System

Abstract

An object of the system according to the embodiment is to grasp movement of a line of sight or consciousness of an athlete or an instructor in detail and to compare the movement with that of the athlete or the instructor.SOLUTION: A system includes a line-of-sight recording unit, an analysis unit, an acquisition unit, and a comparison unit. The line-of-sight recording unit records the movement of the line of sight. The analysis unit analyzes the data recorded by the line-of-sight recording unit. The acquisition unit acquires biological information data of a user. The comparison unit compares the analysis result obtained by the analysis unit with the biological information data of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to grasp the gaze and attentional movements of athletes and instructors in detail and compare them with one's own.

[0005] The system according to the embodiment aims to grasp the gaze and attentional movements of athletes and instructors in detail and compare them with the athletes themselves. [Means for solving the problem]

[0006] The system according to the embodiment includes a gaze recording unit, an analysis unit, an acquisition unit, and a comparison unit. The gaze recording unit records gaze movements. The analysis unit analyzes the data recorded by the gaze recording unit. The acquisition unit acquires biometric information data of a user. The comparison unit compares the analysis result obtained by the analysis unit with the biometric information data of the user. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the gaze and attentional movements of athletes and instructors in detail and compare them with the athletes themselves. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention uses visual data storage glasses to analyze data on top athletes and instructors' gaze directions, focus points, and routines, allowing users to compare their own performance with their own. The AI ​​system allows athletes and instructors to wear the visual data storage glasses while training or competing, recording their gaze movements, gaze points, and movement routines in real time. The recorded data is then analyzed by AI to identify the athletes' gaze movements, gaze points, and movement routines. The analysis results are then compared with user data to identify areas where the user differs. For example, the AI ​​system records the gaze movements of a basketball player when taking a shot or the gaze points of a golf instructor when making a swing. The AI ​​system then analyzes the gaze movements, gaze points, and movement routines obtained from the visual data storage glasses to identify the athletes' and instructors' gaze directions and focus points. For example, it identifies the area where a basketball player focuses when taking a shot or the area where a golf instructor focuses when making a swing. Next, the AI ​​system compares the user's data with that of top athletes and instructors to identify differences. For example, if a user focuses on different points when shooting a basketball, the differences are clearly shown. This allows the AI ​​system to identify specific areas for improvement in the user's training and competition. For example, by improving the points a basketball player focuses on when shooting, the success rate of the shot can be improved. Similarly, by improving the points a golf instructor focuses on when swinging, the accuracy of the swing can be improved.

[0029] The AI ​​system according to the embodiment includes a gaze recording unit, an analysis unit, an acquisition unit, and a comparison unit. The gaze recording unit records gaze movements using visual data storage glasses worn by athletes and instructors. The gaze recording unit includes, for example, a sensor that records gaze movements. The gaze recording unit can record gaze movements with high accuracy using, for example, an infrared sensor. The gaze recording unit also includes a camera that identifies a gaze point. For example, the gaze recording unit can accurately identify the gaze point using a high-resolution camera. The analysis unit analyzes the data recorded by the gaze recording unit. The analysis unit includes, for example, an algorithm that analyzes gaze movements, gaze points, and movement routines. The analysis unit can analyze gaze movements, gaze points, and movement routines using, for example, a machine learning algorithm. The acquisition unit acquires biometric data of a user. For example, the user wears the visual data storage glasses and performs training, and acquires the data. The acquisition unit can acquire biometric data such as the user's heart rate and electrodermal activity. The comparison unit compares the analysis results obtained by the analysis unit with the user's biometric information data. For example, the comparison unit compares the analysis results with the user's data to clarify where the differences lie. For example, the comparison unit can compare the analysis results with the user's data using statistical methods. As a result, the AI ​​system according to the embodiment can record and analyze eye movements, gaze points, and movement routines, and compare them with the user's data to specifically identify areas for improvement in the user's training or competition.

[0030] The gaze recording unit includes a sensor that records gaze movements. The gaze recording unit can use an infrared sensor or a camera sensor to record gaze movements. For example, the gaze recording unit uses an infrared sensor to record gaze movements with high accuracy. The infrared sensor can accurately measure the speed and direction of gaze movement. The gaze recording unit can also record gaze movements using a camera sensor. The camera sensor is used to identify the gaze point. For example, the gaze recording unit uses a high-resolution camera to record gaze movements in detail. This allows for accurate recording of gaze movements. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input data obtained from the sensor that records gaze movements into AI and have the AI ​​analyze the gaze movements.

[0031] The gaze recording unit includes a camera that identifies the gaze point. The gaze recording unit can use a high-resolution camera to identify the gaze point. For example, the gaze recording unit uses a camera to accurately identify the gaze point. The camera records gaze movements in real time and provides data for identifying the gaze point. The gaze recording unit can also record gaze movements in detail by adjusting the resolution and frame rate of the camera. For example, the gaze recording unit uses a high-resolution camera to record gaze movements in detail and identify the gaze point. This allows the gaze point to be accurately identified. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input gaze data acquired by the camera into AI and have the AI ​​identify the gaze point.

[0032] The analysis unit includes an algorithm for analyzing gaze movements, gaze points, and action routines. The analysis unit can use machine learning algorithms or statistical methods to analyze gaze movements, gaze points, and action routines. For example, the analysis unit analyzes gaze movements, gaze points, and action routines using a machine learning algorithm. The machine learning algorithm learns large amounts of data and can analyze gaze movements, gaze points, and action routines with high accuracy. The analysis unit can also analyze gaze movements, gaze points, and action routines using statistical methods. For example, the analysis unit statistically analyzes changes in gaze movement speed and direction and gaze points. This allows for accurate analysis of gaze movements, gaze points, and action routines. Some or all of the above-described processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input data obtained from the gaze recording unit into AI and have the AI ​​analyze gaze movements, gaze points, and action routines.

[0033] The acquisition unit acquires data when a user wears the visual data storage glasses and performs training. The acquisition unit acquires the data when the user wears the visual data storage glasses and performs training. For example, the acquisition unit records the user's gaze movements, gaze points, and behavior routines using the visual data storage glasses. The visual data storage glasses have performance features such as a data recording method and battery life. For example, the acquisition unit records the user's gaze movements in real time using the visual data storage glasses. The acquisition unit can also acquire biometric data of the user. For example, the acquisition unit acquires biometric data such as the user's heart rate and electrodermal activity. This allows the user's training data to be accurately acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit may input data obtained from the visual data storage glasses into AI and have the AI ​​analyze the data.

[0034] The comparison unit compares the analysis results with the user's data to clarify specific differences. The comparison unit compares the analysis results with the user's data to clarify where the differences exist. The comparison unit compares the analysis results with the user's data using, for example, statistical methods or correlation analysis. For example, the comparison unit statistically analyzes the numerical differences between the analysis results and the user's data. The comparison unit can also clarify pattern differences between the analysis results and the user's data through correlation analysis. This makes it possible to specifically identify areas for improvement by comparing the analysis results with the user's data. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit may input the analysis results and the user's data into AI and have the AI ​​perform a difference analysis.

[0035] The gaze recording unit selects the optimal recording method by referring to the user's past gaze data when recording the gaze. The gaze recording unit selects the optimal recording method by referring to the user's past gaze data when recording the gaze. The gaze recording unit determines the focus of gaze recording, for example, based on points the user has previously gazed at. The gaze recording unit can, for example, identify important gaze points during specific actions from the user's past gaze data and adjust the recording method. The gaze recording unit can also analyze the user's past gaze data and select the optimal recording method to improve the accuracy of the gaze recording. In this way, the accuracy of the gaze recording is improved by referring to the past gaze data. Some or all of the above-mentioned processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input the user's past gaze data into AI and have the AI ​​select the optimal recording method.

[0036] The gaze recording unit filters gaze data based on the user's current activity during gaze recording. The gaze recording unit filters gaze data based on the user's current activity during gaze recording. For example, when the user is playing sports, the gaze recording unit records gazes by focusing on important moments of the movement. For example, when the user is training, the gaze recording unit can filter the gaze record based on specific training movements. The gaze recording unit can also pause gaze recording when the user is taking a break and restart recording when the user resumes their activity. This allows important data to be preferentially recorded by filtering the gaze record based on the current activity. Some or all of the above-described processing in the gaze recording unit may be performed using AI or without AI. For example, the gaze recording unit can input the user's current activity into AI and have the AI ​​perform the filtering of the gaze record.

[0037] The gaze recording unit selects the optimal recording means depending on the user's input method when recording gaze. The gaze recording unit selects the optimal recording means depending on the user's input method when recording gaze. For example, if the user is using voice input, the gaze recording unit starts or stops gaze recording based on a voice command. For example, if the user is using text input, the gaze recording unit can adjust gaze recording based on a specific keyword. Furthermore, if the user is using gesture input, the gaze recording unit can also adjust the timing of gaze recording depending on the gesture. This allows for more appropriate data to be collected by selecting the gaze recording means depending on the user's input method. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit may input the user's input method into AI and have the AI ​​select the optimal recording means.

[0038] The gaze recording unit, when recording gaze, prioritizes recording highly relevant gaze data taking into account the user's geographical location information. The gaze recording unit, when recording gaze, prioritizes recording highly relevant gaze data taking into account the user's geographical location information. For example, when the user is in a specific sports facility, the gaze recording unit prioritizes recording gaze data within the facility. For example, when the user is in a specific training area, the gaze recording unit can prioritize recording gaze data within that area. Furthermore, when the user is in a specific stadium, the gaze recording unit can also prioritize recording gaze data within the stadium. In this way, by taking the geographical location information into account, highly relevant gaze data can be prioritized and recorded. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit may input the user's geographical location information into AI and have the AI ​​determine the priority of the gaze data.

[0039] The gaze recording unit analyzes the user's social media activities and records related gaze data during gaze recording. The gaze recording unit analyzes the user's social media activities and records related gaze data during gaze recording. The gaze recording unit records gaze data based on, for example, training videos shared by the user on social media. The gaze recording unit can analyze, for example, the content of the user's social media posts and record related gaze data. The gaze recording unit can also record related gaze data with reference to the activities of the user's friends on social media. In this way, related gaze data can be recorded by analyzing social media activities. Some or all of the above-mentioned processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input the user's social media activities into AI and cause the AI ​​to record gaze data.

[0040] The gaze recording unit customizes the recording method by reflecting the user's past feedback when recording gaze data. The gaze recording unit customizes the recording method by reflecting the user's past feedback when recording gaze data. The gaze recording unit adjusts the frequency of gaze recording, for example, based on feedback provided by the user in the past. The gaze recording unit can, for example, determine the importance of specific gaze data from the user's past feedback and customize the recording method. The gaze recording unit can also analyze the user's past feedback and select an optimal recording method to improve the accuracy of gaze recording. This improves the accuracy of gaze recording by reflecting past feedback. Some or all of the above-mentioned processing in the gaze recording unit may be performed using AI or without AI. For example, the gaze recording unit can input the user's past feedback into AI and have the AI ​​customize the recording method.

[0041] The analysis unit adjusts the level of detail of the analysis based on the importance of the gaze data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the gaze data during analysis. For example, the analysis unit performs a detailed analysis on important gaze data. For example, the analysis unit can perform a simplified analysis on less important gaze data. The analysis unit can also determine the priority of the analysis according to the importance of the gaze data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input the importance of the gaze data to AI and have the AI ​​adjust the level of detail of the analysis.

[0042] The analysis unit applies different analysis algorithms depending on the category of gaze data during analysis. The analysis unit applies different analysis algorithms depending on the category of gaze data during analysis. The analysis unit applies different analysis algorithms depending on, for example, the type of sport. The analysis unit can apply different analysis algorithms depending on, for example, the type of training. The analysis unit can also apply different analysis algorithms depending on the type of competition. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the category of gaze data into AI and cause the AI ​​to apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can, for example, determine the importance of specific gaze data from the user's past analysis results and improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and select an optimal method for improving the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0044] During analysis, the analysis unit determines the priority of analysis based on the time when the gaze data was acquired. During analysis, the analysis unit determines the priority of analysis based on the time when the gaze data was acquired. For example, the analysis unit prioritizes the most recent gaze data in analysis. For example, the analysis unit can prioritize the most recent data while referring to past gaze data. The analysis unit can also adjust the priority of analysis depending on the time when the gaze data was acquired. This enables efficient analysis by determining the priority of analysis based on the time when the gaze data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the time when the gaze data was acquired into AI and have the AI ​​determine the priority of analysis.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the gaze data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the gaze data during analysis. For example, the analysis unit prioritizes analysis of highly relevant gaze data. For example, the analysis unit can postpone analysis of less relevant gaze data. The analysis unit can also adjust the order of analysis according to the relevance of the gaze data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the gaze data to AI and have the AI ​​adjust the order of analysis.

[0046] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. For example, if the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terminology. Furthermore, if the user is an advanced user, the analysis unit can provide analysis results using detailed technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terminology.

[0047] When acquiring data, the acquisition unit selects the optimal acquisition method by referring to the user's past data acquisition history. When acquiring data, the acquisition unit selects the optimal acquisition method by referring to the user's past data acquisition history. The acquisition unit selects the optimal data acquisition method, for example, based on data previously acquired by the user. The acquisition unit can, for example, preferentially select a specific data acquisition method from the user's past data acquisition history. The acquisition unit can also analyze the user's past data acquisition history and select the optimal data acquisition method. This improves the accuracy of data acquisition by referring to the past data acquisition history. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past data acquisition history into AI and have the AI ​​select the optimal acquisition method.

[0048] The acquisition unit filters data based on the user's current training content when acquiring data. The acquisition unit filters data based on the user's current training content when acquiring data. For example, if the user is performing a specific training, the acquisition unit prioritizes acquiring data related to that training. For example, if the user is performing multiple trainings, the acquisition unit can filter and acquire data related to each training. The acquisition unit can also determine the priority of data acquisition based on the user's training content. This allows important data to be acquired preferentially by filtering data acquisition based on the current training content. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI or without AI. For example, the acquisition unit can input the user's current training content into AI and have the AI ​​perform data acquisition filtering.

[0049] The acquisition unit selects the optimal acquisition means according to the user's input method when acquiring data. The acquisition unit selects the optimal acquisition means according to the user's input method when acquiring data. For example, if the user is using voice input, the acquisition unit starts or stops data acquisition based on a voice command. For example, if the user is using text input, the acquisition unit can adjust data acquisition based on a specific keyword. Furthermore, if the user is using gesture input, the acquisition unit can also adjust the timing of data acquisition according to the gesture. This allows more appropriate data to be collected by selecting a data acquisition means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit may input the user's input method into AI and have the AI ​​select the optimal acquisition means.

[0050] When acquiring data, the acquisition unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. When acquiring data, the acquisition unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific sports facility, the acquisition unit prioritizes acquiring data within the facility. For example, when the user is in a specific training area, the acquisition unit can prioritize acquiring data within that area. Furthermore, when the user is in a specific stadium, the acquisition unit can also prioritize acquiring data within the stadium. In this way, by taking the geographical location information into account, highly relevant data can be prioritized. Some or all of the above-described processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information into AI and have the AI ​​determine the priority of the data.

[0051] The acquisition unit analyzes the user's social media activity and acquires related data when acquiring data. The acquisition unit analyzes the user's social media activity and acquires related data when acquiring data. The acquisition unit acquires data based on, for example, training videos shared by the user on social media. The acquisition unit can analyze, for example, the content of the user's social media posts and acquire related data. The acquisition unit can also acquire related data by referring to the activity of the user's friends on social media. In this way, related data can be acquired by analyzing social media activity. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity into AI and cause the AI ​​to acquire data.

[0052] The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit adjusts the frequency of data acquisition, for example, based on feedback provided by the user in the past. The acquisition unit can determine the importance of specific data from the user's past feedback and customize the acquisition method, for example. The acquisition unit can also analyze the user's past feedback and select an optimal method for improving the accuracy of data acquisition. In this way, the accuracy of data acquisition is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI or without AI. For example, the acquisition unit can input the user's past feedback into AI and have the AI ​​customize the acquisition method.

[0053] The comparison unit adjusts the level of detail of the comparison based on the importance of the analysis results during comparison. The comparison unit adjusts the level of detail of the comparison based on the importance of the analysis results during comparison. For example, the comparison unit performs a detailed comparison for important analysis results. For example, the comparison unit can perform a simplified comparison for less important analysis results. The comparison unit can also determine the priority of the comparison according to the importance of the analysis results. This enables efficient comparison by adjusting the level of detail of the comparison based on the importance of the analysis results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the importance of the analysis results to AI and have the AI ​​adjust the level of detail of the comparison.

[0054] The comparison unit applies different comparison algorithms depending on the category of the analysis results during comparison. The comparison unit applies different comparison algorithms depending on the category of the analysis results during comparison. The comparison unit applies different comparison algorithms depending on, for example, the type of sport. The comparison unit can apply different comparison algorithms depending on, for example, the type of training. The comparison unit can also apply different comparison algorithms depending on the type of competition. This improves the accuracy of the comparison by applying an appropriate comparison algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the category of the analysis results into AI and cause the AI ​​to apply an appropriate comparison algorithm.

[0055] The comparison unit improves the accuracy of the comparison by referring to the user's past comparison results during comparison. The comparison unit improves the accuracy of the comparison by referring to the user's past comparison results during comparison. For example, the comparison unit adjusts the comparison algorithm based on the user's past comparison results. For example, the comparison unit can determine the importance of specific analysis results from the user's past comparison results and improve the accuracy of the comparison. The comparison unit can also analyze the user's past comparison results and select an optimal method for improving the accuracy of the comparison. In this way, the accuracy of the comparison is improved by referring to the past comparison results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the user's past comparison results into AI and have the AI ​​improve the accuracy of the comparison.

[0056] The comparison unit determines the comparison priority based on the time when the analysis results were acquired during comparison. The comparison unit determines the comparison priority based on the time when the analysis results were acquired during comparison. For example, the comparison unit prioritizes the most recent analysis results. For example, the comparison unit can prioritize the most recent data while referring to past analysis results. The comparison unit can also adjust the comparison priority according to the time when the analysis results were acquired. This enables efficient comparison by determining the comparison priority based on the time when the analysis results were acquired. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the time when the analysis results were acquired into AI and have the AI ​​determine the comparison priority.

[0057] The comparison unit adjusts the order of comparison based on the relevance of the analysis results during comparison. The comparison unit adjusts the order of comparison based on the relevance of the analysis results during comparison. For example, the comparison unit prioritizes comparison of highly relevant analysis results. For example, the comparison unit can postpone analysis results with low relevance. The comparison unit can also adjust the order of comparison according to the relevance of the analysis results. This enables efficient comparison by adjusting the order of comparison based on the relevance of the analysis results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the relevance of the analysis results into AI and have the AI ​​adjust the order of comparison.

[0058] The comparison unit adjusts the use of technical terms during comparison according to the user's level of expertise. The comparison unit adjusts the use of technical terms during comparison according to the user's level of expertise. For example, if the user is a beginner, the comparison unit can provide comparison results while avoiding technical terms. For example, if the user is an intermediate user, the comparison unit can provide comparison results using appropriate technical terms. Furthermore, if the user is an advanced user, the comparison unit can provide comparison results using detailed technical terms. In this way, by adjusting the use of technical terms during comparison according to the user's level of expertise, it is possible to provide comparison results that are easier to understand. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When recording a user's gaze data, the gaze recording unit can compare it with the user's past gaze data in real time to detect abnormal gaze patterns. For example, if a user exhibits a gaze pattern that deviates from their normal training routine, the gaze recording unit can detect the abnormality and provide feedback to the user. Furthermore, if an abnormal gaze pattern is detected, the gaze recording unit can record the data in detail and save it as data for later analysis. This allows abnormalities in the user's gaze pattern to be detected in real time and appropriate feedback to be provided.

[0061] When recording the user's gaze data, the gaze recording unit can simultaneously record the user's gaze movements and audio data. For example, it can record the voice and instructions the user gives during training and synchronize the audio data with the gaze data. The gaze recording unit can also analyze the audio data and identify the correlation between the user's gaze movements and audio. This allows the detailed status of the user's training to be grasped by combining and analyzing the gaze data and audio data.

[0062] When recording the user's gaze data, the gaze recording unit can simultaneously record environmental data along with the user's gaze movements. For example, it can record environmental data such as the temperature, humidity, and lighting brightness of the location where the user is training, and synchronize that data with the gaze data. The gaze recording unit can also analyze the environmental data and identify the relationship between the user's gaze movements and the environment. This allows the combined analysis of the gaze data and the environmental data to understand the influence of the user's training environment.

[0063] When analyzing the user's gaze data, the analysis unit can compare it with the user's past training data to evaluate progress. For example, the analysis unit can compare the gaze data from the user's past training with the current gaze data to identify changes in gaze movements and points of gaze. The analysis unit can also set indicators for evaluating progress and quantitatively evaluate the effectiveness of the user's training. This allows the user's training progress to be understood and an effective training plan to be created.

[0064] When acquiring the user's gaze data, the acquisition unit can synchronize it with the user's biometric data. For example, it can acquire biometric data such as the user's heart rate and electrodermal activity and synchronize that data with the gaze data. The acquisition unit can also analyze the biometric data and identify the correlation between the user's gaze movements and the biometric information. This makes it possible to understand the user's training status in detail by combining and analyzing the gaze data and the biometric data.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The gaze recording unit records gaze movements using visual data storage glasses worn by athletes and instructors. The gaze recording unit is equipped with sensors that record gaze movements, infrared sensors, and cameras that identify the gaze point, and records gaze movements with high accuracy. Step 2: The analysis unit analyzes the data recorded by the gaze recording unit. The analysis unit is equipped with algorithms for analyzing gaze movements, gaze points, and behavioral routines, and can analyze these using machine learning algorithms. Step 3: The acquisition unit acquires the user's biometric data. The acquisition unit acquires the data while the user wears the visual data storage glasses and performs training. The acquisition unit can acquire biometric data such as the user's heart rate and electrodermal activity. Step 4: The comparison unit compares the analysis results obtained by the analysis unit with the user's biometric information data. The comparison unit compares the analysis results with the user's data and clarifies where the differences exist. The comparison unit can use statistical methods to compare the analysis results with the user's data.

[0067] (Example 2) An AI system according to an embodiment of the present invention uses visual data storage glasses to analyze data on top athletes and instructors' gaze directions, focus points, and routines, allowing users to compare their own performance with their own. The AI ​​system allows athletes and instructors to wear the visual data storage glasses while training or competing, recording their gaze movements, gaze points, and movement routines in real time. The recorded data is then analyzed by AI to identify the athletes' gaze movements, gaze points, and movement routines. The analysis results are then compared with user data to identify areas where the user differs. For example, the AI ​​system records the gaze movements of a basketball player when taking a shot or the gaze points of a golf instructor when making a swing. The AI ​​system then analyzes the gaze movements, gaze points, and movement routines obtained from the visual data storage glasses to identify the athletes' and instructors' gaze directions and focus points. For example, it identifies the area where a basketball player focuses when taking a shot or the area where a golf instructor focuses when making a swing. Next, the AI ​​system compares the user's data with that of top athletes and instructors to identify differences. For example, if a user focuses on different points when shooting a basketball, the differences are clearly shown. This allows the AI ​​system to identify specific areas for improvement in the user's training and competition. For example, by improving the points a basketball player focuses on when shooting, the success rate of the shot can be improved. Similarly, by improving the points a golf instructor focuses on when swinging, the accuracy of the swing can be improved.

[0068] The AI ​​system according to the embodiment includes a gaze recording unit, an analysis unit, an acquisition unit, and a comparison unit. The gaze recording unit records gaze movements using visual data storage glasses worn by athletes and instructors. The gaze recording unit includes, for example, a sensor that records gaze movements. The gaze recording unit can record gaze movements with high accuracy using, for example, an infrared sensor. The gaze recording unit also includes a camera that identifies a gaze point. For example, the gaze recording unit can accurately identify the gaze point using a high-resolution camera. The analysis unit analyzes the data recorded by the gaze recording unit. The analysis unit includes, for example, an algorithm that analyzes gaze movements, gaze points, and movement routines. The analysis unit can analyze gaze movements, gaze points, and movement routines using, for example, a machine learning algorithm. The acquisition unit acquires biometric data of a user. For example, the user wears the visual data storage glasses and performs training, and acquires the data. The acquisition unit can acquire biometric data such as the user's heart rate and electrodermal activity. The comparison unit compares the analysis results obtained by the analysis unit with the user's biometric information data. For example, the comparison unit compares the analysis results with the user's data to clarify where the differences lie. For example, the comparison unit can compare the analysis results with the user's data using statistical methods. As a result, the AI ​​system according to the embodiment can record and analyze eye movements, gaze points, and movement routines, and compare them with the user's data to specifically identify areas for improvement in the user's training or competition.

[0069] The gaze recording unit includes a sensor that records gaze movements. The gaze recording unit can use an infrared sensor or a camera sensor to record gaze movements. For example, the gaze recording unit uses an infrared sensor to record gaze movements with high accuracy. The infrared sensor can accurately measure the speed and direction of gaze movement. The gaze recording unit can also record gaze movements using a camera sensor. The camera sensor is used to identify the gaze point. For example, the gaze recording unit uses a high-resolution camera to record gaze movements in detail. This allows for accurate recording of gaze movements. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input data obtained from the sensor that records gaze movements into AI and have the AI ​​analyze the gaze movements.

[0070] The gaze recording unit includes a camera that identifies the gaze point. The gaze recording unit can use a high-resolution camera to identify the gaze point. For example, the gaze recording unit uses a camera to accurately identify the gaze point. The camera records gaze movements in real time and provides data for identifying the gaze point. The gaze recording unit can also record gaze movements in detail by adjusting the resolution and frame rate of the camera. For example, the gaze recording unit uses a high-resolution camera to record gaze movements in detail and identify the gaze point. This allows the gaze point to be accurately identified. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input gaze data acquired by the camera into AI and have the AI ​​identify the gaze point.

[0071] The analysis unit includes an algorithm for analyzing gaze movements, gaze points, and action routines. The analysis unit can use machine learning algorithms or statistical methods to analyze gaze movements, gaze points, and action routines. For example, the analysis unit analyzes gaze movements, gaze points, and action routines using a machine learning algorithm. The machine learning algorithm learns large amounts of data and can analyze gaze movements, gaze points, and action routines with high accuracy. The analysis unit can also analyze gaze movements, gaze points, and action routines using statistical methods. For example, the analysis unit statistically analyzes changes in gaze movement speed and direction and gaze points. This allows for accurate analysis of gaze movements, gaze points, and action routines. Some or all of the above-described processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input data obtained from the gaze recording unit into AI and have the AI ​​analyze gaze movements, gaze points, and action routines.

[0072] The acquisition unit acquires data when a user wears the visual data storage glasses and performs training. The acquisition unit acquires the data when the user wears the visual data storage glasses and performs training. For example, the acquisition unit records the user's gaze movements, gaze points, and behavior routines using the visual data storage glasses. The visual data storage glasses have performance features such as a data recording method and battery life. For example, the acquisition unit records the user's gaze movements in real time using the visual data storage glasses. The acquisition unit can also acquire biometric data of the user. For example, the acquisition unit acquires biometric data such as the user's heart rate and electrodermal activity. This allows the user's training data to be accurately acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit may input data obtained from the visual data storage glasses into AI and have the AI ​​analyze the data.

[0073] The comparison unit compares the analysis results with the user's data to clarify specific differences. The comparison unit compares the analysis results with the user's data to clarify where the differences exist. The comparison unit compares the analysis results with the user's data using, for example, statistical methods or correlation analysis. For example, the comparison unit statistically analyzes the numerical differences between the analysis results and the user's data. The comparison unit can also clarify pattern differences between the analysis results and the user's data through correlation analysis. This makes it possible to specifically identify areas for improvement by comparing the analysis results with the user's data. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit may input the analysis results and the user's data into AI and have the AI ​​perform a difference analysis.

[0074] The gaze recording unit estimates the user's emotion and adjusts the timing of gaze recording based on the estimated user emotion. The gaze recording unit estimates the user's emotion and adjusts the timing of gaze recording based on the estimated user emotion. For example, when the user is concentrating, the gaze recording unit increases the frequency of gaze recording and collects detailed data. For example, when the user is relaxed, the gaze recording unit can reduce the frequency of gaze recording and collect the minimum amount of data necessary. Furthermore, when the user is tired, the gaze recording unit can adjust the timing of gaze recording to reduce the user's burden. This allows for more appropriate data to be collected by adjusting the timing of gaze recording according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the gaze recording unit may be performed using AI, or may be performed without AI. For example, the gaze recording unit can input the user's emotion data into AI and have the AI ​​adjust the timing of gaze recording.

[0075] The gaze recording unit selects the optimal recording method by referring to the user's past gaze data when recording the gaze. The gaze recording unit selects the optimal recording method by referring to the user's past gaze data when recording the gaze. The gaze recording unit determines the focus of gaze recording, for example, based on points the user has previously gazed at. The gaze recording unit can, for example, identify important gaze points during specific actions from the user's past gaze data and adjust the recording method. The gaze recording unit can also analyze the user's past gaze data and select the optimal recording method to improve the accuracy of the gaze recording. In this way, the accuracy of the gaze recording is improved by referring to the past gaze data. Some or all of the above-mentioned processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input the user's past gaze data into AI and have the AI ​​select the optimal recording method.

[0076] The gaze recording unit filters gaze data based on the user's current activity during gaze recording. The gaze recording unit filters gaze data based on the user's current activity during gaze recording. For example, when the user is playing sports, the gaze recording unit records gazes by focusing on important moments of the movement. For example, when the user is training, the gaze recording unit can filter the gaze record based on specific training movements. The gaze recording unit can also pause gaze recording when the user is taking a break and restart recording when the user resumes their activity. This allows important data to be preferentially recorded by filtering the gaze record based on the current activity. Some or all of the above-described processing in the gaze recording unit may be performed using AI or without AI. For example, the gaze recording unit can input the user's current activity into AI and have the AI ​​perform the filtering of the gaze record.

[0077] The gaze recording unit selects the optimal recording means depending on the user's input method when recording gaze. The gaze recording unit selects the optimal recording means depending on the user's input method when recording gaze. For example, if the user is using voice input, the gaze recording unit starts or stops gaze recording based on a voice command. For example, if the user is using text input, the gaze recording unit can adjust gaze recording based on a specific keyword. Furthermore, if the user is using gesture input, the gaze recording unit can also adjust the timing of gaze recording depending on the gesture. This allows for more appropriate data to be collected by selecting the gaze recording means depending on the user's input method. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit may input the user's input method into AI and have the AI ​​select the optimal recording means.

[0078] The gaze recording unit estimates the user's emotion and determines the priority of the gaze data to be recorded based on the estimated user's emotion. The gaze recording unit estimates the user's emotion and determines the priority of the gaze data to be recorded based on the estimated user's emotion. For example, when the user is excited, the gaze recording unit prioritizes recording particularly important parts of the gaze data. For example, when the user is relaxed, the gaze recording unit can prioritize recording detailed parts of the gaze data. Furthermore, when the user is concentrating, the gaze recording unit can prioritize recording important moments of movement among the gaze data. This allows important data to be prioritized by determining the priority of the gaze data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the gaze recording unit may be performed using AI or without AI. For example, the gaze recording unit can input the user's emotion data into AI and have the AI ​​determine the priority of the gaze data.

[0079] The gaze recording unit, when recording gaze, prioritizes recording highly relevant gaze data taking into account the user's geographical location information. The gaze recording unit, when recording gaze, prioritizes recording highly relevant gaze data taking into account the user's geographical location information. For example, when the user is in a specific sports facility, the gaze recording unit prioritizes recording gaze data within the facility. For example, when the user is in a specific training area, the gaze recording unit can prioritize recording gaze data within that area. Furthermore, when the user is in a specific stadium, the gaze recording unit can also prioritize recording gaze data within the stadium. In this way, by taking the geographical location information into account, highly relevant gaze data can be prioritized and recorded. Some or all of the above-described processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit may input the user's geographical location information into AI and have the AI ​​determine the priority of the gaze data.

[0080] The gaze recording unit analyzes the user's social media activities and records related gaze data during gaze recording. The gaze recording unit analyzes the user's social media activities and records related gaze data during gaze recording. The gaze recording unit records gaze data based on, for example, training videos shared by the user on social media. The gaze recording unit can analyze, for example, the content of the user's social media posts and record related gaze data. The gaze recording unit can also record related gaze data with reference to the activities of the user's friends on social media. In this way, related gaze data can be recorded by analyzing social media activities. Some or all of the above-mentioned processing in the gaze recording unit may be performed using AI, or may be performed without using AI. For example, the gaze recording unit can input the user's social media activities into AI and cause the AI ​​to record gaze data.

[0081] The gaze recording unit customizes the recording method by reflecting the user's past feedback when recording gaze data. The gaze recording unit customizes the recording method by reflecting the user's past feedback when recording gaze data. The gaze recording unit adjusts the frequency of gaze recording, for example, based on feedback provided by the user in the past. The gaze recording unit can, for example, determine the importance of specific gaze data from the user's past feedback and customize the recording method. The gaze recording unit can also analyze the user's past feedback and select an optimal recording method to improve the accuracy of gaze recording. This improves the accuracy of gaze recording by reflecting past feedback. Some or all of the above-mentioned processing in the gaze recording unit may be performed using AI or without AI. For example, the gaze recording unit can input the user's past feedback into AI and have the AI ​​customize the recording method.

[0082] The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user's emotions. The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the presentation method of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may input the user's emotion data into AI and have the AI ​​adjust the presentation method of the analysis.

[0083] The analysis unit adjusts the level of detail of the analysis based on the importance of the gaze data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the gaze data during analysis. For example, the analysis unit performs a detailed analysis on important gaze data. For example, the analysis unit can perform a simplified analysis on less important gaze data. The analysis unit can also determine the priority of the analysis according to the importance of the gaze data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI or without AI. For example, the analysis unit can input the importance of the gaze data to AI and have the AI ​​adjust the level of detail of the analysis.

[0084] The analysis unit applies different analysis algorithms depending on the category of gaze data during analysis. The analysis unit applies different analysis algorithms depending on the category of gaze data during analysis. The analysis unit applies different analysis algorithms depending on, for example, the type of sport. The analysis unit can apply different analysis algorithms depending on, for example, the type of training. The analysis unit can also apply different analysis algorithms depending on the type of competition. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the category of gaze data into AI and cause the AI ​​to apply an appropriate analysis algorithm.

[0085] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can, for example, determine the importance of specific gaze data from the user's past analysis results and improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and select an optimal method for improving the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0086] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may input the user's emotion data into AI and have the AI ​​adjust the length of the analysis.

[0087] During analysis, the analysis unit determines the priority of analysis based on the time when the gaze data was acquired. During analysis, the analysis unit determines the priority of analysis based on the time when the gaze data was acquired. For example, the analysis unit prioritizes the most recent gaze data in analysis. For example, the analysis unit can prioritize the most recent data while referring to past gaze data. The analysis unit can also adjust the priority of analysis depending on the time when the gaze data was acquired. This enables efficient analysis by determining the priority of analysis based on the time when the gaze data was acquired. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the time when the gaze data was acquired into AI and have the AI ​​determine the priority of analysis.

[0088] The analysis unit adjusts the order of analysis based on the relevance of the gaze data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the gaze data during analysis. For example, the analysis unit prioritizes analysis of highly relevant gaze data. For example, the analysis unit can postpone analysis of less relevant gaze data. The analysis unit can also adjust the order of analysis according to the relevance of the gaze data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the gaze data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the gaze data to AI and have the AI ​​adjust the order of analysis.

[0089] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. For example, if the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terminology. Furthermore, if the user is an advanced user, the analysis unit can provide analysis results using detailed technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terminology.

[0090] The acquisition unit estimates the user's emotion and adjusts the timing of data acquisition based on the estimated user emotion. The acquisition unit estimates the user's emotion and adjusts the timing of data acquisition based on the estimated user emotion. For example, when the user is concentrating, the acquisition unit increases the frequency of data acquisition and collects detailed data. For example, when the user is relaxed, the acquisition unit can reduce the frequency of data acquisition and collect the minimum amount of data necessary. Furthermore, when the user is tired, the acquisition unit can adjust the timing of data acquisition to reduce the user's burden. This allows for more appropriate data to be collected by adjusting the timing of data acquisition according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using AI, or may be performed without AI. For example, the acquisition unit may input the user's emotion data into AI and have the AI ​​adjust the timing of data acquisition.

[0091] When acquiring data, the acquisition unit selects the optimal acquisition method by referring to the user's past data acquisition history. When acquiring data, the acquisition unit selects the optimal acquisition method by referring to the user's past data acquisition history. The acquisition unit selects the optimal data acquisition method, for example, based on data previously acquired by the user. The acquisition unit can, for example, preferentially select a specific data acquisition method from the user's past data acquisition history. The acquisition unit can also analyze the user's past data acquisition history and select the optimal data acquisition method. This improves the accuracy of data acquisition by referring to the past data acquisition history. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past data acquisition history into AI and have the AI ​​select the optimal acquisition method.

[0092] The acquisition unit filters data based on the user's current training content when acquiring data. The acquisition unit filters data based on the user's current training content when acquiring data. For example, if the user is performing a specific training, the acquisition unit prioritizes acquiring data related to that training. For example, if the user is performing multiple trainings, the acquisition unit can filter and acquire data related to each training. The acquisition unit can also determine the priority of data acquisition based on the user's training content. This allows important data to be acquired preferentially by filtering data acquisition based on the current training content. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI or without AI. For example, the acquisition unit can input the user's current training content into AI and have the AI ​​perform data acquisition filtering.

[0093] The acquisition unit selects the optimal acquisition means according to the user's input method when acquiring data. The acquisition unit selects the optimal acquisition means according to the user's input method when acquiring data. For example, if the user is using voice input, the acquisition unit starts or stops data acquisition based on a voice command. For example, if the user is using text input, the acquisition unit can adjust data acquisition based on a specific keyword. Furthermore, if the user is using gesture input, the acquisition unit can also adjust the timing of data acquisition according to the gesture. This allows more appropriate data to be collected by selecting a data acquisition means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit may input the user's input method into AI and have the AI ​​select the optimal acquisition means.

[0094] The acquisition unit estimates the user's emotion and determines the priority of data to be acquired based on the estimated user's emotion. The acquisition unit estimates the user's emotion and determines the priority of data to be acquired based on the estimated user's emotion. For example, when the user is excited, the acquisition unit prioritizes acquiring important data. For example, when the user is relaxed, the acquisition unit can prioritize acquiring detailed data. Furthermore, when the user is concentrating, the acquisition unit can prioritize acquiring important moments of movement. This allows important data to be acquired preferentially by determining the priority of data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without AI. For example, the acquisition unit may input the user's emotion data into AI and have the AI ​​determine the priority of the data.

[0095] When acquiring data, the acquisition unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. When acquiring data, the acquisition unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific sports facility, the acquisition unit prioritizes acquiring data within the facility. For example, when the user is in a specific training area, the acquisition unit can prioritize acquiring data within that area. Furthermore, when the user is in a specific stadium, the acquisition unit can also prioritize acquiring data within the stadium. In this way, by taking the geographical location information into account, highly relevant data can be prioritized. Some or all of the above-described processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information into AI and have the AI ​​determine the priority of the data.

[0096] The acquisition unit analyzes the user's social media activity and acquires related data when acquiring data. The acquisition unit analyzes the user's social media activity and acquires related data when acquiring data. The acquisition unit acquires data based on, for example, training videos shared by the user on social media. The acquisition unit can analyze, for example, the content of the user's social media posts and acquire related data. The acquisition unit can also acquire related data by referring to the activity of the user's friends on social media. In this way, related data can be acquired by analyzing social media activity. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity into AI and cause the AI ​​to acquire data.

[0097] The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit adjusts the frequency of data acquisition, for example, based on feedback provided by the user in the past. The acquisition unit can determine the importance of specific data from the user's past feedback and customize the acquisition method, for example. The acquisition unit can also analyze the user's past feedback and select an optimal method for improving the accuracy of data acquisition. In this way, the accuracy of data acquisition is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI or without AI. For example, the acquisition unit can input the user's past feedback into AI and have the AI ​​customize the acquisition method.

[0098] The comparison unit estimates the user's emotion and adjusts the comparison method based on the estimated user's emotion. The comparison unit estimates the user's emotion and adjusts the comparison method based on the estimated user's emotion. For example, if the user is nervous, the comparison unit provides a simple, highly visible comparison result. For example, if the user is relaxed, the comparison unit can provide a detailed comparison result. Furthermore, if the user is excited, the comparison unit can provide a visually stimulating comparison result. This allows the comparison unit to adjust the comparison method according to the user's emotion and provide a more appropriate comparison result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the comparison unit may be performed using AI or without AI. For example, the comparison unit may input user emotion data into AI and have the AI ​​adjust the comparison method.

[0099] The comparison unit adjusts the level of detail of the comparison based on the importance of the analysis results during comparison. The comparison unit adjusts the level of detail of the comparison based on the importance of the analysis results during comparison. For example, the comparison unit performs a detailed comparison for important analysis results. For example, the comparison unit can perform a simplified comparison for less important analysis results. The comparison unit can also determine the priority of the comparison according to the importance of the analysis results. This enables efficient comparison by adjusting the level of detail of the comparison based on the importance of the analysis results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the importance of the analysis results to AI and have the AI ​​adjust the level of detail of the comparison.

[0100] The comparison unit applies different comparison algorithms depending on the category of the analysis results during comparison. The comparison unit applies different comparison algorithms depending on the category of the analysis results during comparison. The comparison unit applies different comparison algorithms depending on, for example, the type of sport. The comparison unit can apply different comparison algorithms depending on, for example, the type of training. The comparison unit can also apply different comparison algorithms depending on the type of competition. This improves the accuracy of the comparison by applying an appropriate comparison algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the category of the analysis results into AI and cause the AI ​​to apply an appropriate comparison algorithm.

[0101] The comparison unit improves the accuracy of the comparison by referring to the user's past comparison results during comparison. The comparison unit improves the accuracy of the comparison by referring to the user's past comparison results during comparison. For example, the comparison unit adjusts the comparison algorithm based on the user's past comparison results. For example, the comparison unit can determine the importance of specific analysis results from the user's past comparison results and improve the accuracy of the comparison. The comparison unit can also analyze the user's past comparison results and select an optimal method for improving the accuracy of the comparison. In this way, the accuracy of the comparison is improved by referring to the past comparison results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the user's past comparison results into AI and have the AI ​​improve the accuracy of the comparison.

[0102] The comparison unit estimates the user's emotion and adjusts the length of the comparison based on the estimated user's emotion. The comparison unit estimates the user's emotion and adjusts the length of the comparison based on the estimated user's emotion. For example, if the user is in a hurry, the comparison unit can provide a short and concise comparison result. For example, if the user is relaxed, the comparison unit can provide a detailed comparison result. Furthermore, if the user is excited, the comparison unit can provide a visually stimulating comparison result. This allows for adjusting the length of the comparison based on the user's emotion to provide a more appropriate comparison result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit can input user emotion data into AI and have the AI ​​adjust the length of the comparison.

[0103] The comparison unit determines the comparison priority based on the time when the analysis results were acquired during comparison. The comparison unit determines the comparison priority based on the time when the analysis results were acquired during comparison. For example, the comparison unit prioritizes the most recent analysis results. For example, the comparison unit can prioritize the most recent data while referring to past analysis results. The comparison unit can also adjust the comparison priority according to the time when the analysis results were acquired. This enables efficient comparison by determining the comparison priority based on the time when the analysis results were acquired. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the time when the analysis results were acquired into AI and have the AI ​​determine the comparison priority.

[0104] The comparison unit adjusts the order of comparison based on the relevance of the analysis results during comparison. The comparison unit adjusts the order of comparison based on the relevance of the analysis results during comparison. For example, the comparison unit prioritizes comparison of highly relevant analysis results. For example, the comparison unit can postpone analysis results with low relevance. The comparison unit can also adjust the order of comparison according to the relevance of the analysis results. This enables efficient comparison by adjusting the order of comparison based on the relevance of the analysis results. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, or may be performed without using AI. For example, the comparison unit can input the relevance of the analysis results into AI and have the AI ​​adjust the order of comparison.

[0105] The comparison unit adjusts the use of technical terms during comparison according to the user's level of expertise. The comparison unit adjusts the use of technical terms during comparison according to the user's level of expertise. For example, if the user is a beginner, the comparison unit can provide comparison results while avoiding technical terms. For example, if the user is an intermediate user, the comparison unit can provide comparison results using appropriate technical terms. Furthermore, if the user is an advanced user, the comparison unit can provide comparison results using detailed technical terms. In this way, by adjusting the use of technical terms during comparison according to the user's level of expertise, it is possible to provide comparison results that are easier to understand. Some or all of the above-described processing in the comparison unit may be performed using AI, or may be performed without AI. For example, the comparison unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the gaze recording unit, analysis unit, acquisition unit, and comparison unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the gaze recording unit can record gaze movements using the camera 42 or an infrared sensor of the smart device 14. For example, the analysis unit can analyze gaze movements, gaze points, and behavior routines using the specific processing unit 290 of the data processing device 12. For example, the acquisition unit can acquire biometric information data of the user using a sensor of the smart device 14. For example, the comparison unit can compare the analysis results with the user data using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described gaze recording unit, analysis unit, acquisition unit, and comparison unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the gaze recording unit can record gaze movements using the camera 42 or an infrared sensor of the smart glasses 214. For example, the analysis unit can analyze gaze movements, gaze points, and behavior routines using the specific processing unit 290 of the data processing device 12. For example, the acquisition unit can acquire biometric information data of the user using a sensor of the smart glasses 214. For example, the comparison unit can compare the analysis results with the user data using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned gaze recording unit, analysis unit, acquisition unit, and comparison unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the gaze recording unit can record gaze movements using the camera 42 or an infrared sensor of the headset-type terminal 314. For example, the analysis unit can analyze gaze movements, gaze points, and behavior routines using the specific processing unit 290 of the data processing device 12. For example, the acquisition unit can acquire biometric information data of the user using a sensor of the headset-type terminal 314. For example, the comparison unit can compare the analysis results with the user data using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the gaze recording unit, analysis unit, acquisition unit, and comparison unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the gaze recording unit can record gaze movements using the camera 42 or an infrared sensor of the robot 414. For example, the analysis unit can analyze gaze movements, gaze points, and behavior routines using the specific processing unit 290 of the data processing device 12. For example, the acquisition unit can acquire biometric information data of the user using a sensor of the robot 414. For example, the comparison unit can compare the analysis results with the user data using the specific processing unit 290 of the data processing device 12.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When recording a user's gaze data, the gaze recording unit can compare it with the user's past gaze data in real time to detect abnormal gaze patterns. For example, if a user exhibits a gaze pattern that deviates from their normal training routine, the gaze recording unit can detect the abnormality and provide feedback to the user. Furthermore, if an abnormal gaze pattern is detected, the gaze recording unit can record the data in detail and save it as data for later analysis. This allows abnormalities in the user's gaze pattern to be detected in real time and appropriate feedback to be provided.

[0108] When recording the user's gaze data, the gaze recording unit can simultaneously record the user's gaze movements and audio data. For example, it can record the voice and instructions the user gives during training and synchronize the audio data with the gaze data. The gaze recording unit can also analyze the audio data and identify the correlation between the user's gaze movements and audio. This allows the detailed status of the user's training to be grasped by combining and analyzing the gaze data and audio data.

[0109] When recording the user's gaze data, the gaze recording unit can simultaneously record environmental data along with the user's gaze movements. For example, it can record environmental data such as the temperature, humidity, and lighting brightness of the location where the user is training, and synchronize that data with the gaze data. The gaze recording unit can also analyze the environmental data and identify the relationship between the user's gaze movements and the environment. This allows the combined analysis of the gaze data and the environmental data to understand the influence of the user's training environment.

[0110] When analyzing the user's gaze data, the analysis unit can compare it with the user's past training data to evaluate progress. For example, the analysis unit can compare the gaze data from the user's past training with the current gaze data to identify changes in gaze movements and points of gaze. The analysis unit can also set indicators for evaluating progress and quantitatively evaluate the effectiveness of the user's training. This allows the user's training progress to be understood and an effective training plan to be created.

[0111] When acquiring the user's gaze data, the acquisition unit can synchronize it with the user's biometric data. For example, it can acquire biometric data such as the user's heart rate and electrodermal activity and synchronize that data with the gaze data. The acquisition unit can also analyze the biometric data and identify the correlation between the user's gaze movements and the biometric information. This makes it possible to understand the user's training status in detail by combining and analyzing the gaze data and the biometric data.

[0112] The gaze recording unit can estimate the user's emotions and adjust the accuracy of the gaze recording based on the estimated user's emotions. For example, if the user is nervous, the accuracy of the gaze recording can be increased to collect more detailed data. Also, if the user is relaxed, the accuracy of the gaze recording can be adjusted to collect the minimum amount of data necessary. In this way, by adjusting the accuracy of the gaze recording according to the user's emotions, more appropriate data can be collected.

[0113] The analysis unit can estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions. For example, if the user is concentrating, the timing of analysis can be accelerated to provide analysis results in real time. Also, if the user is relaxed, the timing of analysis can be adjusted to analyze the minimum amount of data necessary. In this way, by adjusting the timing of analysis according to the user's emotions, more appropriate analysis results can be provided.

[0114] The acquisition unit can estimate the user's emotions and adjust the data acquisition method based on the estimated user emotions. For example, if the user is excited, the frequency of data acquisition can be increased to collect more detailed data. Also, if the user is relaxed, the frequency of data acquisition can be adjusted to collect the minimum amount of data necessary. In this way, by adjusting the data acquisition method according to the user's emotions, more appropriate data can be collected.

[0115] The comparison unit can estimate the user's emotions and adjust the accuracy of the comparison based on the estimated user's emotions. For example, if the user is nervous, the comparison unit can increase the accuracy of the comparison and provide a detailed comparison result. Also, if the user is relaxed, the comparison unit can adjust the accuracy of the comparison and compare the minimum amount of data necessary. In this way, by adjusting the accuracy of the comparison according to the user's emotions, more appropriate comparison results can be provided.

[0116] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, if the user is excited, it can provide visually stimulating analysis results. On the other hand, if the user is relaxed, it can provide detailed analysis results. In this way, by adjusting the analysis method according to the user's emotions, it is possible to provide more appropriate analysis results.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The gaze recording unit records gaze movements using visual data storage glasses worn by athletes and instructors. The gaze recording unit is equipped with sensors that record gaze movements, infrared sensors, and cameras that identify the gaze point, and records gaze movements with high accuracy. Step 2: The analysis unit analyzes the data recorded by the gaze recording unit. The analysis unit is equipped with algorithms for analyzing gaze movements, gaze points, and behavioral routines, and can analyze these using machine learning algorithms. Step 3: The acquisition unit acquires the user's biometric data. The acquisition unit acquires the data while the user wears the visual data storage glasses and performs training. The acquisition unit can acquire biometric data such as the user's heart rate and electrodermal activity. Step 4: The comparison unit compares the analysis results obtained by the analysis unit with the user's biometric information data. The comparison unit compares the analysis results with the user's data and clarifies where the differences exist. The comparison unit can use statistical methods to compare the analysis results with the user's data.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An eye gaze recording unit that records eye gaze movements; an analysis unit that analyzes the data recorded by the line-of-sight recording unit; an acquisition unit that acquires biometric information data of a user; a comparison unit that compares the analysis result obtained by the analysis unit with the user's biometric information data; Equipped with A system characterized by:

2. The device includes a gaze recording unit that includes a sensor that records gaze movements.

2. The system of claim 1.

3. A gaze recording unit is provided that includes a camera for identifying a gaze point.

2. The system of claim 1.

4. An analysis unit equipped with algorithms for analyzing eye movements, gaze points, and behavioral routines 2. The system of claim 1.

5. The device includes an acquisition unit that acquires data when a user wears the visual data storage glasses and performs training.

2. The system of claim 1.

6. Equipped with a comparison section that compares the analysis results with the user's data and clarifies specific differences 2. The system of claim 1.

7. The gaze recording unit Estimate the user's emotion and adjust the timing of eye gaze recording based on the estimated user emotion.

2. The system of claim 1.

8. The gaze recording unit When recording gaze, the system selects the optimal recording method by referencing the user's past gaze data.

2. The system of claim 1.

Images