion in real time and automatically generate an HIA protocol. For example, if an athlete receives a head impact during a sports match, the generation unit generates an HIA protocol and suggests that the athlete take a rest. The generation unit can also analyze video footage while driving in real time, detect the moment of a traffic accident, and generate an HIA protocol. As a result, the head impact detection system according to this embodiment can accurately detect impacts to the head, evaluate the risk of concussion in real time, and provide an appropriate protocol.

[0071] The data collection unit collects video data. For example, the data collection unit can collect video footage of sports or driving in real time. The data collection unit acquires video data using cameras and sensors. Specifically, to track the movements of athletes during a sports match, multiple high-resolution cameras are installed to capture the athletes' movements from various angles. This allows for the accurate capture of the moment when an impact to the head occurs. In addition, to collect video footage while driving, dashcams and in-car cameras are installed in vehicles to record the driver's movements and surrounding conditions in real time. This allows for the capture of the moment of a traffic accident. The data collection unit can also use AI to analyze video data and detect the occurrence of head impacts. The AI ​​analyzes the movements of athletes and drivers from the video data and identifies abnormal movements and moments of impact. For example, if an athlete receives an impact to the head, the moment is captured and the intensity and direction of the impact are evaluated. It also analyzes video footage while driving to identify the moment of a traffic accident and evaluate the intensity and direction of the impact. As a result, the data collection unit can accurately detect head impacts during sports and driving and collect data in real time. Furthermore, the data collection unit can send the collected data to a cloud server and integrate with other systems and departments. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes video data collected by the collection unit in real time to detect head impacts. For example, the analysis unit uses AI to analyze video data and evaluate the intensity and direction of head impacts. Specifically, the AI ​​uses image recognition technology to identify the position of the player's or driver's head from the video data and detect the moment of impact. The AI ​​analyzes the movements of the player's or driver from the video data and identifies abnormal movements or moments of impact. For example, if a player receives a head impact, the moment is captured and the intensity and direction of the impact are evaluated. It also analyzes video footage of driving to identify the moment of a traffic accident and evaluate the intensity and direction of the impact. This allows the analysis unit to accurately detect head impacts during sports and driving and analyze the data in real time. Furthermore, the analysis unit can also utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past impact data, it can predict fluctuations in risk under specific situations and conditions and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0073] The generation unit automatically generates a HIA protocol based on head impacts detected by the analysis unit, assessing the risk of concussion. For example, the generation unit uses AI to assess the risk of concussion and generate a protocol suggesting appropriate treatment. Specifically, the AI ​​calculates the risk of concussion based on collected data, evaluating the intensity and direction of the impact, and the condition of the athlete or driver. The AI ​​utilizes past data and medical knowledge to accurately assess the risk of concussion and generate a protocol suggesting appropriate treatment. For example, if an athlete receives a head impact during a sports match, the AI ​​assesses the intensity and direction of the impact and the athlete's condition to calculate the risk of concussion. If the AI ​​determines that the risk of concussion is high, it generates a protocol suggesting that the athlete take a rest. It can also analyze driving footage in real time, detect the moment of a traffic accident, assess the risk of concussion, and generate a protocol suggesting appropriate treatment. This allows the generation unit to accurately detect head impacts, assess the risk of concussion in real time, and provide appropriate protocols. Furthermore, the generation unit can store the generated protocols on a cloud server and integrate with other systems and departments. This allows the generation unit to efficiently and effectively generate protocols, thereby improving the overall system performance.

[0074] The evaluation unit can collect individual historical data and perform individual risk assessments. For example, the evaluation unit can collect a user's medical history and exercise history to assess the risk of concussion. The evaluation unit can use AI to analyze individual historical data and perform risk assessments. For example, the evaluation unit can reduce the risk of recurrence by providing a more stringent protocol to users who have previously experienced concussions. The evaluation unit can also assess the risk of concussion based on a user's exercise history and provide an appropriate protocol. This allows for the provision of personalized protocols and improved diagnostic accuracy by utilizing individual historical data. Some or all of the above-described processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input a user's medical history into a generating AI and have the generating AI perform the risk assessment.

[0075] The providing unit can provide the HIA protocol generated by the generating unit to the user. For example, the providing unit can send the generated HIA protocol to the user's device and suggest appropriate action. The providing unit can use AI to provide the generated protocol to the user. For example, if an athlete suffers a head injury during a sports match, the providing unit can generate an HIA protocol and suggest that the athlete take a rest. The providing unit can also analyze driving footage in real time, detect the moment of a traffic accident, and generate an HIA protocol. By providing the generated HIA protocol to the user, appropriate action can be taken quickly. Some or all of the above processing in the providing unit may be performed using AI or not. For example, the providing unit can input the generated HIA protocol into a generating AI and have the generating AI perform the protocol provision.

[0076] The custom protocol unit can provide a protocol tailored to each user based on the risk assessed by the evaluation unit. For example, the custom protocol unit can generate individual protocols based on the user's medical history and exercise history. The custom protocol unit can also use AI to generate protocols tailored to each user. For example, the custom protocol unit can reduce the risk of recurrence by providing a more stringent protocol to a user who has previously experienced a concussion. Furthermore, the custom protocol unit can assess the risk of concussion based on the user's exercise history and provide an appropriate protocol. This improves the accuracy of diagnosis by providing protocols tailored to each user. Some or all of the above-described processes in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's medical history into a generation AI and have the generation AI perform protocol generation.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of video data collection based on the estimated emotions. For example, if the user is tense, the data collection unit can increase the collection frequency to collect detailed data. If the user is relaxed, the data collection unit can also decrease the collection frequency to collect only the minimum necessary data. If the user is excited, the data collection unit can adjust the collection timing to ensure that important moments are not missed. This allows for data collection without missing important moments by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0078] The data collection unit can analyze past collected data and select an efficient collection method. For example, the data collection unit can identify the most effective collection timing from past collected data and reflect this in future collections. The data collection unit can also analyze past collected data to find areas for improvement in the collection method and optimize it. Based on past collected data, the data collection unit can customize the collection method and provide the optimal collection method for each user. This allows for the optimization of the collection method and efficient data collection by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select a collection method.

[0079] The data collection unit can filter video data based on the user's current activity and environment. For example, if the user is playing sports, the unit will prioritize collecting scenes with high activity. If the user is driving, the unit can also collect data considering road conditions and traffic information. If the user is going about their daily life, the unit can filter and collect data related to specific activities. This allows for the efficient collection of highly relevant data by filtering data according to the user's activity and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user activity and environment data into a generating AI and have the generating AI perform the filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of video data to collect based on the estimated emotions. For example, if the user is tense, the data collection unit may prioritize collecting important scenes. If the user is relaxed, the data collection unit may also prioritize collecting general scenes. If the user is excited, the data collection unit may also prioritize collecting scenes related to specific events. This allows for the priority collection of important scenes by determining the priority of video data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting video data. For example, if the user is participating in a specific sporting event, the data collection unit will prioritize the collection of data related to that event. If the user is in a specific region, the data collection unit can also prioritize the collection of data related to that region. If the user is traveling, the data collection unit can also prioritize the collection of data related to tourist attractions and landmarks. This allows for the efficient collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the data.

[0082] The data collection unit can analyze the user's social media activity and collect relevant data when collecting video data. For example, the data collection unit can collect data related to events shared by the user on social media. The data collection unit can also collect data related to accounts followed by the user on social media. The data collection unit can also collect data related to topics the user has shown interest in on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. If the user is excited, the analysis unit can also provide a visually stimulating presentation. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the video data during the analysis. For example, the analysis unit can perform a detailed analysis on important scenes. The analysis unit can also perform a simplified analysis on general scenes. The analysis unit can also perform a special analysis on scenes related to specific events. This allows for efficient analysis by adjusting the level of detail according to the importance of the video data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the video data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0085] The analysis unit can apply different analysis algorithms depending on the category of the video data during analysis. For example, the analysis unit can apply a sports-specific analysis algorithm to sports video data. The analysis unit can also apply a driving-specific analysis algorithm to driving video data. The analysis unit can also apply a general analysis algorithm to everyday life video data. By applying the appropriate analysis algorithm according to the category of the video data, the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the video data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with detailed explanations. If the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0087] The analysis unit can determine the priority of analysis based on when the video data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also prioritize the analysis of data related to a specific event. The analysis unit may also prioritize the analysis of current data while referring to past data. This allows for efficient analysis by determining the priority of analysis based on when the video data was collected. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the video data collection dates into a generating AI and have the generating AI perform the priority determination.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the video data during the analysis. For example, the analysis unit can prioritize the analysis of important scenes. The analysis unit can also postpone the analysis of general scenes. The analysis unit can also prioritize the analysis of scenes related to specific events. This allows for the prioritization of important scenes by adjusting the order of analysis based on the relevance of the video data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the video data into a generating AI and have the generating AI execute the order of analysis.

[0089] The generation unit can estimate the user's emotions and adjust the way the generated protocol is expressed based on the estimated user emotions. For example, if the user is tense, the generation unit can generate a simple and highly visual protocol. If the user is relaxed, the generation unit can also generate a protocol that includes detailed information. If the user is excited, the generation unit can also generate a visually stimulating protocol. In this way, by adjusting the way the protocol is expressed according to the user's emotions, a protocol that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the way the protocol is expressed.

[0090] The generation unit can adjust the level of detail of the protocol based on the degree of concussion risk during generation. For example, in the case of high risk, the generation unit generates a detailed protocol. In the case of medium risk, the generation unit can also generate a general protocol. In the case of low risk, the generation unit can also generate a simplified protocol. This allows for the provision of an appropriate protocol by adjusting the level of detail of the protocol according to the degree of concussion risk. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the degree of concussion risk into a generation AI and have the generation AI perform the adjustment of the level of detail of the protocol.

[0091] The generation unit can apply different protocol generation algorithms depending on the concussion category during generation. For example, for concussions caused by sports, the generation unit can apply a protocol generation algorithm specifically for sports. For concussions caused by traffic accidents, the generation unit can also apply a protocol generation algorithm specifically for traffic accidents. For concussions caused by daily life, the generation unit can also apply a general protocol generation algorithm. This allows for the generation of the optimal protocol according to the concussion category. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the concussion category into a generation AI and have the generation AI execute the application of the protocol generation algorithm.

[0092] The generation unit can estimate the user's emotions and adjust the length of the protocol it generates based on the estimated emotions. For example, if the user is tense, the generation unit can generate a short, concise protocol. If the user is relaxed, the generation unit can also generate a longer protocol with detailed explanations. If the user is excited, the generation unit can also generate a protocol with visually stimulating effects. By adjusting the length of the protocol according to the user's emotions, it is possible to provide a protocol of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the protocol.

[0093] The generation unit can determine the priority of protocols based on the timing of concussion occurrence during generation. For example, the generation unit will prioritize the generation of protocols for recently occurring concussions. The generation unit can also generate general protocols for concussions that have occurred in the past. The generation unit can also generate special protocols for concussions related to specific events. This allows for a rapid response by prioritizing protocols based on the timing of concussion occurrence. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the timing of concussion occurrence into a generation AI and have the generation AI perform the determination of protocol priority.

[0094] The generation unit can adjust the order of protocols based on the relevance of the concussion during generation. For example, the generation unit can prioritize the generation of protocols for critical concussions. The generation unit can also postpone the generation of protocols for general concussions. The generation unit can also generate special protocols for concussions related to specific events. This allows for the priority provision of important protocols by adjusting the order of protocols based on the relevance of the concussion. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of the concussion into a generation AI and have the generation AI execute the protocol order.

[0095] The evaluation unit can estimate the user's emotions and adjust the risk assessment method based on the estimated user emotions. For example, if the user is tense, the evaluation unit can provide a simple and highly visual risk assessment method. If the user is relaxed, the evaluation unit can also provide a risk assessment method that includes detailed information. If the user is excited, the evaluation unit can also provide a visually stimulating risk assessment method. By adjusting the risk assessment method according to the user's emotions, it is possible to provide a risk assessment that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the risk assessment method.

[0096] The evaluation unit can optimize its evaluation algorithm by referring to past risk assessment data during the evaluation process. For example, the evaluation unit can adjust the evaluation algorithm based on past risk assessment data to improve accuracy. The evaluation unit can also analyze past risk assessment data to identify areas for improvement in the evaluation algorithm and optimize it. The evaluation unit can also customize the evaluation algorithm while referring to past risk assessment data to provide the optimal evaluation for each user. In this way, by referring to past risk assessment data, the evaluation algorithm can be optimized and accuracy improved. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past risk assessment data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0097] The evaluation unit can improve the accuracy of risk assessment based on the user's health history during the evaluation process. For example, the evaluation unit can refer to the user's past health history to improve the accuracy of risk assessment. The evaluation unit can also analyze the user's health history to identify areas for improvement in risk assessment and optimize it. The evaluation unit can also customize risk assessment based on the user's health history and provide an optimal assessment for each user. This allows for the provision of a more accurate risk assessment by improving the accuracy of risk assessment based on the user's health history. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's health history into a generating AI and have the generating AI perform the task of improving the accuracy of risk assessment.

[0098] The evaluation unit can estimate the user's emotions and determine the priority of risk assessments based on the estimated user emotions. For example, if the user is tense, the evaluation unit may prioritize important risk assessments. If the user is relaxed, the evaluation unit may also prioritize general risk assessments. If the user is excited, the evaluation unit may also prioritize risk assessments related to specific events. This allows for prioritizing important risk assessments by determining the priority of risk assessments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI determine the priority of risk assessments.

[0099] The evaluation unit can perform risk assessments while considering the user's geographical location information. For example, if the user is in a specific region, the evaluation unit can perform risk assessments related to that region. If the user is traveling, the evaluation unit can also perform risk assessments related to the travel destination. If the user is participating in a specific event, the evaluation unit can also perform risk assessments related to that event. By considering the user's geographical location information, it is possible to provide highly relevant risk assessments. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform the risk assessment.

[0100] The evaluation unit can improve the accuracy of risk assessments by analyzing users' social media activity during the evaluation process. For example, the evaluation unit can improve the accuracy of risk assessments based on information shared by users on social media. The evaluation unit can also perform risk assessments related to accounts that users follow on social media. The evaluation unit can also perform risk assessments related to topics that users have shown interest in on social media. In this way, the accuracy of risk assessments can be improved by analyzing users' social media activity. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of risk assessments.

[0101] The service provider can estimate the user's emotions and adjust the protocol delivery method based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visual delivery method. If the user is relaxed, the service provider can also provide a delivery method that includes detailed information. If the user is excited, the service provider can also provide a visually stimulating delivery method. By adjusting the protocol delivery method according to the user's emotions, the service provider can provide a protocol that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the protocol delivery method.

[0102] The service provider can select the optimal service delivery method by referring to the user's past protocol history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's past protocol history. The service provider can also analyze the user's past protocol history to identify areas for improvement in the service delivery method and optimize it. The service provider can also customize the service delivery method while referring to the user's past protocol history and provide the optimal service delivery method for each user. This allows the service provider to select the optimal service delivery method by referring to the user's past protocol history and deliver protocols efficiently. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past protocol history into a generating AI and have the generating AI perform the selection of the service delivery method.

[0103] The service provider can customize the protocol's content based on the user's current health status at the time of delivery. For example, the service provider can refer to the user's current health status and customize the protocol's content. The service provider can also analyze the user's health status, identify areas for improvement in the content, and optimize it. Based on the user's health status, the service provider can customize the protocol's content to provide the most suitable content for each user. This allows the service provider to provide the most suitable protocol for each user by customizing the protocol's content based on the user's current health status. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the user's current health status into a generating AI and have the generating AI perform the customization of the content.

[0104] The service provider can estimate the user's emotions and adjust the order in which protocols are delivered based on the estimated emotions. For example, if the user is tense, the service provider may prioritize important protocols. If the user is relaxed, the service provider may also prioritize general protocols. If the user is excited, the service provider may also prioritize protocols related to specific events. This allows for the prioritization of important protocols by adjusting the order in which they are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the delivery order.

[0105] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can also provide a delivery method optimized for a larger screen. If the user is using a smartwatch, the delivery unit can also provide a concise and highly visible delivery method. This allows the delivery unit to select the optimal delivery method and efficiently deliver the protocol by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI perform the selection of the delivery method.

[0106] The service provider can analyze the user's social media activity and customize the content of the protocol at the time of delivery. For example, the service provider can customize the content of the protocol based on information shared by the user on social media. The service provider can also provide protocols related to accounts that the user follows on social media. The service provider can also provide protocols related to topics that the user has shown interest in on social media. In this way, by analyzing the user's social media activity, the service provider can customize the content of the protocol and provide the most suitable protocol for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content of the service.

[0107] The custom protocol unit can estimate the user's emotions and adjust the method of generating the custom protocol based on the estimated user emotions. For example, if the user is tense, the custom protocol unit can generate a simple and highly visual custom protocol. If the user is relaxed, the custom protocol unit can also generate a custom protocol that includes detailed information. If the user is excited, the custom protocol unit can also generate a custom protocol that is visually stimulating. In this way, by adjusting the method of generating the custom protocol according to the user's emotions, it is possible to provide a custom protocol that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input user emotion data into a generative AI and have the generative AI adjust the method of generating the custom protocol.

[0108] The custom protocol unit can generate the optimal protocol by referring to the user's past health history when generating a custom protocol. For example, the custom protocol unit generates the optimal custom protocol based on the user's past health history. The custom protocol unit can also analyze the user's health history, find areas for improvement in the custom protocol, and optimize it. The custom protocol unit can also customize the custom protocol while referring to the user's health history, and provide the optimal protocol for each user. In this way, by referring to the user's past health history, the optimal custom protocol can be generated and the optimal protocol for the user can be provided. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's past health history into a generation AI and have the generation AI execute the protocol generation.

[0109] The custom protocol unit can customize the content of a custom protocol based on the user's current living situation when generating it. For example, the custom protocol unit can refer to the user's current living situation and customize the content of the custom protocol. The custom protocol unit can also analyze the user's living situation, find areas for improvement in the custom protocol, and optimize it. Based on the user's living situation, the custom protocol unit can customize the content of the custom protocol and provide the optimal protocol for each user. This allows the system to provide the optimal protocol for each user by customizing the protocol content based on the user's current living situation. Some or all of the above-described processes in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's current living situation into a generation AI and have the generation AI perform the customization of the protocol content.

[0110] The custom protocol unit can estimate the user's emotions and determine the priority of custom protocols based on the estimated emotions. For example, if the user is tense, the custom protocol unit may prioritize providing important custom protocols. If the user is relaxed, the custom protocol unit may also prioritize providing general custom protocols. If the user is excited, the custom protocol unit may also prioritize providing custom protocols related to specific events. This allows for the priority of important custom protocols to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0111] The custom protocol unit can generate the optimal protocol by considering the user's geographical location information when generating a custom protocol. For example, if the user is in a specific region, the custom protocol unit can generate a custom protocol related to that region. If the user is traveling, the custom protocol unit can also generate a custom protocol for the travel destination. If the user is participating in a specific event, the custom protocol unit can also generate a custom protocol related to that event. In this way, by considering the user's geographical location information, the optimal custom protocol can be generated and the most suitable protocol for the user can be provided. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's geographical location information into a generation AI and have the generation AI perform the protocol generation.

[0112] The custom protocol unit can analyze a user's social media activity and customize the content of the protocol when generating a custom protocol. For example, the custom protocol unit can customize the content of the custom protocol based on information shared by the user on social media. The custom protocol unit can also provide custom protocols related to accounts that the user follows on social media. The custom protocol unit can also provide custom protocols related to topics that the user has shown interest in on social media. This allows the protocol content to be customized by analyzing the user's social media activity, providing the optimal protocol for the user. Some or all of the above processing in the custom protocol unit may be performed using AI or not. For example, the custom protocol unit can input the user's social media activity data into a generation AI and have the generation AI perform the customization of the protocol content.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit collects the user's biometric data and can adjust the timing of video data collection based on the collected biometric data. For example, the data collection unit can monitor the user's heart rate and body temperature, and increase the collection frequency if an abnormality is detected. The data collection unit can also slow down the collection frequency if the user's biometric data is stable. This allows for data collection without missing important moments by adjusting the collection timing according to the user's biometric data.

[0115] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on those emotions. For example, if the user is feeling tense, the analysis unit will prioritize analyzing important scenes. If the user is relaxed, the analysis unit can also prioritize analyzing general scenes. This allows for prioritizing the analysis based on the user's emotions, thereby ensuring that important scenes are analyzed first.

[0116] The generation unit can estimate the user's emotions and adjust the content of the protocol it generates based on those emotions. For example, if the user is tense, the generation unit will generate a simple and highly visual protocol. If the user is relaxed, the generation unit can also generate a protocol that includes more detailed information. By adjusting the content of the protocol according to the user's emotions, it is possible to provide a protocol that is easy for the user to understand.

[0117] The delivery unit can estimate the user's emotions and adjust the protocol delivery method based on the estimated emotions. For example, if the user is nervous, the delivery unit will provide a simple and highly visual delivery method. If the user is relaxed, the delivery unit may also provide a delivery method that includes detailed information. By adjusting the protocol delivery method according to the user's emotions, it is possible to provide a protocol that is easy for the user to understand.

[0118] The evaluation unit can estimate the user's emotions and adjust the risk assessment method based on the estimated emotions. For example, if the user is tense, the evaluation unit provides a simple and highly visual risk assessment method. If the user is relaxed, the evaluation unit can also provide a risk assessment method that includes detailed information. By adjusting the risk assessment method according to the user's emotions, it is possible to provide a risk assessment that is easy for the user to understand.

[0119] The data collection unit collects user activity data and can adjust the timing of video data collection based on the collected activity data. For example, if the user is exercising, the data collection unit will increase the frequency of collection to collect detailed data. If the user is resting, the data collection unit can also decrease the frequency of collection. This allows for data collection of important moments without missing any important events by adjusting the collection timing according to the user's activity data.

[0120] The analysis department can determine the priority of analysis based on when the video data was collected. For example, the analysis department might prioritize analyzing recently collected data. The analysis department could also prioritize analyzing data related to a specific event. This allows for efficient analysis by prioritizing analysis based on when the video data was collected.

[0121] The generation unit can adjust the level of detail of the protocol based on the degree of concussion risk. For example, in the case of high risk, the generation unit generates a detailed protocol. In the case of moderate risk, the generation unit can also generate a general protocol. This allows for the provision of an appropriate protocol by adjusting the level of detail according to the degree of concussion risk.

[0122] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, the service provider will provide a delivery method that matches the screen size. If the user is using a tablet, the service provider can also provide a delivery method optimized for the larger screen. This allows the service provider to select the optimal delivery method by considering the user's device information and deliver the protocol efficiently.

[0123] The evaluation unit can improve the accuracy of risk assessments based on the user's health history. For example, the evaluation unit can refer to the user's past health history to improve the accuracy of risk assessments. The evaluation unit can also analyze the user's health history to identify areas for improvement in risk assessments and optimize them. This allows for the provision of more accurate risk assessments by improving the accuracy of risk assessments based on the user's health history.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The collection unit collects video data. The collection unit can, for example, collect video in real time during sports or while driving. The collection unit acquires video data using cameras and sensors. For example, the collection unit can track the movements of athletes during a sports match and capture the moment an impact to the head occurs. The collection unit can also collect video in real time while driving and capture the moment of a traffic accident. Step 2: The analysis unit analyzes the video data collected by the collection unit in real time to detect head impacts. The analysis unit, for example, uses AI to analyze the video data and evaluate the intensity and direction of the impact to the head. The analysis unit can use AI to detect the occurrence of head impacts in real time. For example, if an athlete is hit in the head during a sports match, the analysis unit can detect the moment and assess the risk of concussion. The analysis unit can also analyze video footage while driving in real time to detect the moment of a traffic accident. Step 3: The generation unit assesses the risk of concussion based on the head impact detected by the analysis unit and automatically generates a Head Injury Assessment Protocol (HIA protocol). For example, the generation unit generates a protocol that uses AI to assess the risk of concussion and proposes appropriate treatment. The generation unit can use AI to assess the risk of concussion in real time and automatically generate an HIA protocol. For example, if an athlete suffers a head impact during a sports match, the generation unit can generate an HIA protocol and suggest that the athlete take a rest. The generation unit can also analyze driving footage in real time, detect the moment of a traffic accident, and generate an HIA protocol.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, evaluation unit, provision unit, and custom protocol unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects video data using the camera 42 and sensors of the smart device 14 and adjusts the collection timing with the control unit 46A. The analysis unit analyzes the video data in real time with the specific processing unit 290 of the data processing unit 12 and detects head impacts. The generation unit evaluates the risk of concussion with the specific processing unit 290 of the data processing unit 12 and automatically generates an HIA protocol. The evaluation unit analyzes the individual's historical data with the specific processing unit 290 of the data processing unit 12 and performs a risk assessment. The provision unit provides the HIA protocol generated by the control unit 46A of the smart device 14 to the user. The custom protocol unit generates a protocol tailored to each user with the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, evaluation unit, provision unit, and custom protocol unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects video data using the camera 42 and sensors of the smart glasses 214 and adjusts the collection timing with the control unit 46A. The analysis unit analyzes the video data in real time with the specific processing unit 290 of the data processing unit 12 and detects head impacts. The generation unit evaluates the risk of concussion with the specific processing unit 290 of the data processing unit 12 and automatically generates an HIA protocol. The evaluation unit analyzes the individual's historical data with the specific processing unit 290 of the data processing unit 12 and performs a risk assessment. The provision unit provides the HIA protocol generated by the control unit 46A of the smart glasses 214 to the user. The custom protocol unit generates a protocol tailored to each user with the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, evaluation unit, provision unit, and custom protocol unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects video data using the camera 42 and sensors of the headset terminal 314 and adjusts the collection timing with the control unit 46A. The analysis unit analyzes the video data in real time with the specific processing unit 290 of the data processing unit 12 and detects head impacts. The generation unit evaluates the risk of concussion with the specific processing unit 290 of the data processing unit 12 and automatically generates an HIA protocol. The evaluation unit analyzes the individual's historical data with the specific processing unit 290 of the data processing unit 12 and performs a risk assessment. The provision unit provides the HIA protocol generated by the control unit 46A of the headset terminal 314 to the user. The custom protocol unit generates a protocol tailored to each user with the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, evaluation unit, provision unit, and custom protocol unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects video data using the camera 42 and sensors of the robot 414 and adjusts the collection timing with the control unit 46A. The analysis unit analyzes the video data in real time with the specific processing unit 290 of the data processing unit 12 and detects head impacts. The generation unit evaluates the risk of concussion with the specific processing unit 290 of the data processing unit 12 and automatically generates an HIA protocol. The evaluation unit analyzes the individual's historical data with the specific processing unit 290 of the data processing unit 12 and performs a risk assessment. The provision unit provides the HIA protocol generated by the control unit 46A of the robot 414 to the user. The custom protocol unit generates a protocol tailored to each user with the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0179] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) The collection unit collects video data, The video data collected by the aforementioned collection unit is analyzed in real time by an analysis unit that detects head impacts, The system includes a generation unit that evaluates the risk of concussion based on the head impact detected by the analysis unit and automatically generates a head injury assessment protocol. A system characterized by the following features. (Note 2) It includes an evaluation unit that collects individual historical data and performs individual risk assessments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes a provisioning unit that provides the HIA protocol generated by the generation unit to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a custom protocol section that provides protocols tailored to each user based on the risks assessed by the evaluation section. The system described in Appendix 2, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past collected data and select the most efficient collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting video data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and determines the priority of video data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting video data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting video data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the video data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the video data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the video data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the video data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the representation of the protocol generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, adjust the level of detail in the protocol based on the degree of concussion risk. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, different protocol generation algorithms are applied depending on the concussion category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the length of the protocol generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the protocol prioritization is determined based on the timing of the concussion. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, adjust the protocol order based on the relevance of concussion. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, We estimate user sentiment and adjust the risk assessment method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation process, the evaluation algorithm is optimized by referring to past risk assessment data. The system described in Appendix 2, characterized by the features described herein. (Note 25) The evaluation unit, During evaluation, improve the accuracy of risk assessment based on the user's health history. The system described in Appendix 2, characterized by the features described herein. (Note 26) The evaluation unit, The system estimates user sentiment and prioritizes risk assessments based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The evaluation unit, During the evaluation, the risk assessment will take into account the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The evaluation unit, During the evaluation process, we analyze users' social media activity to improve the accuracy of risk assessments. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the protocol is delivered based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past protocol history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the protocol's content will be customized based on the user's current health status. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order in which the protocols are delivered based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the service, the protocol's content is customized based on an analysis of the user's social media activity. The system described in Appendix 3, characterized by the features described herein. (Note 35) The custom protocol section is, It estimates the user's emotions and adjusts how custom protocols are generated based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The custom protocol section is, When generating a custom protocol, the system references the user's past health history to generate the optimal protocol. The system described in Appendix 4, characterized by the features described herein. (Note 37) The custom protocol section is, When generating a custom protocol, customize the protocol content based on the user's current life circumstances. The system described in Appendix 4, characterized by the features described herein. (Note 38) The custom protocol section is, It estimates the user's emotions and prioritizes custom protocols based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The custom protocol section is, When generating a custom protocol, the system considers the user's geographical location to generate the optimal protocol. The system described in Appendix 4, characterized by the features described herein. (Note 40) The custom protocol section is, When generating a custom protocol, analyze the user's social media activity to customize the protocol's content. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects video data, The video data collected by the aforementioned collection unit is analyzed in real time by an analysis unit that detects head impacts, The system includes a generation unit that evaluates the risk of concussion based on the head impact detected by the analysis unit and automatically generates a head injury assessment protocol. A system characterized by the following features.

2. It includes an evaluation unit that collects individual historical data and performs individual risk assessments. The system according to feature 1.

3. The system includes a providing unit that provides the HIA protocol generated by the generation unit to the user. The system according to feature 1.

4. The system includes a custom protocol unit that provides a protocol tailored to each user based on the risks assessed by the aforementioned evaluation unit. The system according to feature 2.

5. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video data collection based on the estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze past collected data and select the most efficient collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting video data, filtering is performed based on the user's current activity status and environment. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and determines the priority of video data to collect based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting video data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When collecting video data, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

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