system

Abstract

The system according to the embodiment aims to instantly provide detailed information about players and teams while watching sports. [Solution] A system according to an embodiment includes an eye tracking unit, an information collecting unit, an analysis unit, and an audio providing unit. The eye tracking unit tracks the gaze of spectators. The information collecting unit collects detailed data on players and teams identified by the eye tracking unit from the Internet. The analysis unit analyzes the information collected by the information collecting unit. The audio providing unit provides the information analyzed by the analysis unit by audio.

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] With conventional technology, it was difficult to instantly obtain detailed information about players and teams while watching a sporting event.

[0005] The system according to the embodiment aims to instantly provide detailed information about players and teams while watching sports. [Means for solving the problem]

[0006] The system according to the embodiment includes an eye tracking unit, an information collecting unit, an analysis unit, and an audio providing unit. The eye tracking unit tracks the gaze of spectators. The information collecting unit collects detailed data on players and teams identified by the eye tracking unit from the Internet. The analysis unit analyzes the information collected by the information collecting unit. The audio providing unit provides the information analyzed by the analysis unit by audio. [Effects of the Invention]

[0007] The system according to the embodiment can instantly provide detailed information about players and teams while watching a sporting event. [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) A system according to an embodiment of the present invention utilizes audio glasses to enhance the enjoyment of sports viewing. Spectators wear audio glasses, track their gaze, and identify the players and teams they are looking at. The system then collects player and team information from the Internet, analyzes it in real time, and provides it to spectators via audio. Using audio glasses, spectators can enjoy the game with detailed information, just like a sports commentator. For example, when a spectator is watching a sports game, the audio glasses track the spectator's gaze and identify the player they are looking at. Information about the player, such as the player's name, past performance, and performance in the current game, is provided via audio through the audio glasses. This allows spectators to enjoy the game more deeply while learning detailed information about the players and teams. Furthermore, these audio glasses can be used not only for sports viewing, but also at other events and tourist spots. For example, when viewing a painting in an art museum, detailed information about the painting can be heard via audio. These applications are diverse.

[0029] A sports spectating support system according to an embodiment includes an eye tracking unit, an information collecting unit, an analysis unit, and an audio providing unit. The eye tracking unit tracks the gaze of a spectator. The eye tracking unit can track the gaze of a spectator using, for example, a camera or a sensor. The eye tracking unit can also detect the gaze movement of a spectator in real time and identify a player or team in the spectator's line of sight. The information collecting unit connects to the Internet to collect information about players and teams. The information collecting unit can collect detailed data such as player statistics and team performances. The analysis unit analyzes the collected information using an AI algorithm. The analysis unit analyzes information using techniques such as machine learning and deep learning, and generates information to be provided to spectators. The audio providing unit provides the analyzed information as audio using a speaker. The audio providing unit can provide the analyzed information as audio using, for example, speech synthesis technology, thereby conveying information to spectators in real time. As a result, the sports viewing support system according to the embodiment tracks the gaze of spectators, acquires and analyzes information about players and teams in real time, and provides it in audio form, making watching sports more enjoyable.

[0030] The gaze tracking unit can track the gaze of the spectator using a camera or a sensor. The gaze tracking unit tracks the gaze of the spectator using, for example, a camera. The camera can detect the eye movements of the spectator at high resolution and identify the direction of the gaze. The gaze tracking unit can also track the gaze of the spectator using a sensor. The sensor detects the eye movements of the spectator in real time and identifies the direction of the gaze. As a result, the accuracy of gaze tracking is improved by using a camera or a sensor.

[0031] The information collection unit can connect to the Internet to collect information about players and teams. For example, the information collection unit can connect to the Internet to collect information about players and teams. The information collection unit can collect detailed data such as player statistics and team performance in real time. The information collection unit can also collect the latest news articles and social media posts on the Internet and obtain information to provide to spectators. In this way, by connecting to the Internet, the latest information can be collected in real time.

[0032] The analysis unit can analyze the collected information using an AI algorithm. The analysis unit analyzes the collected information using, for example, an AI algorithm. The AI ​​algorithm analyzes the information using technologies such as machine learning and deep learning, and generates information to be provided to spectators. For example, the analysis unit analyzes a player's past performance and performance in the current match, and generates information to be provided to spectators. The analysis unit can also analyze a team's strategy and tactics, and generate information to be provided to spectators. As a result, the use of an AI algorithm improves the accuracy of information analysis.

[0033] The audio providing unit can provide the analyzed information by audio using a speaker. The audio providing unit can provide the analyzed information by audio using, for example, a speaker. The speaker can convey information to spectators with high sound quality. The audio providing unit can also provide the analyzed information by audio using voice synthesis technology. Voice synthesis technology can convey information to spectators in a natural voice. As a result, by using a speaker, information can be provided to spectators by audio.

[0034] The audio glasses can start functioning when worn by a spectator. For example, the audio glasses can start functioning when worn by a spectator. When a spectator wears the audio glasses, the gaze tracking unit, information collection unit, analysis unit, and audio provision unit automatically start operating. Furthermore, while the spectator is wearing the audio glasses, the audio glasses can constantly track the gaze of the spectator, collect and analyze information, and provide it in audio form. As a result, when a spectator wears the audio glasses, the system automatically starts operating.

[0035] The gaze tracking unit can correct the gaze based on the movement or posture of the spectator's head during gaze tracking. For example, if the spectator moves their head left or right, the gaze tracking unit corrects the movement to identify the exact position of the gaze. If the spectator moves their body forward or backward, the gaze tracking unit can also correct the movement to identify the exact position of the gaze. Furthermore, if the spectator leans their body diagonally, the gaze tracking unit can also correct the movement to identify the exact position of the gaze. In this way, the exact position of the gaze can be identified by taking into account the movement and posture of the spectator's head.

[0036] The gaze tracking unit can evaluate the degree of gaze concentration by detecting the dilation or constriction of the spectator's pupils during gaze tracking. For example, if the spectator's pupils dilate, the gaze tracking unit evaluates the degree of gaze concentration as high. If the spectator's pupils constrict, the gaze tracking unit can also evaluate the degree of gaze concentration as low. The gaze tracking unit can also detect changes in the spectator's pupils in real time and dynamically evaluate the degree of gaze concentration. In this way, the degree of gaze concentration can be evaluated by detecting changes in the spectator's pupils.

[0037] The gaze tracking unit can analyze the blinking frequency of the spectator during gaze tracking to evaluate the stability of the gaze. For example, if the spectator blinks frequently, the gaze tracking unit evaluates the gaze stability as low. If the spectator blinks frequently, the gaze tracking unit can also evaluate the gaze stability as high. The gaze tracking unit can also analyze the blinking frequency of the spectator in real time to dynamically evaluate the gaze stability. In this way, the gaze stability can be evaluated by analyzing the blinking frequency of the spectator.

[0038] The eye tracking unit can customize the eye tracking algorithm based on the age and gender of the spectator when tracking the eye gaze. For example, if the spectator is young, the eye tracking unit can set the eye tracking algorithm to a high speed. If the spectator is elderly, the eye tracking unit can also set the eye tracking algorithm to a low speed. The eye tracking unit can also fine-tune the eye tracking algorithm based on the gender of the spectator. This allows for more accurate eye tracking by customizing the eye tracking algorithm based on the age and gender of the spectator.

[0039] The gaze tracking unit can correct the gaze tracking by taking into account the spectator's visual acuity information during gaze tracking. For example, if the spectator is myopic, the gaze tracking unit corrects the gaze tracking to identify the accurate gaze position. If the spectator is farsighted, the gaze tracking unit can also correct the gaze tracking to identify the accurate gaze position. The gaze tracking unit can also acquire the spectator's visual acuity information in real time and dynamically correct the gaze tracking. In this way, the accurate gaze position can be identified by taking into account the spectator's visual acuity information.

[0040] The gaze tracking unit can adjust the accuracy of gaze tracking by detecting whether the spectator is wearing glasses or contact lenses during gaze tracking. For example, the gaze tracking unit increases the accuracy of gaze tracking when the spectator is wearing glasses. The gaze tracking unit can also maintain normal accuracy of gaze tracking when the spectator is wearing contact lenses. The gaze tracking unit can also adjust the accuracy of gaze tracking when the spectator is not wearing glasses or contact lenses. In this way, the accuracy of gaze tracking can be adjusted by detecting whether the spectator is wearing glasses or contact lenses.

[0041] When collecting information, the information collecting unit can refer to the spectator's past watching history and preferentially collect highly relevant information. For example, the information collecting unit preferentially collects information about matches that the spectator has watched in the past. The information collecting unit can also preferentially collect information about players or teams that the spectator has shown interest in in the past. The information collecting unit can also analyze the spectator's past watching history and preferentially collect highly relevant information. In this way, by referring to the spectator's past watching history, highly relevant information can be preferentially collected.

[0042] When collecting information, the information collection unit can collect region-specific information based on the current location information of the spectators. For example, the information collection unit prioritizes collecting information about the stadium where the spectators are located. The information collection unit can also prioritize collecting information about teams and players in the region where the spectators are located. The information collection unit can also collect region-specific information based on the current location information of the spectators. In this way, region-specific information can be collected by taking the current location information of the spectators into consideration.

[0043] When collecting information, the information collecting unit can collect information in multiple languages ​​based on the language setting of the spectator. For example, the information collecting unit collects information about players and teams based on the language setting of the spectator. The information collecting unit can also provide a language switching function when the spectator speaks multiple languages. The information collecting unit can also collect information in multiple languages ​​based on the language setting of the spectator. In this way, by collecting information in multiple languages ​​based on the language setting of the spectator, it is possible to provide information suitable for the spectator.

[0044] When collecting information, the information collection unit can analyze the social media activities of spectators to collect relevant information. For example, the information collection unit collects information about players and teams that spectators follow on social media. The information collection unit can also analyze the content posted on social media by spectators to collect relevant information. The information collection unit can also collect relevant information by referring to the activities of the spectators' friends on social media. In this way, highly relevant information can be collected by analyzing the social media activities of spectators.

[0045] When collecting information, the information collecting unit can select the optimal information collection means by taking into consideration device information of the spectator. For example, if the spectator is using a smartphone, the information collecting unit selects the optimal information collection means for the smartphone. If the spectator is using a tablet, the information collecting unit can also select the optimal information collection means for the tablet. Furthermore, if the spectator is using a smartwatch, the information collecting unit can also select the optimal information collection means for the smartwatch. In this way, the optimal information collection means can be selected by taking into consideration device information of the spectator.

[0046] The information collecting unit can optimize the information collecting algorithm by reflecting past feedback from spectators when collecting information. The information collecting unit optimizes the information collecting algorithm based on, for example, feedback provided by spectators in the past. The information collecting unit can also adjust the priority of information collection based on the past feedback from spectators. The information collecting unit can also analyze the past feedback from spectators and improve the information collecting algorithm. In this way, the information collecting algorithm can be optimized by reflecting the past feedback from spectators.

[0047] During analysis, the analysis unit can optimize the analysis algorithm by referring to the spectator's past viewing history. The analysis unit optimizes the analysis algorithm, for example, based on data on matches that the spectator has watched in the past. The analysis unit can also optimize the analysis algorithm based on data on players or teams that the spectator has shown interest in in the past. The analysis unit can also analyze the spectator's past viewing history and improve the analysis algorithm. In this way, the analysis algorithm can be optimized by referring to the spectator's past viewing history.

[0048] During analysis, the analysis unit can determine the priority of analysis based on the spectator's current areas of interest. For example, the analysis unit prioritizes analysis of data on players or teams in which the spectator is currently interested. The analysis unit can also prioritize analysis of data on matches in which the spectator is currently interested. The analysis unit can also determine the priority of analysis based on the spectator's current areas of interest. In this way, by taking the spectator's current areas of interest into consideration, more important information can be prioritized in analysis.

[0049] The analysis unit can improve the analysis algorithm by reflecting spectator feedback during analysis. The analysis unit improves the analysis algorithm based on, for example, feedback provided by spectators. The analysis unit can also adjust the analysis priority based on spectator feedback. The analysis unit can also analyze spectator feedback and optimize the analysis algorithm. In this way, the analysis algorithm can be improved by reflecting spectator feedback.

[0050] During analysis, the analysis unit can customize the analysis algorithm based on the age and gender of the spectator. For example, if the spectator is young, the analysis unit provides detailed analysis results. If the spectator is elderly, the analysis unit can also provide concise analysis results. The analysis unit can also fine-tune the analysis algorithm based on the gender of the spectator. In this way, by customizing the analysis algorithm based on the age and gender of the spectator, more accurate analysis results can be provided.

[0051] During analysis, the analysis unit can adjust the display method of the analysis results by taking into account the spectator's visual acuity information. For example, if the spectator is myopic, the analysis unit provides a display method with high visibility. If the spectator is farsighted, the analysis unit can also provide a display method with high visibility. The analysis unit can also provide an optimal display method based on the spectator's visual acuity information. In this way, by taking into account the spectator's visual acuity information, a display method with higher visibility can be provided.

[0052] During analysis, the analysis unit can detect whether a spectator is wearing glasses or contact lenses and adjust the display method of the analysis results. For example, if the spectator is wearing glasses, the analysis unit provides a display method with high visibility. If the spectator is wearing contact lenses, the analysis unit can also provide a display method with high visibility. Furthermore, if the spectator is not wearing glasses or contact lenses, the analysis unit can also provide a display method with high visibility. In this way, by detecting whether a spectator is wearing glasses or contact lenses, a display method with even higher visibility can be provided.

[0053] When providing audio, the audio providing unit can customize the audio by referring to the spectator's past audio feedback. The audio providing unit customizes the audio, for example, based on the audio tone that the spectator has previously preferred. The audio providing unit can also adjust the speed of the audio based on the feedback that the spectator has previously provided. The audio providing unit can also analyze the spectator's past audio feedback and provide optimal audio. In this way, more appropriate audio information can be provided by referring to the spectator's past audio feedback.

[0054] When providing audio, the audio providing unit can detect the spectator's current environmental sounds and adjust the volume of the audio. For example, if the spectator is in a noisy environment, the audio providing unit can increase the volume of the audio. If the spectator is in a quiet environment, the audio providing unit can also decrease the volume of the audio. The audio providing unit can also detect the spectator's current environmental sounds in real time and dynamically adjust the volume of the audio. In this way, by detecting the spectator's current environmental sounds, audio information can be provided at a more appropriate volume.

[0055] When providing audio, the audio provider can provide audio in multiple languages ​​based on the language setting of the spectator. For example, the audio provider automatically sets the audio based on the language setting of the spectator. The audio provider can also provide a language switching function when the spectator speaks multiple languages. The audio provider can also provide audio in multiple languages ​​based on the language setting of the spectator. In this way, by providing audio in multiple languages ​​based on the language setting of the spectator, it is possible to provide information suitable for the spectator.

[0056] When providing audio, the audio providing unit can customize the tone and content of the audio based on the age and gender of the spectator. For example, if the spectator is young, the audio providing unit can provide detailed information in a bright tone. If the spectator is elderly, the audio providing unit can also provide concise information in a calm tone. The audio providing unit can also fine-tune the tone and content of the audio based on the gender of the spectator. In this way, more appropriate information can be provided by customizing the tone and content of the audio based on the age and gender of the spectator.

[0057] When providing audio, the audio providing unit can adjust the content of the audio taking into account the spectator's visual acuity information. For example, if the spectator is nearsighted, the audio providing unit can provide detailed information. If the spectator is farsighted, the audio providing unit can also provide concise information. The audio providing unit can also provide optimal audio content based on the spectator's visual acuity information. In this way, more appropriate audio information can be provided by taking into account the spectator's visual acuity information.

[0058] The audio providing unit can detect whether a spectator is wearing glasses or contact lenses when providing audio and adjust the content of the audio accordingly. For example, the audio providing unit can provide detailed information if the spectator is wearing glasses. The audio providing unit can also provide detailed information if the spectator is wearing contact lenses. The audio providing unit can also provide concise information if the spectator is not wearing glasses or contact lenses. In this way, more appropriate audio information can be provided by detecting whether a spectator is wearing glasses or contact lenses.

[0059] When worn, the audio glasses can adjust the fit based on the shape and size of the spectator's head. For example, the audio glasses automatically adjust the fit based on the shape of the spectator's head. The audio glasses can also automatically adjust the fit based on the size of the spectator's head. The audio glasses can also detect the shape and size of the spectator's head in real time and dynamically adjust the fit. This allows the spectator to adjust the fit based on the shape and size of the head, providing a more comfortable wearing experience.

[0060] When worn, Audio Glasses can adjust the lenses taking into account the spectator's visual acuity information. For example, if the spectator is nearsighted, Audio Glasses can adjust the lenses to improve visibility. If the spectator is farsighted, Audio Glasses can also adjust the lenses to improve visibility. Audio Glasses can also dynamically adjust the lenses based on the spectator's visual acuity information. This allows for lens adjustment with better visibility by taking into account the spectator's visual acuity information.

[0061] The Audio Glasses can improve the fit by reflecting spectator feedback when worn. For example, the Audio Glasses can improve the fit of the Audio Glasses based on feedback provided by spectators. The Audio Glasses can also adjust the fit based on spectator feedback. The Audio Glasses can also analyze spectator feedback and optimize the fit. This allows for a more comfortable fit by reflecting spectator feedback.

[0062] When worn, Audio Glasses can customize the fit based on the spectator's age and gender. For example, Audio Glasses can provide a tighter fit for younger spectators. Audio Glasses can also provide a looser fit for older spectators. Audio Glasses can also fine-tune the fit based on the spectator's gender. This allows for a more comfortable fit by customizing the fit based on the spectator's age and gender.

[0063] When worn, the audio glasses can select the optimal operating mode based on the spectator's device information. For example, if the spectator is using a smartphone, the audio glasses can select the optimal operating mode for the smartphone. If the spectator is using a tablet, the audio glasses can also select the optimal operating mode for the tablet. Furthermore, if the spectator is using a smartwatch, the audio glasses can also select the optimal operating mode for the smartwatch. In this way, the optimal operating mode can be selected by taking into account the spectator's device information.

[0064] When worn, the audio glasses can optimize the operation algorithm by reflecting past feedback from spectators. For example, the audio glasses optimize the operation algorithm based on feedback provided by spectators. The audio glasses can also adjust the priority of operations based on past feedback from spectators. The audio glasses can also analyze past feedback from spectators and improve the operation algorithm. This allows the operation algorithm to be optimized by reflecting past feedback from spectators.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The gaze tracking unit can detect not only the gaze movement of the spectator, but also the movement and posture of the spectator's head in real time and correct the gaze. For example, if the spectator moves their head from side to side, that movement can be corrected to identify the exact position of the gaze. Also, if the spectator moves their body forward or backward, that movement can be corrected to identify the exact position of the gaze. Furthermore, if the spectator leans their body diagonally, that movement can be corrected to identify the exact position of the gaze. In this way, the exact position of the gaze can be identified by taking into account the movement and posture of the spectator's head.

[0067] The information collection unit can refer to the spectator's past viewing history and preferentially collect highly relevant information. For example, it preferentially collects information about matches that the spectator has watched in the past. The information collection unit can also preferentially collect information about players or teams that the spectator has shown interest in in the past. The information collection unit can also analyze the spectator's past viewing history and preferentially collect highly relevant information. In this way, it is possible to preferentially collect highly relevant information by referring to the spectator's past viewing history.

[0068] The gaze tracking unit can evaluate the degree of gaze concentration by detecting the dilation or constriction of the spectator's pupils during gaze tracking. For example, if the spectator's pupils dilate, it evaluates the degree of gaze concentration as high. If the spectator's pupils constrict, it can also evaluate the degree of gaze concentration as low. The gaze tracking unit can also detect changes in the spectator's pupils in real time and dynamically evaluate the degree of gaze concentration. This makes it possible to evaluate the degree of gaze concentration by detecting changes in the spectator's pupils.

[0069] When collecting information, the information collection unit can collect region-specific information based on the current location information of the spectators. For example, the information collection unit can collect information about the stadium where the spectators are located with priority. The information collection unit can also collect information about teams and players in the region where the spectators are located with priority. The information collection unit can also collect region-specific information based on the current location information of the spectators. In this way, region-specific information can be collected by taking the current location information of the spectators into consideration.

[0070] During analysis, the analysis unit can customize the analysis algorithm based on the age and gender of the spectator. For example, if the spectator is young, the analysis unit can provide detailed analysis results. If the spectator is elderly, the analysis unit can also provide concise analysis results. The analysis unit can also fine-tune the analysis algorithm based on the gender of the spectator. This allows the analysis algorithm to be customized based on the age and gender of the spectator, making it possible to provide more accurate analysis results.

[0071] When providing audio, the audio providing unit can detect the spectator's current environmental sounds and adjust the volume of the audio. For example, if the spectator is in a noisy environment, the audio providing unit can increase the volume of the audio. If the spectator is in a quiet environment, the audio providing unit can also decrease the volume of the audio. The audio providing unit can also detect the spectator's current environmental sounds in real time and dynamically adjust the volume of the audio. In this way, by detecting the spectator's current environmental sounds, audio information can be provided at a more appropriate volume.

[0072] When worn, the Audio Glasses can adjust the fit based on the shape and size of the spectator's head. For example, the Audio Glasses can automatically adjust the fit based on the shape and size of the spectator's head. The Audio Glasses can also automatically adjust the fit based on the size of the spectator's head. The Audio Glasses can also detect the shape and size of the spectator's head in real time and dynamically adjust the fit. This allows the fit to be adjusted based on the shape and size of the spectator's head, providing a more comfortable wearing experience.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The eye tracking unit tracks the gaze of the spectator. The eye tracking unit uses cameras and sensors to detect the gaze of the spectator in real time and identify the player or team in their line of sight. Step 2: The information gathering unit connects to the Internet to gather detailed data on players and teams. The information gathering unit gathers data such as player statistics and team performance. Step 3: The analysis unit uses AI algorithms to analyze the collected information. The analysis unit uses techniques such as machine learning and deep learning to analyze the information and generate information to provide to spectators. Step 4: The audio provider uses a speaker to provide the analyzed information by voice. The audio provider uses voice synthesis technology to provide the analyzed information by voice, conveying the information to spectators in real time.

[0075] (Example 2) A system according to an embodiment of the present invention utilizes audio glasses to enhance the enjoyment of sports viewing. Spectators wear audio glasses, track their gaze, and identify the players and teams they are looking at. The system then collects player and team information from the Internet, analyzes it in real time, and provides it to spectators via audio. Using audio glasses, spectators can enjoy the game with detailed information, just like a sports commentator. For example, when a spectator is watching a sports game, the audio glasses track the spectator's gaze and identify the player they are looking at. Information about the player, such as the player's name, past performance, and performance in the current game, is provided via audio through the audio glasses. This allows spectators to enjoy the game more deeply while learning detailed information about the players and teams. Furthermore, these audio glasses can be used not only for sports viewing, but also at other events and tourist spots. For example, when viewing a painting in an art museum, detailed information about the painting can be heard via audio. These applications are diverse.

[0076] A sports spectating support system according to an embodiment includes an eye tracking unit, an information collecting unit, an analysis unit, and an audio providing unit. The eye tracking unit tracks the gaze of a spectator. The eye tracking unit can track the gaze of a spectator using, for example, a camera or a sensor. The eye tracking unit can also detect the gaze movement of a spectator in real time and identify a player or team in the spectator's line of sight. The information collecting unit connects to the Internet to collect information about players and teams. The information collecting unit can collect detailed data such as player statistics and team performances. The analysis unit analyzes the collected information using an AI algorithm. The analysis unit analyzes information using techniques such as machine learning and deep learning, and generates information to be provided to spectators. The audio providing unit provides the analyzed information as audio using a speaker. The audio providing unit can provide the analyzed information as audio using, for example, speech synthesis technology, thereby conveying information to spectators in real time. As a result, the sports viewing support system according to the embodiment tracks the gaze of spectators, acquires and analyzes information about players and teams in real time, and provides it in audio form, making watching sports more enjoyable.

[0077] The gaze tracking unit can track the gaze of the spectator using a camera or a sensor. The gaze tracking unit tracks the gaze of the spectator using, for example, a camera. The camera can detect the eye movements of the spectator at high resolution and identify the direction of the gaze. The gaze tracking unit can also track the gaze of the spectator using a sensor. The sensor detects the eye movements of the spectator in real time and identifies the direction of the gaze. As a result, the accuracy of gaze tracking is improved by using a camera or a sensor.

[0078] The information collection unit can connect to the Internet to collect information about players and teams. For example, the information collection unit can connect to the Internet to collect information about players and teams. The information collection unit can collect detailed data such as player statistics and team performance in real time. The information collection unit can also collect the latest news articles and social media posts on the Internet and obtain information to provide to spectators. In this way, by connecting to the Internet, the latest information can be collected in real time.

[0079] The analysis unit can analyze the collected information using an AI algorithm. The analysis unit analyzes the collected information using, for example, an AI algorithm. The AI ​​algorithm analyzes the information using technologies such as machine learning and deep learning, and generates information to be provided to spectators. For example, the analysis unit analyzes a player's past performance and performance in the current match, and generates information to be provided to spectators. The analysis unit can also analyze a team's strategy and tactics, and generate information to be provided to spectators. As a result, the use of an AI algorithm improves the accuracy of information analysis.

[0080] The audio providing unit can provide the analyzed information by audio using a speaker. The audio providing unit can provide the analyzed information by audio using, for example, a speaker. The speaker can convey information to spectators with high sound quality. The audio providing unit can also provide the analyzed information by audio using voice synthesis technology. Voice synthesis technology can convey information to spectators in a natural voice. As a result, by using a speaker, information can be provided to spectators by audio.

[0081] The audio glasses can start functioning when worn by a spectator. For example, the audio glasses can start functioning when worn by a spectator. When a spectator wears the audio glasses, the gaze tracking unit, information collection unit, analysis unit, and audio provision unit automatically start operating. Furthermore, while the spectator is wearing the audio glasses, the audio glasses can constantly track the gaze of the spectator, collect and analyze information, and provide it in audio form. As a result, when a spectator wears the audio glasses, the system automatically starts operating.

[0082] The gaze tracking unit can estimate the emotion of the spectator and adjust the accuracy of gaze tracking based on the estimated emotion of the spectator. For example, the gaze tracking unit estimates the emotion of the spectator and adjusts the accuracy of gaze tracking based on the estimated emotion of the spectator. When the spectator is excited, the gaze tracking unit increases the accuracy of gaze tracking to detect even subtle gaze movements. When the spectator is relaxed, the gaze tracking unit can maintain normal accuracy and track natural gaze movements. When the spectator is tired, the gaze tracking unit can slightly relax the accuracy of gaze tracking to detect rough gaze movements. This enables more accurate gaze tracking by adjusting the accuracy of gaze tracking according to the emotion of the spectator. The estimation of the emotion of the spectator is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The gaze tracking unit can correct the gaze based on the movement or posture of the spectator's head during gaze tracking. For example, if the spectator moves their head left or right, the gaze tracking unit corrects the movement to identify the exact position of the gaze. If the spectator moves their body forward or backward, the gaze tracking unit can also correct the movement to identify the exact position of the gaze. Furthermore, if the spectator leans their body diagonally, the gaze tracking unit can also correct the movement to identify the exact position of the gaze. In this way, the exact position of the gaze can be identified by taking into account the movement and posture of the spectator's head.

[0084] The gaze tracking unit can evaluate the degree of gaze concentration by detecting the dilation or constriction of the spectator's pupils during gaze tracking. For example, if the spectator's pupils dilate, the gaze tracking unit evaluates the degree of gaze concentration as high. If the spectator's pupils constrict, the gaze tracking unit can also evaluate the degree of gaze concentration as low. The gaze tracking unit can also detect changes in the spectator's pupils in real time and dynamically evaluate the degree of gaze concentration. In this way, the degree of gaze concentration can be evaluated by detecting changes in the spectator's pupils.

[0085] The gaze tracking unit can analyze the blinking frequency of the spectator during gaze tracking to evaluate the stability of the gaze. For example, if the spectator blinks frequently, the gaze tracking unit evaluates the gaze stability as low. If the spectator blinks frequently, the gaze tracking unit can also evaluate the gaze stability as high. The gaze tracking unit can also analyze the blinking frequency of the spectator in real time to dynamically evaluate the gaze stability. In this way, the gaze stability can be evaluated by analyzing the blinking frequency of the spectator.

[0086] The gaze tracking unit can estimate the emotion of the spectator and adjust the timing of starting gaze tracking based on the estimated emotion of the spectator. For example, if the spectator is excited, the gaze tracking unit can immediately start gaze tracking. If the spectator is relaxed, the gaze tracking unit can also start gaze tracking with a slight delay. Furthermore, if the spectator is tired, the gaze tracking unit can also start gaze tracking slowly. In this way, by adjusting the timing of starting gaze tracking according to the emotion of the spectator, gaze tracking can be started at a more appropriate time. The estimation of the emotion of the spectator is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The eye tracking unit can customize the eye tracking algorithm based on the age and gender of the spectator when tracking the eye gaze. For example, if the spectator is young, the eye tracking unit can set the eye tracking algorithm to a high speed. If the spectator is elderly, the eye tracking unit can also set the eye tracking algorithm to a low speed. The eye tracking unit can also fine-tune the eye tracking algorithm based on the gender of the spectator. This allows for more accurate eye tracking by customizing the eye tracking algorithm based on the age and gender of the spectator.

[0088] The gaze tracking unit can correct the gaze tracking by taking into account the spectator's visual acuity information during gaze tracking. For example, if the spectator is myopic, the gaze tracking unit corrects the gaze tracking to identify the accurate gaze position. If the spectator is farsighted, the gaze tracking unit can also correct the gaze tracking to identify the accurate gaze position. The gaze tracking unit can also acquire the spectator's visual acuity information in real time and dynamically correct the gaze tracking. In this way, the accurate gaze position can be identified by taking into account the spectator's visual acuity information.

[0089] The gaze tracking unit can adjust the accuracy of gaze tracking by detecting whether the spectator is wearing glasses or contact lenses during gaze tracking. For example, the gaze tracking unit increases the accuracy of gaze tracking when the spectator is wearing glasses. The gaze tracking unit can also maintain normal accuracy of gaze tracking when the spectator is wearing contact lenses. The gaze tracking unit can also adjust the accuracy of gaze tracking when the spectator is not wearing glasses or contact lenses. In this way, the accuracy of gaze tracking can be adjusted by detecting whether the spectator is wearing glasses or contact lenses.

[0090] The information collection unit can estimate the emotions of the spectators and determine the priority of information collection based on the estimated emotions of the spectators. For example, if the spectators are excited, the information collection unit can prioritize collecting information about important players and teams. If the spectators are relaxed, the information collection unit can also prioritize collecting general information. Furthermore, if the spectators are tired, the information collection unit can prioritize collecting simple information. In this way, by determining the priority of information collection according to the emotions of the spectators, more important information can be collected preferentially. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When collecting information, the information collecting unit can refer to the spectator's past watching history and preferentially collect highly relevant information. For example, the information collecting unit preferentially collects information about matches that the spectator has watched in the past. The information collecting unit can also preferentially collect information about players or teams that the spectator has shown interest in in the past. The information collecting unit can also analyze the spectator's past watching history and preferentially collect highly relevant information. In this way, by referring to the spectator's past watching history, highly relevant information can be preferentially collected.

[0092] When collecting information, the information collection unit can collect region-specific information based on the current location information of the spectators. For example, the information collection unit prioritizes collecting information about the stadium where the spectators are located. The information collection unit can also prioritize collecting information about teams and players in the region where the spectators are located. The information collection unit can also collect region-specific information based on the current location information of the spectators. In this way, region-specific information can be collected by taking the current location information of the spectators into consideration.

[0093] When collecting information, the information collecting unit can collect information in multiple languages ​​based on the language setting of the spectator. For example, the information collecting unit collects information about players and teams based on the language setting of the spectator. The information collecting unit can also provide a language switching function when the spectator speaks multiple languages. The information collecting unit can also collect information in multiple languages ​​based on the language setting of the spectator. In this way, by collecting information in multiple languages ​​based on the language setting of the spectator, it is possible to provide information suitable for the spectator.

[0094] The information collection unit can estimate the emotions of the spectators and adjust the frequency of information collection based on the estimated emotions of the spectators. For example, if the spectators are excited, the information collection unit increases the frequency of information collection. If the spectators are relaxed, the information collection unit can also maintain the frequency of information collection at a normal level. Furthermore, if the spectators are tired, the information collection unit can also slightly reduce the frequency of information collection. In this way, by adjusting the frequency of information collection according to the emotions of the spectators, information can be collected at a more appropriate time. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using 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.

[0095] When collecting information, the information collection unit can analyze the social media activities of spectators to collect relevant information. For example, the information collection unit collects information about players and teams that spectators follow on social media. The information collection unit can also analyze the content posted on social media by spectators to collect relevant information. The information collection unit can also collect relevant information by referring to the activities of the spectators' friends on social media. In this way, highly relevant information can be collected by analyzing the social media activities of spectators.

[0096] When collecting information, the information collecting unit can select the optimal information collection means by taking into consideration device information of the spectator. For example, if the spectator is using a smartphone, the information collecting unit selects the optimal information collection means for the smartphone. If the spectator is using a tablet, the information collecting unit can also select the optimal information collection means for the tablet. Furthermore, if the spectator is using a smartwatch, the information collecting unit can also select the optimal information collection means for the smartwatch. In this way, the optimal information collection means can be selected by taking into consideration device information of the spectator.

[0097] The information collecting unit can optimize the information collecting algorithm by reflecting past feedback from spectators when collecting information. The information collecting unit optimizes the information collecting algorithm based on, for example, feedback provided by spectators in the past. The information collecting unit can also adjust the priority of information collection based on the past feedback from spectators. The information collecting unit can also analyze the past feedback from spectators and improve the information collecting algorithm. In this way, the information collecting algorithm can be optimized by reflecting the past feedback from spectators.

[0098] The analysis unit can estimate the emotions of the spectators and adjust the display method of the analysis results based on the estimated emotions of the spectators. For example, if the spectator is excited, the analysis unit can provide a visually stimulating display method. If the spectator is relaxed, the analysis unit can also provide a calm display method. Furthermore, if the spectator is tired, the analysis unit can also provide a simple, highly visible display method. This allows for a more appropriate display by adjusting the display method of the analysis results according to the emotions of the spectators. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0099] During analysis, the analysis unit can optimize the analysis algorithm by referring to the spectator's past viewing history. The analysis unit optimizes the analysis algorithm, for example, based on data on matches that the spectator has watched in the past. The analysis unit can also optimize the analysis algorithm based on data on players or teams that the spectator has shown interest in in the past. The analysis unit can also analyze the spectator's past viewing history and improve the analysis algorithm. In this way, the analysis algorithm can be optimized by referring to the spectator's past viewing history.

[0100] During analysis, the analysis unit can determine the priority of analysis based on the spectator's current areas of interest. For example, the analysis unit prioritizes analysis of data on players or teams in which the spectator is currently interested. The analysis unit can also prioritize analysis of data on matches in which the spectator is currently interested. The analysis unit can also determine the priority of analysis based on the spectator's current areas of interest. In this way, by taking the spectator's current areas of interest into consideration, more important information can be prioritized in analysis.

[0101] The analysis unit can improve the analysis algorithm by reflecting spectator feedback during analysis. The analysis unit improves the analysis algorithm based on, for example, feedback provided by spectators. The analysis unit can also adjust the analysis priority based on spectator feedback. The analysis unit can also analyze spectator feedback and optimize the analysis algorithm. In this way, the analysis algorithm can be improved by reflecting spectator feedback.

[0102] The analysis unit can estimate the emotions of the spectators and adjust the level of detail of the analysis based on the estimated emotions of the spectators. For example, if the spectators are excited, the analysis unit provides detailed analysis results. If the spectators are relaxed, the analysis unit can also provide general analysis results. Furthermore, if the spectators are tired, the analysis unit can also provide concise analysis results. In this way, by adjusting the level of detail of the analysis according to the emotions of the spectators, more appropriate analysis results can be provided. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using 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 these examples.

[0103] During analysis, the analysis unit can customize the analysis algorithm based on the age and gender of the spectator. For example, if the spectator is young, the analysis unit provides detailed analysis results. If the spectator is elderly, the analysis unit can also provide concise analysis results. The analysis unit can also fine-tune the analysis algorithm based on the gender of the spectator. In this way, by customizing the analysis algorithm based on the age and gender of the spectator, more accurate analysis results can be provided.

[0104] During analysis, the analysis unit can adjust the display method of the analysis results by taking into account the spectator's visual acuity information. For example, if the spectator is myopic, the analysis unit provides a display method with high visibility. If the spectator is farsighted, the analysis unit can also provide a display method with high visibility. The analysis unit can also provide an optimal display method based on the spectator's visual acuity information. In this way, by taking into account the spectator's visual acuity information, a display method with higher visibility can be provided.

[0105] During analysis, the analysis unit can detect whether a spectator is wearing glasses or contact lenses and adjust the display method of the analysis results. For example, if the spectator is wearing glasses, the analysis unit provides a display method with high visibility. If the spectator is wearing contact lenses, the analysis unit can also provide a display method with high visibility. Furthermore, if the spectator is not wearing glasses or contact lenses, the analysis unit can also provide a display method with high visibility. In this way, by detecting whether a spectator is wearing glasses or contact lenses, a display method with even higher visibility can be provided.

[0106] The audio providing unit can estimate the emotions of the spectators and adjust the tone and speed of the audio based on the estimated emotions of the spectators. For example, if the spectators are excited, the audio providing unit can increase the tone and speed of the audio. If the spectators are relaxed, the audio providing unit can also calm the tone and maintain the normal speed of the audio. Furthermore, if the spectators are tired, the audio providing unit can calm the tone and slow the speed of the audio. In this way, by adjusting the tone and speed of the audio according to the emotions of the spectators, more appropriate audio information can be provided. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] When providing audio, the audio providing unit can customize the audio by referring to the spectator's past audio feedback. The audio providing unit customizes the audio, for example, based on the audio tone that the spectator has previously preferred. The audio providing unit can also adjust the speed of the audio based on the feedback that the spectator has previously provided. The audio providing unit can also analyze the spectator's past audio feedback and provide optimal audio. In this way, more appropriate audio information can be provided by referring to the spectator's past audio feedback.

[0108] When providing audio, the audio providing unit can detect the spectator's current environmental sounds and adjust the volume of the audio. For example, if the spectator is in a noisy environment, the audio providing unit can increase the volume of the audio. If the spectator is in a quiet environment, the audio providing unit can also decrease the volume of the audio. The audio providing unit can also detect the spectator's current environmental sounds in real time and dynamically adjust the volume of the audio. In this way, by detecting the spectator's current environmental sounds, audio information can be provided at a more appropriate volume.

[0109] When providing audio, the audio provider can provide audio in multiple languages ​​based on the language setting of the spectator. For example, the audio provider automatically sets the audio based on the language setting of the spectator. The audio provider can also provide a language switching function when the spectator speaks multiple languages. The audio provider can also provide audio in multiple languages ​​based on the language setting of the spectator. In this way, by providing audio in multiple languages ​​based on the language setting of the spectator, it is possible to provide information suitable for the spectator.

[0110] The audio providing unit can estimate the emotion of the spectator and adjust the content of the audio based on the estimated emotion of the spectator. For example, if the spectator is excited, the audio providing unit can provide detailed information. If the spectator is relaxed, the audio providing unit can also provide general information. Furthermore, if the spectator is tired, the audio providing unit can also provide concise information. In this way, by adjusting the content of the audio according to the emotion of the spectator, more appropriate information can be provided. The estimation of the emotion of the spectator is realized using an emotion estimation function, for example, using 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 these examples.

[0111] When providing audio, the audio providing unit can customize the tone and content of the audio based on the age and gender of the spectator. For example, if the spectator is young, the audio providing unit can provide detailed information in a bright tone. If the spectator is elderly, the audio providing unit can also provide concise information in a calm tone. The audio providing unit can also fine-tune the tone and content of the audio based on the gender of the spectator. In this way, more appropriate information can be provided by customizing the tone and content of the audio based on the age and gender of the spectator.

[0112] When providing audio, the audio providing unit can adjust the content of the audio taking into account the spectator's visual acuity information. For example, if the spectator is nearsighted, the audio providing unit can provide detailed information. If the spectator is farsighted, the audio providing unit can also provide concise information. The audio providing unit can also provide optimal audio content based on the spectator's visual acuity information. In this way, more appropriate audio information can be provided by taking into account the spectator's visual acuity information.

[0113] The audio providing unit can detect whether a spectator is wearing glasses or contact lenses when providing audio and adjust the content of the audio accordingly. For example, the audio providing unit can provide detailed information if the spectator is wearing glasses. The audio providing unit can also provide detailed information if the spectator is wearing contact lenses. The audio providing unit can also provide concise information if the spectator is not wearing glasses or contact lenses. In this way, more appropriate audio information can be provided by detecting whether a spectator is wearing glasses or contact lenses.

[0114] The audio glasses can estimate the emotions of the spectators and adjust the operation mode of the audio glasses based on the estimated emotions of the spectators. For example, if the spectators are excited, the audio glasses can set the operation mode of the audio glasses to high performance. If the spectators are relaxed, the audio glasses can also set the operation mode of the audio glasses to normal. Furthermore, if the spectators are tired, the audio glasses can also set the operation mode of the audio glasses to energy saving mode. This allows for more appropriate operation by adjusting the operation mode of the audio glasses according to the emotions of the spectators. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] When worn, the audio glasses can adjust the fit based on the shape and size of the spectator's head. For example, the audio glasses automatically adjust the fit based on the shape of the spectator's head. The audio glasses can also automatically adjust the fit based on the size of the spectator's head. The audio glasses can also detect the shape and size of the spectator's head in real time and dynamically adjust the fit. This allows the spectator to adjust the fit based on the shape and size of the head, providing a more comfortable wearing experience.

[0116] When worn, Audio Glasses can adjust the lenses taking into account the spectator's visual acuity information. For example, if the spectator is nearsighted, Audio Glasses can adjust the lenses to improve visibility. If the spectator is farsighted, Audio Glasses can also adjust the lenses to improve visibility. Audio Glasses can also dynamically adjust the lenses based on the spectator's visual acuity information. This allows for lens adjustment with better visibility by taking into account the spectator's visual acuity information.

[0117] The Audio Glasses can improve the fit by reflecting spectator feedback when worn. For example, the Audio Glasses can improve the fit of the Audio Glasses based on feedback provided by spectators. The Audio Glasses can also adjust the fit based on spectator feedback. The Audio Glasses can also analyze spectator feedback and optimize the fit. This allows for a more comfortable fit by reflecting spectator feedback.

[0118] The audio glasses can estimate the emotions of the spectators and adjust the timing of their operation based on the estimated emotions of the spectators. For example, if the spectators are excited, the audio glasses can immediately start their operation. If the spectators are relaxed, the audio glasses can also start their operation with a slight delay. Also, if the spectators are tired, the audio glasses can start their operation slowly. In this way, by adjusting the operation timing of the audio glasses according to the emotions of the spectators, operation can be started at a more appropriate time. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using 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 these examples.

[0119] When worn, Audio Glasses can customize the fit based on the spectator's age and gender. For example, Audio Glasses can provide a tighter fit for younger spectators. Audio Glasses can also provide a looser fit for older spectators. Audio Glasses can also fine-tune the fit based on the spectator's gender. This allows for a more comfortable fit by customizing the fit based on the spectator's age and gender.

[0120] When worn, the audio glasses can select the optimal operating mode based on the spectator's device information. For example, if the spectator is using a smartphone, the audio glasses can select the optimal operating mode for the smartphone. If the spectator is using a tablet, the audio glasses can also select the optimal operating mode for the tablet. Furthermore, if the spectator is using a smartwatch, the audio glasses can also select the optimal operating mode for the smartwatch. In this way, the optimal operating mode can be selected by taking into account the spectator's device information.

[0121] When worn, the audio glasses can optimize the operation algorithm by reflecting past feedback from spectators. For example, the audio glasses optimize the operation algorithm based on feedback provided by spectators. The audio glasses can also adjust the priority of operations based on past feedback from spectators. The audio glasses can also analyze past feedback from spectators and improve the operation algorithm. This allows the operation algorithm to be optimized by reflecting past feedback from spectators. === Hard Collateral 1-1 === Each of the multiple elements including the gaze tracking unit, information collecting unit, analysis unit, and audio providing 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 tracking unit tracks the gaze of a spectator using the camera 42 or sensor of the smart device 14, and identifies the player or team in the gaze direction using the control unit 46A. The information collecting unit collects information on players and teams from the Internet using, for example, the identification processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by, for example, the identification processing unit 290 of the data processing device 12 using an AI algorithm. The audio providing unit provides the analyzed information by audio using, for example, the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the gaze tracking unit, information collecting unit, analysis unit, and audio providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the gaze tracking unit tracks the gaze of a spectator using the camera 42 or sensor of the smart glasses 214, and identifies the player or team in the gaze direction using the control unit 46A. The information collecting unit, for example, collects information on players and teams from the Internet using the identification processing unit 290 of the data processing device 12. The analysis unit, for example, analyzes the information collected by the identification processing unit 290 of the data processing device 12 using an AI algorithm. The audio providing unit, for example, provides the analyzed information by audio using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the gaze tracking unit, information collecting unit, analysis unit, and audio providing unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the gaze tracking unit tracks the gaze of a spectator using the camera 42 or sensor of the headset terminal 314, and identifies the player or team in the direction of the gaze using the control unit 46A. The information collecting unit collects information about players and teams from the Internet using, for example, the identification processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by, for example, the identification processing unit 290 of the data processing device 12 using an AI algorithm. The audio providing unit provides the analyzed information by audio using, for example, the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the gaze tracking unit, information collecting unit, analysis unit, and audio providing 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 tracking unit tracks the gaze of spectators using the camera 42 or sensor of the robot 414, and identifies the player or team in the direction of the gaze using the control unit 46A. The information collecting unit collects information about players and teams from the Internet using, for example, the identification processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by, for example, the identification processing unit 290 of the data processing device 12 using an AI algorithm. The audio providing unit provides the analyzed information by audio using, for example, the speaker 240 of the robot 414.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The gaze tracking unit can detect not only the gaze movement of the spectator, but also the movement and posture of the spectator's head in real time and correct the gaze. For example, if the spectator moves their head from side to side, that movement can be corrected to identify the exact position of the gaze. Also, if the spectator moves their body forward or backward, that movement can be corrected to identify the exact position of the gaze. Furthermore, if the spectator leans their body diagonally, that movement can be corrected to identify the exact position of the gaze. In this way, the exact position of the gaze can be identified by taking into account the movement and posture of the spectator's head.

[0124] The information collection unit can refer to the spectator's past viewing history and preferentially collect highly relevant information. For example, it preferentially collects information about matches that the spectator has watched in the past. The information collection unit can also preferentially collect information about players or teams that the spectator has shown interest in in the past. The information collection unit can also analyze the spectator's past viewing history and preferentially collect highly relevant information. In this way, it is possible to preferentially collect highly relevant information by referring to the spectator's past viewing history.

[0125] The analysis unit can estimate the emotions of the spectators and adjust the display method of the analysis results based on the estimated emotions of the spectators. For example, if the spectators are excited, a visually stimulating display method is provided. If the spectators are relaxed, the analysis unit can also provide a calming display method. If the spectators are tired, a simple, highly visible display method is also provided. This allows for a more appropriate display by adjusting the display method of the analysis results according to the emotions of the spectators. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0126] The audio providing unit can estimate the emotions of the spectator and adjust the tone and speed of the audio based on the estimated emotions of the spectator. For example, if the spectator is excited, the audio tone can be raised and the speed can be increased. If the spectator is relaxed, the audio providing unit can also calm the tone of the audio and maintain a normal speed. Also, if the spectator is tired, the audio tone can be calmed and the speed can be slowed. In this way, by adjusting the tone and speed of the audio according to the emotions of the spectator, more appropriate audio information can be provided. The estimation of the emotions of the spectator is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] The audio glasses can estimate the emotions of the spectators and adjust the operation mode of the audio glasses based on the estimated emotions of the spectators. For example, if the spectator is excited, the operation mode of the audio glasses can be set to high performance. If the spectator is relaxed, the audio glasses can also set the operation mode of the audio glasses to normal. Also, if the spectator is tired, the operation mode of the audio glasses can be set to energy saving mode. This allows for more appropriate operation by adjusting the operation mode of the audio glasses according to the emotions of the spectators. The estimation of the emotions of the spectators is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0128] The gaze tracking unit can evaluate the degree of gaze concentration by detecting the dilation or constriction of the spectator's pupils during gaze tracking. For example, if the spectator's pupils dilate, it evaluates the degree of gaze concentration as high. If the spectator's pupils constrict, it can also evaluate the degree of gaze concentration as low. The gaze tracking unit can also detect changes in the spectator's pupils in real time and dynamically evaluate the degree of gaze concentration. This makes it possible to evaluate the degree of gaze concentration by detecting changes in the spectator's pupils.

[0129] When collecting information, the information collection unit can collect region-specific information based on the current location information of the spectators. For example, the information collection unit can collect information about the stadium where the spectators are located with priority. The information collection unit can also collect information about teams and players in the region where the spectators are located with priority. The information collection unit can also collect region-specific information based on the current location information of the spectators. In this way, region-specific information can be collected by taking the current location information of the spectators into consideration.

[0130] During analysis, the analysis unit can customize the analysis algorithm based on the age and gender of the spectator. For example, if the spectator is young, the analysis unit can provide detailed analysis results. If the spectator is elderly, the analysis unit can also provide concise analysis results. The analysis unit can also fine-tune the analysis algorithm based on the gender of the spectator. This allows the analysis algorithm to be customized based on the age and gender of the spectator, making it possible to provide more accurate analysis results.

[0131] When providing audio, the audio providing unit can detect the spectator's current environmental sounds and adjust the volume of the audio. For example, if the spectator is in a noisy environment, the audio providing unit can increase the volume of the audio. If the spectator is in a quiet environment, the audio providing unit can also decrease the volume of the audio. The audio providing unit can also detect the spectator's current environmental sounds in real time and dynamically adjust the volume of the audio. In this way, by detecting the spectator's current environmental sounds, audio information can be provided at a more appropriate volume.

[0132] When worn, the Audio Glasses can adjust the fit based on the shape and size of the spectator's head. For example, the Audio Glasses can automatically adjust the fit based on the shape and size of the spectator's head. The Audio Glasses can also automatically adjust the fit based on the size of the spectator's head. The Audio Glasses can also detect the shape and size of the spectator's head in real time and dynamically adjust the fit. This allows the fit to be adjusted based on the shape and size of the spectator's head, providing a more comfortable wearing experience.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The eye tracking unit tracks the gaze of the spectator. The eye tracking unit uses cameras and sensors to detect the gaze of the spectator in real time and identify the player or team in their line of sight. Step 2: The information gathering unit connects to the Internet to gather detailed data on players and teams. The information gathering unit gathers data such as player statistics and team performance. Step 3: The analysis unit uses AI algorithms to analyze the collected information. The analysis unit uses techniques such as machine learning and deep learning to analyze the information and generate information to provide to spectators. Step 4: The audio provider uses a speaker to provide the analyzed information by voice. The audio provider uses voice synthesis technology to provide the analyzed information by voice, conveying the information to spectators in real time.

[0135] 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.

[0136] 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.

[0137] 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.

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

[0140] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

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

[0156] 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.

[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 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.

[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 (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).

[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] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 tracking unit that tracks the gaze of a spectator; an information collection unit that collects detailed data on the players and teams identified by the gaze tracking unit from the Internet; an analysis unit that analyzes the information collected by the information collection unit; a voice providing unit that provides the information analyzed by the analysis unit by voice; Equipped with A system characterized by:

2. The gaze tracking unit Use a camera or sensor to track the gaze of spectators 2. The system of claim 1.

3. The information collecting unit Connect to the Internet to gather information on players and teams 2. The system of claim 1.

4. The analysis unit Analyze the collected information using AI algorithms 2. The system of claim 1.

5. The voice providing unit Provide analyzed information by voice using a speaker 2. The system of claim 1.

6. Audio glasses are It starts functioning when worn by a spectator.

2. The system of claim 1.

7. The gaze tracking unit Estimate the emotions of spectators and adjust the accuracy of eye tracking based on the estimated emotions of spectators.

2. The system of claim 1.

8. The gaze tracking unit When tracking eyes, correct the gaze based on the spectator's head movement or posture 2. The system of claim 1.

9. The gaze tracking unit During gaze tracking, the degree of gaze concentration is assessed by detecting the dilation and contraction of the spectator's pupils.

2. The system of claim 1.

Images