System

Abstract

A system in accordance with an embodiment aims to allow remote spectators to experience the atmosphere of the arena in real-time and participate in cheering.SOLUTION: A system includes a collection part, a generation part, a provision part, a remote support device, and a transmission part. The collection unit collects voice data of a stadium. The generation unit analyzes the audio data collected by the collection unit and generates audio that reproduces cheers and an atmosphere of the stadium. The provision section provides the sound generated by the generation section to the remote spectator. The remote cheering device is a device for inputting a cheering voice or sound of the remote spectator. The transmission unit transmits the voice data input by the remote support device to the stadium and outputs the voice data from a speaker in the stadium.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult for remote spectators to experience the atmosphere of the stadium in real time and join in the cheering.

[0005] The system according to the embodiment aims to allow remote spectators to experience the atmosphere of the stadium in real time and join in cheering. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a provision unit, a remote cheering device, and a transmission unit. The collection unit collects audio data from the stadium. The generation unit analyzes the audio data collected by the collection unit and generates audio that reproduces the cheers and atmosphere of the stadium. The provision unit provides the audio generated by the generation unit to remote spectators. The remote cheering device is a device for inputting the cheering voices and sounds of the remote spectators. The transmission unit transmits the audio data input by the remote cheering device to the stadium and plays it from speakers in the stadium. [Effects of the Invention]

[0007] The system according to the embodiment allows remote spectators to experience the atmosphere of the stadium in real time and join in cheering. [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 collects and generates stadium audio data and provides it to remote spectators. This system collects stadium audio data, analyzes it using a generation AI, generates audio that reproduces the cheers and excitement of the stadium, and provides it to the remote spectators. Furthermore, remote spectators can use remote cheering devices to participate in the excitement of the venue and have that audio played over the stadium's speakers. For example, multiple microphones installed in the stadium collect audio data in real time, and the generation AI analyzes the audio data to generate audio that reproduces the cheers and cheers of the spectators, the sounds of the competition, and other sounds. The generated audio is then provided to the remote spectators via the Internet. The remote spectators can listen to the live sounds of the stadium in real time from their homes or other locations. Furthermore, when remote spectators input cheering voices and sounds using the remote cheering device, the audio data is transmitted to the stadium via the Internet and played over the stadium's speakers. This allows remote spectators to share the atmosphere of the stadium and feel a sense of unity. This system allows remote spectators to share the atmosphere of the stadium and feel a sense of unity. For example, remote spectators can cheer from home, joining in with the spectators in the stadium. Also, by listening to the cheers and excitement from the stadium in real time, remote spectators can feel like they are actually at the stadium.

[0029] A stadium sound reproduction system according to an embodiment includes a collection unit, a generation unit, a provision unit, a remote cheering device, and a transmission unit. The collection unit collects stadium sound data. For example, the collection unit can collect the sound data using multiple microphones installed in the stadium. The collection unit can also estimate the emotions of spectators and adjust the collection timing based on the emotions in order to adjust the timing of collecting the sound data. The generation unit uses a generation AI to analyze the sound data collected by the collection unit and generate sound that reproduces cheers and cheers from the stadium, the sounds of the competition, and the like. For example, the generation unit can adjust the volume and tone of the collected sound data to reproduce realistic sound. The generation unit can also customize the sound generation algorithm according to the characteristics of different competitions. The provision unit provides the sound generated by the generation unit to a remote spectator. For example, the provision unit can provide the generated sound to the remote spectator via the Internet. The provision unit can also adjust the format of the sound depending on the device of the remote spectator. The remote cheering device is a device through which the remote spectator inputs cheering voices and sounds. The remote cheering device can, for example, estimate the emotions of the remote spectators and adjust the input method of the cheering bat based on the emotions. The transmission unit transmits the audio data input by the remote cheering device to the stadium and plays it from speakers in the stadium. The transmission unit can, for example, estimate the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the emotions. In this way, the stadium audio reproduction system according to the embodiment allows remote spectators to share the atmosphere of the stadium and feel a sense of unity.

[0030] The collection unit can collect audio data using multiple microphones installed within the stadium. The collection unit collects audio data using, for example, multiple microphones installed within the stadium. The multiple microphones may include, for example, condenser microphones, dynamic microphones, etc. The collection unit can also collect audio data from different areas within the stadium and analyze the audio characteristics of each area. The collection unit can also improve the accuracy of collection based on specific events (goals, points, etc.) when collecting audio data. Furthermore, the collection unit can filter and collect environmental sounds (wind, rain, etc.) within the stadium when collecting audio data. In this way, by using multiple microphones, audio data within the stadium can be collected over a wide area. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0031] The generation unit can analyze the collected audio data and generate audio that reproduces the cheers of spectators, cheers, and sounds of the competition. The generation unit, for example, analyzes the collected audio data and generates audio that reproduces the cheers of spectators, cheers, and sounds of the competition. The generation unit, for example, adjusts the volume of the collected audio data to reproduce realistic cheers. The generation unit can also adjust the tone of the collected audio data to reproduce realistic sounds. Furthermore, the generation unit can adjust the balance of the collected audio data to reproduce natural sounds. In this way, by analyzing the collected audio data, realistic sounds of a stadium can be reproduced. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected audio data to the generation AI and cause the generation AI to generate audio that reproduces the cheers of spectators, cheers, and sounds of the competition.

[0032] The providing unit can provide the generated audio to the remote spectators via the Internet. For example, the providing unit can provide the generated audio to the remote spectators via the Internet. The providing unit can provide the audio using a communication method such as HTTP, WebSocket, or a streaming protocol. The providing unit can also adjust the audio format depending on the device of the remote spectator. For example, when viewing on a smartphone, the audio bitrate can be adjusted and provided. Also, when viewing on a tablet, high-quality audio can be provided. Furthermore, the providing unit can adjust the audio quality depending on the network conditions of the remote spectator. For example, when the network is unstable, the audio bitrate can be lowered and provided. In this way, by providing the audio via the Internet, the remote spectators can listen to the audio from the stadium in real time. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0033] The remote cheering bat is a device that allows remote spectators to input cheering voices and sounds. The remote cheering bat is, for example, a device that allows remote spectators to input cheering voices and sounds. The remote cheering bat includes, for example, a voice input function, a vibration function, and a light function. The remote cheering bat can, for example, estimate the emotions of the remote spectators and adjust the cheering bat input method based on the emotions. For example, if the remote spectator is excited, the remote cheering bat can allow them to input cheering voices with simple operations. Also, if the remote spectator is relaxed, the remote cheering bat can provide detailed input options. Furthermore, the remote cheering bat can suggest the optimal input method by referring to the remote spectator's past cheering history. For example, previously used cheering voices and sounds can be automatically displayed as candidates. This allows remote spectators to participate in livening up the stadium by using the remote cheering bat. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without AI.

[0034] The transmitting unit can transmit audio data input by the remote cheering bat to the stadium via the Internet and play it through the stadium's speakers. For example, the transmitting unit transmits audio data input by the remote cheering bat to the stadium via the Internet and play it through the stadium's speakers. The transmitting unit can transmit the audio data using a communication method such as HTTP, WebSocket, or streaming protocol. The transmitting unit can also estimate the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the emotions. For example, if a remote spectator is excited, the transmitting unit can prioritize transmitting the cheering voices of that remote spectator. Also, if a remote spectator becomes quiet, the transmitting unit can prioritize transmitting the cheering voices of other spectators. Furthermore, the transmitting unit can adjust the quality of the transmitted data depending on the remote spectator's network conditions. For example, if the network is unstable, the transmitting unit can lower the bit rate of the audio data before transmitting it. This allows the remote spectators to participate in the excitement of the stadium by transmitting the cheering voices of the remote spectators to the stadium and playing them through the speakers. Some or all of the above-described processing by the transmitting unit can be performed using, for example, AI, or without AI.

[0035] The collection unit can collect audio data from different areas within the stadium and analyze the audio characteristics of each area. For example, the collection unit collects audio data from different areas within the stadium and analyzes the audio characteristics of each area. For example, the collection unit collects audio data from VIP seats in the stadium and analyzes the reactions of specific spectators. The collection unit can also collect audio data from general seating in the stadium and analyze the overall excitement. Furthermore, the collection unit can collect audio data from specific sections of the stadium (such as behind the goal or near the bench) and analyze the differences in cheering between areas. In this way, by collecting and analyzing audio data from different areas, it is possible to understand the differences in cheering between areas. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI.

[0036] The collection unit can improve the accuracy of collection based on a specific event (e.g., a goal, a score, etc.) when collecting audio data. For example, the collection unit improves the accuracy of collection based on a specific event (e.g., a goal, a score, etc.) when collecting audio data. For example, the collection unit collects highly accurate audio data to capture the peak of cheers at the moment a goal is scored. The collection unit can also adjust the sensitivity of the microphone to collect detailed information about the spectators' reactions when a goal is scored. Furthermore, the collection unit can perform noise filtering to clearly collect surrounding audio at the moment an important play is made. In this way, by improving the accuracy of collection based on a specific event, audio data at important moments can be collected with high accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0037] The collection unit can filter and collect environmental sounds (wind, rain, etc.) in the stadium when collecting audio data. For example, the collection unit filters and collects environmental sounds (wind, rain, etc.) in the stadium when collecting audio data. For example, the collection unit filters out the sound of wind to clearly collect the cheers of spectators. The collection unit can also remove the sound of rain to clearly collect the sounds of the competition. Furthermore, the collection unit can filter out ambient noise to collect important audio data. In this way, by filtering the environmental sounds, clear audio data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0038] When collecting audio data, the collection unit can prioritize collecting audio from specific sections within the stadium. For example, when collecting audio data, the collection unit prioritizes collecting audio from specific sections within the stadium (VIP seats, general seats, etc.). For example, the collection unit prioritizes collecting audio data from VIP seats to analyze the reactions of specific spectators in detail. The collection unit can also prioritize collecting audio data from general seats to analyze the overall excitement. Furthermore, the collection unit can prioritize collecting audio data from behind the goal to analyze differences in cheering. In this way, by collecting audio from specific sections preferentially, the reactions of specific spectators can be analyzed in detail. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI.

[0039] The collection unit can synchronize audio and video in conjunction with a video camera in the stadium when collecting audio data. For example, the collection unit synchronizes audio and video in conjunction with a video camera in the stadium when collecting audio data. For example, the collection unit synchronizes video and audio data from the video camera to analyze spectator reactions in real time. The collection unit can also synchronize and collect video and audio at important play moments. Furthermore, the collection unit can synchronize spectator movements with audio to perform detailed analysis. In this way, by synchronizing audio and video, spectator reactions can be analyzed in real time. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0040] The collection unit can use a sensor to detect the movements of spectators in the stadium when collecting audio data, and adjust the audio to be collected based on the movements. For example, the collection unit can use a sensor to detect the movements of spectators in the stadium when collecting audio data, and adjust the audio to be collected based on the movements. For example, the collection unit collects audio data to capture the peak of cheers the moment a spectator stands up. The collection unit can also adjust the sensitivity of the microphone to collect quieter audio the moment a spectator sits down. Furthermore, the collection unit can adjust the priority of the audio data to be collected according to the movements of the spectators. In this way, by adjusting the audio data according to the movements of the spectators, more realistic audio data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The generation unit can adjust the volume and tone of the collected audio data during generation to reproduce realistic audio. For example, the generation unit adjusts the volume and tone of the collected audio data during generation to reproduce realistic audio. For example, the generation unit adjusts the volume of the collected audio data to reproduce realistic cheers. The generation unit can also adjust the tone of the collected audio data to reproduce realistic audio. Furthermore, the generation unit can adjust the balance of the collected audio data to reproduce natural audio. In this way, realistic audio can be reproduced by adjusting the volume and tone. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input collected audio data to the generation AI and cause the generation AI to execute processing to reproduce realistic audio by adjusting the volume and tone.

[0042] The generation unit can customize the voice generation algorithm according to the characteristics of different sports during generation. For example, the generation unit customizes the voice generation algorithm according to the characteristics of different sports during generation. For example, in a soccer game, the generation unit uses an algorithm that emphasizes cheers at the moment a goal is scored. In a basketball game, the generation unit can also use an algorithm that emphasizes cheers at the moment a shot is made. Furthermore, in a tennis game, the generation unit can use an algorithm that reproduces quiet moments and cheering moments in a balanced manner. In this way, by customizing the voice generation algorithm according to the characteristics of the sport, voices suitable for each sport can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute voice generation algorithms according to the characteristics of different sports.

[0043] The generation unit can improve the accuracy of generation by referring to past voice data during generation. For example, the generation unit can improve the accuracy of generation by referring to past voice data during generation. For example, the generation unit can refer to voice data from past matches to recreate realistic cheers. The generation unit can also refer to voice data from past events to recreate realistic voices. Furthermore, the generation unit can analyze past voice data and use an optimal voice generation algorithm. In this way, the accuracy of generation can be improved by referring to past voice data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past voice data into the generation AI and cause the generation AI to execute processing to improve the accuracy of generation.

[0044] The generation unit can change the characteristics of the generated audio based on the time zone of the collected audio data at the time of generation. For example, the generation unit can change the characteristics of the generated audio based on the time zone of the collected audio data at the time of generation. For example, the generation unit can generate bright audio for daytime matches. The generation unit can also generate calm audio for nighttime matches. Furthermore, the generation unit can adjust the tone and effects of the audio to suit a specific time zone. In this way, by changing the characteristics of the audio based on the time zone, more realistic audio can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute processing to change the characteristics of the generated audio based on the time zone of the collected audio data.

[0045] The generation unit can adjust the order of the sounds to be generated based on the relevance of the collected audio data during generation. The generation unit, for example, adjusts the order of the sounds to be generated based on the relevance of the collected audio data during generation. For example, the generation unit prioritizes generating audio for important events. Furthermore, if the audience cheers continuously, the generation unit can generate the audio while maintaining that order. Furthermore, the generation unit can generate audio in a natural order based on the relevance of the audio data. In this way, natural audio can be generated by adjusting the order based on the relevance of the audio data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute processing to adjust the order of the sounds to be generated based on the relevance of the collected audio data.

[0046] The generation unit can customize the sound to be generated in accordance with a specific event in the stadium during generation. For example, the generation unit customizes the sound to be generated in accordance with a specific event in the stadium (halftime, end of the game, etc.) during generation. For example, the generation unit generates a sound that combines cheers from the audience with music at halftime. The generation unit can also generate cheers and applause at the end of the game. Furthermore, the generation unit can customize the content of the sound to match a specific event. In this way, by customizing the sound in accordance with a specific event, more realistic sound can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to perform processing to customize the sound to be generated in accordance with a specific event in the stadium.

[0047] The providing unit can adjust the audio format according to the device of the remote spectator when providing the audio. For example, the providing unit adjusts the audio format according to the device (smartphone, tablet, etc.) of the remote spectator when providing the audio. For example, when viewing on a smartphone, the providing unit adjusts the audio bit rate and provides the audio. Furthermore, when viewing on a tablet, the providing unit can also provide high-quality audio. Furthermore, the providing unit can select and provide the optimal audio format according to the type of device. In this way, the optimal audio can be provided by adjusting the audio format according to the device. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The providing unit can customize the content of the audio to be provided by referring to the past viewing history of the remote spectator when providing the audio. For example, the providing unit customizes the content of the audio to be provided by referring to the past viewing history of the remote spectator when providing the audio. For example, the providing unit provides audio tailored to the preferences of the remote spectator by referring to audio data of games viewed in the past. The providing unit can also preferentially provide audio of a specific team or player based on the past viewing history. Furthermore, the providing unit can analyze the viewing history and provide audio that is likely to interest the remote spectator. In this way, by referring to the past viewing history, audio tailored to the preferences of the remote spectator can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0049] The providing unit can adjust the quality of the audio according to the network conditions of the remote spectators when providing the audio. For example, the providing unit adjusts the quality of the audio according to the network conditions of the remote spectators when providing the audio. For example, if the network is unstable, the providing unit reduces the audio bit rate before providing the audio. Furthermore, the providing unit can also provide high-quality audio in a high-speed network environment. Furthermore, the providing unit can adjust the compression rate of the audio before providing the audio according to the network conditions. In this way, stable audio can be provided by adjusting the audio quality according to the network conditions. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0050] The providing unit can provide highly relevant audio by taking into account the geographical location information of the remote spectator when providing the audio. For example, the providing unit can provide highly relevant audio by taking into account the geographical location information of the remote spectator when providing the audio. For example, if the remote spectator is in a specific area, the providing unit can provide audio related to that area. The providing unit can also provide audio related to events in the area based on the location information of the remote spectator. Furthermore, the providing unit can also provide audio that is likely to interest the remote spectator by taking into account the geographical location information. In this way, highly relevant audio can be provided to the remote spectator by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0051] The providing unit can analyze the social media activity of the remote spectator at the time of providing and provide the related audio. For example, the providing unit can analyze the social media activity of the remote spectator at the time of providing and provide the related audio. For example, the providing unit can provide audio related to a location where the remote spectator checked in on social media. The providing unit can also analyze the content of posts on social media and provide the related audio. Furthermore, the providing unit can also provide the related audio by referring to the activities of friends on social media. In this way, by analyzing social media activity, it is possible to provide the related audio to the remote spectator. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0052] The providing unit can customize the content of the audio to be provided by reflecting feedback from the remote spectators when providing the audio. For example, the providing unit customizes the content of the audio to be provided by reflecting feedback from the remote spectators when providing the audio. For example, the providing unit adjusts the content of the audio based on the feedback from the remote spectators and provides it. The providing unit can also analyze the feedback and provide audio that the remote spectators prefer. Furthermore, the providing unit can adjust the effects and tone of the audio by reflecting the opinions of the remote spectators. In this way, by reflecting the feedback, it is possible to provide audio that suits the preferences of the remote spectators. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0053] The remote cheering bat can suggest the optimal input method when inputting cheering bat information by referring to the past cheering history of the remote spectator. For example, when inputting cheering bat information by referring to the past cheering history of the remote spectator, the remote cheering bat suggests the optimal input method. For example, the remote cheering bat automatically displays cheering voices and sounds used in the past as candidates. The remote cheering bat can also predict and suggest cheering voices and sounds to be used at a specific time period from the past cheering history. Furthermore, the remote cheering bat can analyze the past cheering history and suggest the optimal input method. In this way, by referring to the past cheering history, the optimal input method can be suggested to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0054] The remote cheering bat can customize the input method according to the current situation of the remote spectator when inputting cheering bat. For example, the remote cheering bat customizes the input method according to the current situation of the remote spectator (at home, away from home, etc.) when inputting cheering bat. For example, the remote cheering bat provides detailed input options when the spectator is at home. Furthermore, the remote cheering bat can also enable the spectator to input cheering voices with simple operations when the spectator is away from home. Furthermore, the remote cheering bat can suggest the optimal input method according to the current situation. In this way, by customizing the input method according to the current situation, the optimal input method can be provided to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0055] The remote cheering bat can customize the input content by taking into account the geographical location information of the remote spectator when inputting the cheering bat. For example, the remote cheering bat customizes the input content by taking into account the geographical location information of the remote spectator when inputting the cheering bat. For example, if the remote spectator is in a specific area, the remote cheering bat can provide cheering messages related to that area. The remote cheering bat can also provide cheering messages related to local events based on the geographical location information. Furthermore, the remote cheering bat can also provide cheering messages that the remote spectator is likely to be interested in by taking into account the geographical location information. In this way, by taking into account the geographical location information, it is possible to provide cheering messages that are highly relevant to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0056] The remote cheering bat can analyze the social media activity of the remote spectator when the cheering bat is input and suggest relevant cheering content. For example, the remote cheering bat can analyze the social media activity of the remote spectator when the cheering bat is input and suggest relevant cheering content. For example, the remote cheering bat can provide cheering messages related to locations where the remote spectator has checked in on social media. The remote cheering bat can also analyze social media posts and provide relevant cheering messages. Furthermore, the remote cheering bat can provide relevant cheering messages by referring to the activities of friends on social media. In this way, by analyzing social media activity, relevant cheering messages can be provided to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0057] The transmitting unit can customize the transmission content by referring to the past transmission history of the remote spectator at the time of transmission. For example, the transmitting unit customizes the transmission content by referring to the past transmission history of the remote spectator at the time of transmission. For example, the transmitting unit transmits audio tailored to the preferences of the remote spectator by referring to cheering voices and sounds transmitted in the past. The transmitting unit can also predict and transmit cheering voices and sounds to be used in a specific time period from the past transmission history. Furthermore, the transmitting unit can analyze the past transmission history and suggest optimal transmission content. In this way, audio tailored to the preferences of the remote spectator can be transmitted by referring to the past transmission history. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI.

[0058] The transmitting unit can adjust the quality of the transmission data during transmission according to the network conditions of the remote spectators. For example, the transmitting unit adjusts the quality of the transmission data during transmission according to the network conditions of the remote spectators. For example, if the network is unstable, the transmitting unit reduces the bit rate of the audio data before transmitting. Furthermore, in a high-speed network environment, the transmitting unit can also transmit high-quality audio data. Furthermore, the transmitting unit can adjust the compression rate of the audio data before transmitting according to the network conditions. In this way, stable audio can be transmitted by adjusting the quality of the transmission data according to the network conditions. Some or all of the above-mentioned processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI.

[0059] The transmitting unit can select the optimal transmission method at the time of transmission, taking into consideration device information of the remote spectator. For example, the transmitting unit selects the optimal transmission method at the time of transmission, taking into consideration device information of the remote spectator. For example, when viewing on a smartphone, the transmitting unit adjusts the bit rate of the audio data before transmitting. Furthermore, when viewing on a tablet, the transmitting unit can also transmit high-quality audio data. Furthermore, the transmitting unit can select and transmit the optimal audio data format depending on the type of device. In this way, by taking into consideration the device information, optimal audio data can be transmitted to the remote spectator. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The transmitting unit can customize the transmission content at the time of transmission by taking into account the geographical location information of the remote spectator. For example, the transmitting unit customizes the transmission content at the time of transmission by taking into account the geographical location information of the remote spectator. For example, if the remote spectator is in a specific area, the transmitting unit transmits audio related to that area. The transmitting unit can also transmit audio related to events in the area based on the geographical location information. Furthermore, the transmitting unit can transmit audio that is likely to interest the remote spectator by taking into account the geographical location information. In this way, audio that is highly relevant to the remote spectator can be transmitted by taking into account the geographical location information. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI or without using AI.

[0061] The transmitting unit can analyze the social media activity of the remote spectator at the time of transmission and suggest related content to transmit. For example, the transmitting unit analyzes the social media activity of the remote spectator at the time of transmission and suggest related content to transmit. For example, the transmitting unit transmits audio related to a location where the remote spectator has checked in on social media. The transmitting unit can also analyze the content posted on social media and transmit related audio. Furthermore, the transmitting unit can also transmit related audio with reference to the activity of friends on social media. In this way, audio related to the remote spectator can be transmitted by analyzing social media activity. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI or without using AI.

[0062] The transmitting unit can customize the content of the audio data to be transmitted by reflecting feedback from the remote spectators at the time of transmission. For example, the transmitting unit customizes the content of the audio data to be transmitted by reflecting feedback from the remote spectators at the time of transmission. For example, the transmitting unit adjusts the content of the audio data based on the feedback from the remote spectators and transmits the adjusted content. The transmitting unit can also analyze the feedback and transmit audio data preferred by the remote spectators. Furthermore, the transmitting unit can adjust the effects and tone of the audio data by reflecting the opinions of the remote spectators. In this way, by reflecting the feedback, audio that suits the preferences of the remote spectators can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can also use sensors to detect the movements of spectators in the stadium and adjust the audio to be collected based on their movements. For example, it can collect audio data to capture the peak of cheers the moment spectators stand up. It can also adjust the sensitivity of the microphone to collect quieter audio the moment spectators sit down. It can also adjust the priority of the audio data to be collected according to spectator movements. This allows for the collection of more realistic audio data by adjusting the audio data according to spectator movements.

[0065] The generator can also change the characteristics of the generated audio based on the time of day of the collected audio data. For example, it can generate bright audio for daytime matches and calm audio for nighttime matches. Furthermore, it can adjust the tone and effects of the audio to suit specific time periods. This allows for more realistic audio to be generated by changing the characteristics of the audio based on the time period.

[0066] When providing audio, the providing unit can provide audio that is highly relevant by taking into account the geographical location information of the remote spectator. For example, if the remote spectator is in a specific area, audio related to that area can be provided. Audio related to local events can also be provided based on the location information of the remote spectator. Furthermore, audio that is likely to interest the remote spectator can be provided by taking into account the geographical location information. In this way, audio that is highly relevant to the remote spectator can be provided by taking into account the geographical location information.

[0067] The remote cheering bat can also analyze the social media activity of remote spectators when they input their cheering information and suggest relevant cheering messages. For example, it can provide cheering messages related to the places where the remote spectator checked in on social media. It can also analyze social media posts to provide relevant cheering messages. It can also provide relevant cheering messages based on the activities of friends on social media. In this way, it is possible to provide relevant cheering messages to remote spectators by analyzing social media activity.

[0068] The transmitting unit can also customize the content of the audio data to be transmitted by reflecting feedback from the remote spectators at the time of transmission. For example, the transmitting unit can adjust the content of the audio data based on the feedback from the remote spectators and transmit it. The transmitting unit can also analyze the feedback and transmit audio data that the remote spectators prefer. Furthermore, the transmitting unit can adjust the effects and tone of the audio data by reflecting the opinions of the remote spectators. In this way, by reflecting the feedback, audio that suits the preferences of the remote spectators can be transmitted.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit collects audio data from the stadium. For example, the collection unit can collect audio data using multiple microphones installed in the stadium. The collection unit can also estimate the emotions of spectators and adjust the collection timing based on the emotions in order to adjust the timing of collecting the audio data. Step 2: The generator uses a generation AI to analyze the audio data collected by the collector and generate audio that reproduces the cheers and cheers of the stadium, the sounds of the competition, and other sounds. The generator can, for example, adjust the volume and tone of the collected audio data to reproduce realistic audio. The generator can also customize the audio generation algorithm according to the characteristics of different sports. Step 3: The providing unit provides the audio generated by the generating unit to the remote spectator. For example, the providing unit can provide the generated audio to the remote spectator via the Internet. The providing unit can also adjust the format of the audio depending on the device of the remote spectator. Step 4: The remote cheering device is a device for remote spectators to input cheering voices or sounds. The remote cheering device can, for example, estimate the emotions of the remote spectators and adjust the input method of cheering bats based on the emotions. Step 5: The transmitting unit transmits the audio data input by the remote cheering device to the stadium and plays it from the speakers in the stadium. The transmitting unit can, for example, estimate the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the emotions.

[0071] (Example 2) A system according to an embodiment of the present invention collects and generates stadium audio data and provides it to remote spectators. This system collects stadium audio data, analyzes it using a generation AI, generates audio that reproduces the cheers and excitement of the stadium, and provides it to the remote spectators. Furthermore, remote spectators can use remote cheering devices to participate in the excitement of the venue and have that audio played over the stadium's speakers. For example, multiple microphones installed in the stadium collect audio data in real time, and the generation AI analyzes the audio data to generate audio that reproduces the cheers and cheers of the spectators, the sounds of the competition, and other sounds. The generated audio is then provided to the remote spectators via the Internet. The remote spectators can listen to the live sounds of the stadium in real time from their homes or other locations. Furthermore, when remote spectators input cheering voices and sounds using the remote cheering device, the audio data is transmitted to the stadium via the Internet and played over the stadium's speakers. This allows remote spectators to share the atmosphere of the stadium and feel a sense of unity. This system allows remote spectators to share the atmosphere of the stadium and feel a sense of unity. For example, remote spectators can cheer from home, joining in with the spectators in the stadium. Also, by listening to the cheers and excitement from the stadium in real time, remote spectators can feel like they are actually at the stadium.

[0072] A stadium sound reproduction system according to an embodiment includes a collection unit, a generation unit, a provision unit, a remote cheering device, and a transmission unit. The collection unit collects stadium sound data. For example, the collection unit can collect the sound data using multiple microphones installed in the stadium. The collection unit can also estimate the emotions of spectators and adjust the collection timing based on the emotions in order to adjust the timing of collecting the sound data. The generation unit uses a generation AI to analyze the sound data collected by the collection unit and generate sound that reproduces cheers and cheers from the stadium, the sounds of the competition, and the like. For example, the generation unit can adjust the volume and tone of the collected sound data to reproduce realistic sound. The generation unit can also customize the sound generation algorithm according to the characteristics of different competitions. The provision unit provides the sound generated by the generation unit to a remote spectator. For example, the provision unit can provide the generated sound to the remote spectator via the Internet. The provision unit can also adjust the format of the sound depending on the device of the remote spectator. The remote cheering device is a device through which the remote spectator inputs cheering voices and sounds. The remote cheering device can, for example, estimate the emotions of the remote spectators and adjust the input method of the cheering bat based on the emotions. The transmission unit transmits the audio data input by the remote cheering device to the stadium and plays it from speakers in the stadium. The transmission unit can, for example, estimate the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the emotions. In this way, the stadium audio reproduction system according to the embodiment allows remote spectators to share the atmosphere of the stadium and feel a sense of unity.

[0073] The collection unit can collect audio data using multiple microphones installed within the stadium. The collection unit collects audio data using, for example, multiple microphones installed within the stadium. The multiple microphones may include, for example, condenser microphones, dynamic microphones, etc. The collection unit can also collect audio data from different areas within the stadium and analyze the audio characteristics of each area. The collection unit can also improve the accuracy of collection based on specific events (goals, points, etc.) when collecting audio data. Furthermore, the collection unit can filter and collect environmental sounds (wind, rain, etc.) within the stadium when collecting audio data. In this way, by using multiple microphones, audio data within the stadium can be collected over a wide area. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The generation unit can analyze the collected audio data and generate audio that reproduces the cheers of spectators, cheers, and sounds of the competition. The generation unit, for example, analyzes the collected audio data and generates audio that reproduces the cheers of spectators, cheers, and sounds of the competition. The generation unit, for example, adjusts the volume of the collected audio data to reproduce realistic cheers. The generation unit can also adjust the tone of the collected audio data to reproduce realistic sounds. Furthermore, the generation unit can adjust the balance of the collected audio data to reproduce natural sounds. In this way, by analyzing the collected audio data, realistic sounds of a stadium can be reproduced. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected audio data to the generation AI and cause the generation AI to generate audio that reproduces the cheers of spectators, cheers, and sounds of the competition.

[0075] The providing unit can provide the generated audio to the remote spectators via the Internet. For example, the providing unit can provide the generated audio to the remote spectators via the Internet. The providing unit can provide the audio using a communication method such as HTTP, WebSocket, or a streaming protocol. The providing unit can also adjust the audio format depending on the device of the remote spectator. For example, when viewing on a smartphone, the audio bitrate can be adjusted and provided. Also, when viewing on a tablet, high-quality audio can be provided. Furthermore, the providing unit can adjust the audio quality depending on the network conditions of the remote spectator. For example, when the network is unstable, the audio bitrate can be lowered and provided. In this way, by providing the audio via the Internet, the remote spectators can listen to the audio from the stadium in real time. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0076] The remote cheering bat is a device that allows remote spectators to input cheering voices and sounds. The remote cheering bat is, for example, a device that allows remote spectators to input cheering voices and sounds. The remote cheering bat includes, for example, a voice input function, a vibration function, and a light function. The remote cheering bat can, for example, estimate the emotions of the remote spectators and adjust the cheering bat input method based on the emotions. For example, if the remote spectator is excited, the remote cheering bat can allow them to input cheering voices with simple operations. Also, if the remote spectator is relaxed, the remote cheering bat can provide detailed input options. Furthermore, the remote cheering bat can suggest the optimal input method by referring to the remote spectator's past cheering history. For example, previously used cheering voices and sounds can be automatically displayed as candidates. This allows remote spectators to participate in livening up the stadium by using the remote cheering bat. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without AI.

[0077] The transmitting unit can transmit audio data input by the remote cheering bat to the stadium via the Internet and play it through the stadium's speakers. For example, the transmitting unit transmits audio data input by the remote cheering bat to the stadium via the Internet and play it through the stadium's speakers. The transmitting unit can transmit the audio data using a communication method such as HTTP, WebSocket, or streaming protocol. The transmitting unit can also estimate the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the emotions. For example, if a remote spectator is excited, the transmitting unit can prioritize transmitting the cheering voices of that remote spectator. Also, if a remote spectator becomes quiet, the transmitting unit can prioritize transmitting the cheering voices of other spectators. Furthermore, the transmitting unit can adjust the quality of the transmitted data depending on the remote spectator's network conditions. For example, if the network is unstable, the transmitting unit can lower the bit rate of the audio data before transmitting it. This allows the remote spectators to participate in the excitement of the stadium by transmitting the cheering voices of the remote spectators to the stadium and playing them through the speakers. Some or all of the above-described processing by the transmitting unit can be performed using, for example, AI, or without AI.

[0078] The collection unit can analyze the emotions of the spectators and adjust the timing of collecting audio data based on the analyzed emotions. For example, the collection unit can analyze the emotions of the spectators and adjust the timing of collecting audio data based on the analyzed emotions. For example, if the spectators are excited, the collection unit collects audio data at the moment when the cheers get louder. The collection unit can also detect the moment when the spectators become quiet and clearly collect audio from important scenes. Furthermore, the collection unit can increase collection of audio data in accordance with events that heighten the emotions of the spectators (e.g., goals, points scored, etc.). In this way, by adjusting the timing of collecting audio data according to the emotions of the spectators, more realistic audio data can be collected. The estimation of the emotions of the spectators is achieved 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. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI.

[0079] The collection unit can collect audio data from different areas within the stadium and analyze the audio characteristics of each area. For example, the collection unit collects audio data from different areas within the stadium and analyzes the audio characteristics of each area. For example, the collection unit collects audio data from VIP seats in the stadium and analyzes the reactions of specific spectators. The collection unit can also collect audio data from general seating in the stadium and analyze the overall excitement. Furthermore, the collection unit can collect audio data from specific sections of the stadium (such as behind the goal or near the bench) and analyze the differences in cheering between areas. In this way, by collecting and analyzing audio data from different areas, it is possible to understand the differences in cheering between areas. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI.

[0080] The collection unit can improve the accuracy of collection based on a specific event (e.g., a goal, a score, etc.) when collecting audio data. For example, the collection unit improves the accuracy of collection based on a specific event (e.g., a goal, a score, etc.) when collecting audio data. For example, the collection unit collects highly accurate audio data to capture the peak of cheers at the moment a goal is scored. The collection unit can also adjust the sensitivity of the microphone to collect detailed information about the spectators' reactions when a goal is scored. Furthermore, the collection unit can perform noise filtering to clearly collect surrounding audio at the moment an important play is made. In this way, by improving the accuracy of collection based on a specific event, audio data at important moments can be collected with high accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0081] The collection unit can filter and collect environmental sounds (wind, rain, etc.) in the stadium when collecting audio data. For example, the collection unit filters and collects environmental sounds (wind, rain, etc.) in the stadium when collecting audio data. For example, the collection unit filters out the sound of wind to clearly collect the cheers of spectators. The collection unit can also remove the sound of rain to clearly collect the sounds of the competition. Furthermore, the collection unit can filter out ambient noise to collect important audio data. In this way, by filtering the environmental sounds, clear audio data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The collection unit can estimate the emotions of the audience and determine the priority of the audio data to be collected based on the estimated emotions. For example, the collection unit estimates the emotions of the audience and determines the priority of the audio data to be collected based on the estimated emotions. For example, if the audience is excited, the collection unit prioritizes collecting audio data from that area. The collection unit can also postpone audio data from areas where the audience has quieted down and prioritize audio from areas where the audience is excited. Furthermore, the collection unit can prioritize collecting audio data from specific areas in accordance with events where the audience's emotions are heightened. In this way, by determining the priority of audio data based on the audience's emotions, important audio data can be collected preferentially. The estimation of audience emotions 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. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI.

[0083] When collecting audio data, the collection unit can prioritize collecting audio from specific sections within the stadium. For example, when collecting audio data, the collection unit prioritizes collecting audio from specific sections within the stadium (VIP seats, general seats, etc.). For example, the collection unit prioritizes collecting audio data from VIP seats to analyze the reactions of specific spectators in detail. The collection unit can also prioritize collecting audio data from general seats to analyze the overall excitement. Furthermore, the collection unit can prioritize collecting audio data from behind the goal to analyze differences in cheering. In this way, by collecting audio from specific sections preferentially, the reactions of specific spectators can be analyzed in detail. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI.

[0084] The collection unit can synchronize audio and video in conjunction with a video camera in the stadium when collecting audio data. For example, the collection unit synchronizes audio and video in conjunction with a video camera in the stadium when collecting audio data. For example, the collection unit synchronizes video and audio data from the video camera to analyze spectator reactions in real time. The collection unit can also synchronize and collect video and audio at important play moments. Furthermore, the collection unit can synchronize spectator movements with audio to perform detailed analysis. In this way, by synchronizing audio and video, spectator reactions can be analyzed in real time. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0085] The collection unit can use a sensor to detect the movements of spectators in the stadium when collecting audio data, and adjust the audio to be collected based on the movements. For example, the collection unit can use a sensor to detect the movements of spectators in the stadium when collecting audio data, and adjust the audio to be collected based on the movements. For example, the collection unit collects audio data to capture the peak of cheers the moment a spectator stands up. The collection unit can also adjust the sensitivity of the microphone to collect quieter audio the moment a spectator sits down. Furthermore, the collection unit can adjust the priority of the audio data to be collected according to the movements of the spectators. In this way, by adjusting the audio data according to the movements of the spectators, more realistic audio data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The generation unit can analyze the emotions of the audience and adjust the expression method of the generated audio based on the analyzed emotions. For example, the generation unit can analyze the emotions of the audience and adjust the expression method of the generated audio based on the analyzed emotions. For example, if the audience is excited, the generation unit can increase the volume of cheers when generating the audio. The generation unit can also calm the tone of the audio when the audience becomes quiet. Furthermore, the generation unit can adjust the audio effects to match an event in which the audience's emotions are heightened. In this way, by adjusting the expression method of the audio based on the audience's emotions, more realistic audio can be generated. The estimation of the audience's emotions 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. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI.

[0087] The generation unit can adjust the volume and tone of the collected audio data during generation to reproduce realistic audio. For example, the generation unit adjusts the volume and tone of the collected audio data during generation to reproduce realistic audio. For example, the generation unit adjusts the volume of the collected audio data to reproduce realistic cheers. The generation unit can also adjust the tone of the collected audio data to reproduce realistic audio. Furthermore, the generation unit can adjust the balance of the collected audio data to reproduce natural audio. In this way, realistic audio can be reproduced by adjusting the volume and tone. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input collected audio data to the generation AI and cause the generation AI to execute processing to reproduce realistic audio by adjusting the volume and tone.

[0088] The generation unit can customize the voice generation algorithm according to the characteristics of different sports during generation. For example, the generation unit customizes the voice generation algorithm according to the characteristics of different sports during generation. For example, in a soccer game, the generation unit uses an algorithm that emphasizes cheers at the moment a goal is scored. In a basketball game, the generation unit can also use an algorithm that emphasizes cheers at the moment a shot is made. Furthermore, in a tennis game, the generation unit can use an algorithm that reproduces quiet moments and cheering moments in a balanced manner. In this way, by customizing the voice generation algorithm according to the characteristics of the sport, voices suitable for each sport can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute voice generation algorithms according to the characteristics of different sports.

[0089] The generation unit can improve the accuracy of generation by referring to past voice data during generation. For example, the generation unit can improve the accuracy of generation by referring to past voice data during generation. For example, the generation unit can refer to voice data from past matches to recreate realistic cheers. The generation unit can also refer to voice data from past events to recreate realistic voices. Furthermore, the generation unit can analyze past voice data and use an optimal voice generation algorithm. In this way, the accuracy of generation can be improved by referring to past voice data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past voice data into the generation AI and cause the generation AI to execute processing to improve the accuracy of generation.

[0090] The generation unit can analyze the emotions of the audience and adjust the length of the generated audio based on the analyzed emotions. For example, the generation unit can analyze the emotions of the audience and adjust the length of the generated audio based on the analyzed emotions. For example, if the audience is excited, the generation unit can generate longer cheering audio. The generation unit can also shorten the length of the audio if the audience becomes quiet. Furthermore, the generation unit can adjust the length of the audio to match an event that heightens the audience's emotions. In this way, by adjusting the length of the audio based on the audience's emotions, more realistic audio can be generated. The estimation of the audience's emotions 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. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI.

[0091] The generation unit can change the characteristics of the generated audio based on the time zone of the collected audio data at the time of generation. For example, the generation unit can change the characteristics of the generated audio based on the time zone of the collected audio data at the time of generation. For example, the generation unit can generate bright audio for daytime matches. The generation unit can also generate calm audio for nighttime matches. Furthermore, the generation unit can adjust the tone and effects of the audio to suit a specific time zone. In this way, by changing the characteristics of the audio based on the time zone, more realistic audio can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute processing to change the characteristics of the generated audio based on the time zone of the collected audio data.

[0092] The generation unit can adjust the order of the sounds to be generated based on the relevance of the collected audio data during generation. The generation unit, for example, adjusts the order of the sounds to be generated based on the relevance of the collected audio data during generation. For example, the generation unit prioritizes generating audio for important events. Furthermore, if the audience cheers continuously, the generation unit can generate the audio while maintaining that order. Furthermore, the generation unit can generate audio in a natural order based on the relevance of the audio data. In this way, natural audio can be generated by adjusting the order based on the relevance of the audio data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to execute processing to adjust the order of the sounds to be generated based on the relevance of the collected audio data.

[0093] The generation unit can customize the sound to be generated in accordance with a specific event in the stadium during generation. For example, the generation unit customizes the sound to be generated in accordance with a specific event in the stadium (halftime, end of the game, etc.) during generation. For example, the generation unit generates a sound that combines cheers from the audience with music at halftime. The generation unit can also generate cheers and applause at the end of the game. Furthermore, the generation unit can customize the content of the sound to match a specific event. In this way, by customizing the sound in accordance with a specific event, more realistic sound can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to perform processing to customize the sound to be generated in accordance with a specific event in the stadium.

[0094] The providing unit can analyze the emotions of the spectators and adjust the audio presentation method to be provided based on the analyzed emotions of the spectators. For example, the providing unit can analyze the emotions of the spectators and adjust the audio presentation method to be provided based on the analyzed emotions of the spectators. For example, if the spectators are excited, the providing unit can increase the volume of cheers when providing the audio. Furthermore, if the spectators become quiet, the providing unit can also provide a calmer tone of the audio. Furthermore, the providing unit can adjust the audio effects to match an event that heightens the emotions of the spectators. In this way, by adjusting the audio presentation method based on the emotions of the spectators, more realistic audio can be provided to the remote spectators. 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. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI.

[0095] The providing unit can adjust the audio format according to the device of the remote spectator when providing the audio. For example, the providing unit adjusts the audio format according to the device (smartphone, tablet, etc.) of the remote spectator when providing the audio. For example, when viewing on a smartphone, the providing unit adjusts the audio bit rate and provides the audio. Furthermore, when viewing on a tablet, the providing unit can also provide high-quality audio. Furthermore, the providing unit can select and provide the optimal audio format according to the type of device. In this way, the optimal audio can be provided by adjusting the audio format according to the device. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0096] The providing unit can customize the content of the audio to be provided by referring to the past viewing history of the remote spectator when providing the audio. For example, the providing unit customizes the content of the audio to be provided by referring to the past viewing history of the remote spectator when providing the audio. For example, the providing unit provides audio tailored to the preferences of the remote spectator by referring to audio data of games viewed in the past. The providing unit can also preferentially provide audio of a specific team or player based on the past viewing history. Furthermore, the providing unit can analyze the viewing history and provide audio that is likely to interest the remote spectator. In this way, by referring to the past viewing history, audio tailored to the preferences of the remote spectator can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0097] The providing unit can adjust the quality of the audio according to the network conditions of the remote spectators when providing the audio. For example, the providing unit adjusts the quality of the audio according to the network conditions of the remote spectators when providing the audio. For example, if the network is unstable, the providing unit reduces the audio bit rate before providing the audio. Furthermore, the providing unit can also provide high-quality audio in a high-speed network environment. Furthermore, the providing unit can adjust the compression rate of the audio before providing the audio according to the network conditions. In this way, stable audio can be provided by adjusting the audio quality according to the network conditions. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0098] The providing unit can analyze the emotions of the spectators and adjust the length of the audio to be provided based on the analyzed emotions of the spectators. For example, the providing unit can analyze the emotions of the spectators and adjust the length of the audio to be provided based on the analyzed emotions of the spectators. For example, if the spectators are excited, the providing unit can provide longer cheering audio. Furthermore, if the spectators become quieter, the providing unit can shorten the length of the audio to be provided. Furthermore, the providing unit can adjust the length of the audio to match an event that increases the emotions of the spectators. In this way, by adjusting the length of the audio based on the emotions of the spectators, more realistic audio can be provided to the remote spectators. 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. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI.

[0099] The providing unit can provide highly relevant audio by taking into account the geographical location information of the remote spectator when providing the audio. For example, the providing unit can provide highly relevant audio by taking into account the geographical location information of the remote spectator when providing the audio. For example, if the remote spectator is in a specific area, the providing unit can provide audio related to that area. The providing unit can also provide audio related to events in the area based on the location information of the remote spectator. Furthermore, the providing unit can also provide audio that is likely to interest the remote spectator by taking into account the geographical location information. In this way, highly relevant audio can be provided to the remote spectator by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0100] The providing unit can analyze the social media activity of the remote spectator at the time of providing and provide the related audio. For example, the providing unit can analyze the social media activity of the remote spectator at the time of providing and provide the related audio. For example, the providing unit can provide audio related to a location where the remote spectator checked in on social media. The providing unit can also analyze the content of posts on social media and provide the related audio. Furthermore, the providing unit can also provide the related audio by referring to the activities of friends on social media. In this way, by analyzing social media activity, it is possible to provide the related audio to the remote spectator. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0101] The providing unit can customize the content of the audio to be provided by reflecting feedback from the remote spectators when providing the audio. For example, the providing unit customizes the content of the audio to be provided by reflecting feedback from the remote spectators when providing the audio. For example, the providing unit adjusts the content of the audio based on the feedback from the remote spectators and provides it. The providing unit can also analyze the feedback and provide audio that the remote spectators prefer. Furthermore, the providing unit can adjust the effects and tone of the audio by reflecting the opinions of the remote spectators. In this way, by reflecting the feedback, it is possible to provide audio that suits the preferences of the remote spectators. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0102] The remote cheering bat can analyze the emotions of the remote spectators and adjust the input method of the cheering bat based on the analyzed emotions. For example, the remote cheering bat can analyze the emotions of the remote spectators and adjust the input method of the cheering bat based on the analyzed emotions. For example, if the remote cheering bat is excited, the remote cheering bat allows the remote spectators to input cheering voices with simple operations. The remote cheering bat can also provide detailed input options if the remote spectators are relaxed. Furthermore, if the remote cheering bat is in a hurry, the remote cheering bat can prioritize voice input and allow the remote spectators to quickly input cheering voices. This allows more appropriate cheering voices to be input by adjusting the input method based on the emotions of the remote spectators. The estimation of the emotions of the remote 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. Some or all of the above-described processing in the remote cheering bat can be performed, for example, using AI or without AI.

[0103] The remote cheering bat can suggest the optimal input method when inputting cheering bat information by referring to the past cheering history of the remote spectator. For example, when inputting cheering bat information by referring to the past cheering history of the remote spectator, the remote cheering bat suggests the optimal input method. For example, the remote cheering bat automatically displays cheering voices and sounds used in the past as candidates. The remote cheering bat can also predict and suggest cheering voices and sounds to be used at a specific time period from the past cheering history. Furthermore, the remote cheering bat can analyze the past cheering history and suggest the optimal input method. In this way, by referring to the past cheering history, the optimal input method can be suggested to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0104] The remote cheering bat can customize the input method according to the current situation of the remote spectator when inputting cheering bat. For example, the remote cheering bat customizes the input method according to the current situation of the remote spectator (at home, away from home, etc.) when inputting cheering bat. For example, the remote cheering bat provides detailed input options when the spectator is at home. Furthermore, the remote cheering bat can also enable the spectator to input cheering voices with simple operations when the spectator is away from home. Furthermore, the remote cheering bat can suggest the optimal input method according to the current situation. In this way, by customizing the input method according to the current situation, the optimal input method can be provided to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0105] The remote cheering bat can analyze the emotions of the remote spectators and adjust the input content of the cheering bat based on the analyzed emotions. For example, the remote cheering bat can analyze the emotions of the remote spectators and adjust the input content of the cheering bat based on the analyzed emotions. For example, if the remote spectators are excited, the remote cheering bat can provide input content that emphasizes the cheering voices. Furthermore, if the remote spectators are relaxed, the remote cheering bat can also enable them to input calm cheering voices. Furthermore, the remote cheering bat can adjust the content of the cheering voices and sounds according to the emotions of the remote spectators. In this way, by adjusting the input content based on the emotions of the remote spectators, more appropriate cheering voices can be input. The estimation of the emotions of the remote 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. Some or all of the above-described processing in the remote cheering bat can be performed, for example, using AI or without AI.

[0106] The remote cheering bat can customize the input content by taking into account the geographical location information of the remote spectator when inputting the cheering bat. For example, the remote cheering bat customizes the input content by taking into account the geographical location information of the remote spectator when inputting the cheering bat. For example, if the remote spectator is in a specific area, the remote cheering bat can provide cheering messages related to that area. The remote cheering bat can also provide cheering messages related to local events based on the geographical location information. Furthermore, the remote cheering bat can also provide cheering messages that the remote spectator is likely to be interested in by taking into account the geographical location information. In this way, by taking into account the geographical location information, it is possible to provide cheering messages that are highly relevant to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0107] The remote cheering bat can analyze the social media activity of the remote spectator when the cheering bat is input and suggest relevant cheering content. For example, the remote cheering bat can analyze the social media activity of the remote spectator when the cheering bat is input and suggest relevant cheering content. For example, the remote cheering bat can provide cheering messages related to locations where the remote spectator has checked in on social media. The remote cheering bat can also analyze social media posts and provide relevant cheering messages. Furthermore, the remote cheering bat can provide relevant cheering messages by referring to the activities of friends on social media. In this way, by analyzing social media activity, relevant cheering messages can be provided to the remote spectator. Some or all of the above-described processing in the remote cheering bat may be performed, for example, using AI or without using AI.

[0108] The transmitting unit can analyze the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the analyzed emotions. For example, the transmitting unit can analyze the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the analyzed emotions. For example, if a remote spectator is excited, the transmitting unit can prioritize transmitting the cheering voices of the excited remote spectator. Also, if a remote spectator becomes quiet, the transmitting unit can prioritize transmitting the cheering voices of other spectators. Furthermore, the transmitting unit can adjust the priority of the audio data in accordance with an event that heightens the emotions of the remote spectators. In this way, by determining the priority of the audio data based on the emotions of the remote spectators, important audio data can be transmitted preferentially. The estimation of the emotions of the remote 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. Some or all of the above-mentioned processing in the transmitting unit can be performed, for example, using AI or without AI.

[0109] The transmitting unit can customize the transmission content by referring to the past transmission history of the remote spectator at the time of transmission. For example, the transmitting unit customizes the transmission content by referring to the past transmission history of the remote spectator at the time of transmission. For example, the transmitting unit transmits audio tailored to the preferences of the remote spectator by referring to cheering voices and sounds transmitted in the past. The transmitting unit can also predict and transmit cheering voices and sounds to be used in a specific time period from the past transmission history. Furthermore, the transmitting unit can analyze the past transmission history and suggest optimal transmission content. In this way, audio tailored to the preferences of the remote spectator can be transmitted by referring to the past transmission history. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI.

[0110] The transmitting unit can adjust the quality of the transmission data during transmission according to the network conditions of the remote spectators. For example, the transmitting unit adjusts the quality of the transmission data during transmission according to the network conditions of the remote spectators. For example, if the network is unstable, the transmitting unit reduces the bit rate of the audio data before transmitting. Furthermore, in a high-speed network environment, the transmitting unit can also transmit high-quality audio data. Furthermore, the transmitting unit can adjust the compression rate of the audio data before transmitting according to the network conditions. In this way, stable audio can be transmitted by adjusting the quality of the transmission data according to the network conditions. Some or all of the above-mentioned processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI.

[0111] The transmitting unit can select the optimal transmission method at the time of transmission, taking into consideration device information of the remote spectator. For example, the transmitting unit selects the optimal transmission method at the time of transmission, taking into consideration device information of the remote spectator. For example, when viewing on a smartphone, the transmitting unit adjusts the bit rate of the audio data before transmitting. Furthermore, when viewing on a tablet, the transmitting unit can also transmit high-quality audio data. Furthermore, the transmitting unit can select and transmit the optimal audio data format depending on the type of device. In this way, by taking into consideration the device information, optimal audio data can be transmitted to the remote spectator. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI, or may be performed without using AI.

[0112] The transmitting unit can analyze the emotions of the remote spectators and adjust the expression method of the audio data to be transmitted based on the analyzed emotions. For example, the transmitting unit can analyze the emotions of the remote spectators and adjust the expression method of the audio data to be transmitted based on the analyzed emotions. For example, if the remote spectators are excited, the transmitting unit can increase the volume of cheers and transmit them. Also, if the remote spectators become quiet, the transmitting unit can calm the tone of the audio and transmit it. Furthermore, the transmitting unit can adjust the audio effects to match an event that heightens the emotions of the remote spectators and transmit it. In this way, by adjusting the expression method of the audio data based on the emotions of the remote spectators, more appropriate audio can be transmitted. The emotions of the remote spectators are estimated 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. Some or all of the above-mentioned processing in the transmitting unit may be performed, for example, using AI or without AI.

[0113] The transmitting unit can customize the transmission content at the time of transmission by taking into account the geographical location information of the remote spectator. For example, the transmitting unit customizes the transmission content at the time of transmission by taking into account the geographical location information of the remote spectator. For example, if the remote spectator is in a specific area, the transmitting unit transmits audio related to that area. The transmitting unit can also transmit audio related to events in the area based on the geographical location information. Furthermore, the transmitting unit can transmit audio that is likely to interest the remote spectator by taking into account the geographical location information. In this way, audio that is highly relevant to the remote spectator can be transmitted by taking into account the geographical location information. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI or without using AI.

[0114] The transmitting unit can analyze the social media activity of the remote spectator at the time of transmission and suggest related content to transmit. For example, the transmitting unit analyzes the social media activity of the remote spectator at the time of transmission and suggest related content to transmit. For example, the transmitting unit transmits audio related to a location where the remote spectator has checked in on social media. The transmitting unit can also analyze the content posted on social media and transmit related audio. Furthermore, the transmitting unit can also transmit related audio with reference to the activity of friends on social media. In this way, audio related to the remote spectator can be transmitted by analyzing social media activity. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI or without using AI.

[0115] The transmitting unit can customize the content of the audio data to be transmitted by reflecting feedback from the remote spectators at the time of transmission. For example, the transmitting unit customizes the content of the audio data to be transmitted by reflecting feedback from the remote spectators at the time of transmission. For example, the transmitting unit adjusts the content of the audio data based on the feedback from the remote spectators and transmits the adjusted content. The transmitting unit can also analyze the feedback and transmit audio data preferred by the remote spectators. Furthermore, the transmitting unit can adjust the effects and tone of the audio data by reflecting the opinions of the remote spectators. In this way, by reflecting the feedback, audio that suits the preferences of the remote spectators can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, provision unit, remote cheering device, and transmission 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 collection unit can collect audio data from the stadium using the camera 42 and microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected audio data to generate audio that reproduces the cheers and cheers from the stadium, the sounds of the competition, and the like. The provision unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that provides the generated audio to remote spectators via the Internet. The remote cheering device is realized by the control unit 46A of the smart device 14 and is a device through which remote spectators input cheering voices and sounds. The transmission unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits the audio data input by the remote cheering device to the stadium and plays it from the speakers in the stadium. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, provision unit, remote cheering device, and transmission 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 collection unit can collect audio data from the stadium using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected audio data to generate audio that reproduces the cheers and cheers from the stadium, the sounds of the competition, and the like. The provision unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that provides the generated audio to remote spectators via the Internet. The remote cheering device is realized by the control unit 46A of the smart glasses 214 and is a device through which remote spectators input cheering voices and sounds. The transmission unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits the audio data input by the remote cheering device to the stadium and plays it from the speakers in the stadium. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, generation unit, provision unit, remote cheering device, and transmission unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect audio data from the stadium using the camera 42 and microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected audio data to generate audio that reproduces the cheers and cheers from the stadium, the sounds of the competition, and the like. The provision unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that provides the generated audio to remote spectators via the Internet. The remote cheering device is realized by the control unit 46A of the headset-type terminal 314, and is a device through which remote spectators input cheering voices and sounds. The transmission unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits the audio data input by the remote cheering device to the stadium and plays it from the speakers in the stadium. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, provision unit, remote cheering device, and transmission unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect audio data from the stadium using the camera 42 and microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected audio data to generate audio that reproduces the cheers and cheers from the stadium, the sounds of the competition, and the like. The provision unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that provides the generated audio to remote spectators via the Internet. The remote cheering device is realized by the control unit 46A of the robot 414 and is a device through which remote spectators input cheering voices and sounds. The transmission unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that transmits the audio data input by the remote cheering device to the stadium and plays it from speakers in the stadium.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The collection unit can also use sensors to detect the movements of spectators in the stadium and adjust the audio to be collected based on their movements. For example, it can collect audio data to capture the peak of cheers the moment spectators stand up. It can also adjust the sensitivity of the microphone to collect quieter audio the moment spectators sit down. It can also adjust the priority of the audio data to be collected according to spectator movements. This allows for the collection of more realistic audio data by adjusting the audio data according to spectator movements.

[0118] The generator can also change the characteristics of the generated audio based on the time of day of the collected audio data. For example, it can generate bright audio for daytime matches and calm audio for nighttime matches. Furthermore, it can adjust the tone and effects of the audio to suit specific time periods. This allows for more realistic audio to be generated by changing the characteristics of the audio based on the time period.

[0119] When providing audio, the providing unit can provide audio that is highly relevant by taking into account the geographical location information of the remote spectator. For example, if the remote spectator is in a specific area, audio related to that area can be provided. Audio related to local events can also be provided based on the location information of the remote spectator. Furthermore, audio that is likely to interest the remote spectator can be provided by taking into account the geographical location information. In this way, audio that is highly relevant to the remote spectator can be provided by taking into account the geographical location information.

[0120] The remote cheering bat can also analyze the social media activity of remote spectators when they input their cheering information and suggest relevant cheering messages. For example, it can provide cheering messages related to the places where the remote spectator checked in on social media. It can also analyze social media posts to provide relevant cheering messages. It can also provide relevant cheering messages based on the activities of friends on social media. In this way, it is possible to provide relevant cheering messages to remote spectators by analyzing social media activity.

[0121] The transmitting unit can also customize the content of the audio data to be transmitted by reflecting feedback from the remote spectators at the time of transmission. For example, the transmitting unit can adjust the content of the audio data based on the feedback from the remote spectators and transmit it. The transmitting unit can also analyze the feedback and transmit audio data that the remote spectators prefer. Furthermore, the transmitting unit can adjust the effects and tone of the audio data by reflecting the opinions of the remote spectators. In this way, by reflecting the feedback, audio that suits the preferences of the remote spectators can be transmitted.

[0122] The collection unit can also analyze the emotions of the audience and determine the priority of the audio data to be collected based on the analyzed emotions of the audience. For example, if the audience is excited, audio data from that area can be collected first. It can also postpone audio data from areas where the audience has quieted down and prioritize audio from areas where the audience is excited. It can also prioritize the collection of audio data from specific areas in accordance with events where the audience's emotions are heightened. In this way, by determining the priority of audio data based on the emotions of the audience, important audio data can be collected with priority.

[0123] The generation unit can also analyze the emotions of the audience and adjust the way the generated sound is expressed based on the analyzed emotions of the audience. For example, if the audience is excited, the volume of the cheers can be increased. If the audience becomes quieter, the tone of the generated sound can be calmed. Furthermore, the sound effects can be adjusted to match events that heighten the audience's emotions. In this way, more realistic sound can be generated by adjusting the way the sound is expressed based on the audience's emotions.

[0124] The providing unit can also analyze the emotions of the spectators and adjust the way the audio is presented based on the analyzed emotions of the spectators. For example, if the spectators are excited, the volume of the cheers can be increased. Alternatively, if the spectators become quieter, the tone of the audio can be calmed down. Furthermore, the audio effects can be adjusted to suit events that heighten the emotions of the spectators. In this way, by adjusting the way the audio is presented based on the emotions of the spectators, more realistic audio can be presented to remote spectators.

[0125] The Remote Cheering Bat can also analyze the emotions of remote spectators and adjust the input method of the cheering bat based on the analyzed emotions. For example, if a remote spectator is excited, they can input cheering voices with simple operations. If a remote spectator is relaxed, more detailed input options can be provided. Furthermore, if a remote spectator is in a hurry, voice input can be prioritized to allow them to input cheering voices quickly. This allows the input method to be adjusted based on the emotions of the remote spectators, allowing them to input more appropriate cheering voices.

[0126] The transmitting unit can also analyze the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the analyzed emotions. For example, if a remote spectator is excited, the voices of his / her cheering can be transmitted with priority. Also, if a remote spectator becomes quiet, the voices of cheering from other spectators can be transmitted with priority. Furthermore, the priority of the audio data can be adjusted according to events that heighten the emotions of the remote spectators. In this way, by determining the priority of the audio data based on the emotions of the remote spectators, important audio data can be transmitted with priority.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The collection unit collects audio data from the stadium. For example, the collection unit can collect audio data using multiple microphones installed in the stadium. The collection unit can also estimate the emotions of spectators and adjust the collection timing based on the emotions in order to adjust the timing of collecting the audio data. Step 2: The generator uses a generation AI to analyze the audio data collected by the collector and generate audio that reproduces the cheers and cheers of the stadium, the sounds of the competition, and other sounds. The generator can, for example, adjust the volume and tone of the collected audio data to reproduce realistic audio. The generator can also customize the audio generation algorithm according to the characteristics of different sports. Step 3: The providing unit provides the audio generated by the generating unit to the remote spectator. For example, the providing unit can provide the generated audio to the remote spectator via the Internet. The providing unit can also adjust the format of the audio depending on the device of the remote spectator. Step 4: The remote cheering device is a device for remote spectators to input cheering voices or sounds. The remote cheering device can, for example, estimate the emotions of the remote spectators and adjust the input method of cheering bats based on the emotions. Step 5: The transmitting unit transmits the audio data input by the remote cheering device to the stadium and plays it from the speakers in the stadium. The transmitting unit can, for example, estimate the emotions of the remote spectators and determine the priority of the audio data to be transmitted based on the emotions.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

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

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

[0143] In the 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.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0147] The data processing system 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 AI 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.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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 AI 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.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] [Explanation of symbols]

[0201] 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. a collection unit that collects audio data from the stadium; a generation unit that analyzes the voice data collected by the collection unit and generates voices that reproduce the cheers and atmosphere of the stadium; a providing unit that provides the sound generated by the generating unit to a remote spectator; A remote cheering device for inputting cheering voices and sounds from remote spectators, a transmitting unit that transmits the audio data input by the remote cheering device to the stadium and plays it from speakers in the stadium. A system characterized by:

2. The collecting unit Audio data is collected using multiple microphones installed in the stadium. The system of claim 1 .

3. The generation unit Analyze the collected audio data and generate audio that reproduces the cheers of spectators, cheers, and sounds of the competition. The system of claim 1 .

4. The providing unit The generated audio is provided to remote spectators via the Internet The system of claim 1 .

5. The transmission unit The voice data input by the remote cheering bat is transmitted to the stadium via the internet and played through the stadium's speakers. The system of claim 1 .

6. The collecting unit Analyze audience emotions and adjust the timing of audio data collection based on the analyzed audience emotions. The system of claim 1 .

7. The collecting unit Collecting audio data from different areas of the stadium and analyzing the audio characteristics of each area The system of claim 1 .

Images