System

Abstract

An object of a system according to an embodiment is to enable a spectator to easily understand a rule and a game situation while watching a sport.SOLUTION: A system includes a question reception part, an answer generation part, and an explanation provision part. The question reception unit receives a question from a spectator. The answer generation unit generates an answer based on the question received by the question reception unit. The explanation providing unit provides the answer generated by the answer generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for spectators to understand the rules and the situation of a sporting event.

[0005] The system according to the embodiment aims to enable spectators to easily understand the rules and the situation of a game while watching sports. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, an answer generating unit, and an explanation providing unit. The question receiving unit receives questions from spectators. The answer generating unit generates answers based on the questions received by the question receiving unit. The explanation providing unit provides the answers generated by the answer generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable spectators to easily understand the rules and the situation of the game while watching sports. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The AI ​​chat consultation system according to the embodiment of the present invention allows spectators to listen to commentators' explanations while enjoying live sound, and if they have any questions, they can simply speak to the system and receive an immediate answer. This allows spectators to enjoy watching sports, classical performing arts, and foreign language films more deeply.

[0029] An AI chat consultation system according to an embodiment includes a question receiving unit, an answer generating unit, and an explanation providing unit. The question receiving unit receives questions from spectators. For example, spectators input questions in text format using a smartphone or tablet. The question receiving unit also supports voice input, allowing spectators to input questions simply by speaking. For example, when a spectator asks, "What are the rules of this sport?", the question receiving unit receives the question. The answer generating unit generates an answer based on the question received by the question receiving unit. For example, the generation AI provides an appropriate answer to the spectator's question. The generation AI generates an answer to the question using a text generation AI (e.g., LLM). The generation AI can also provide additional information in response to the spectator's question. For example, when a spectator asks, "What is the current play situation?", the generation AI explains the situation. The explanation providing unit provides the answer generated by the answer generating unit. For example, the explanation providing unit provides the answer generated by the generation AI to the spectator by voice. The explanation providing unit can also display the answer in text format. For example, if a spectator asks, "What does this line mean?", the commentary provider will explain the meaning aloud. This means that spectators can get an instant answer by simply inputting their question.

[0030] The question receiving unit can generate individually customized answers based on the spectator's past question history. For example, the question receiving unit stores questions previously asked by spectators in a database and generates individually customized answers based on that history. For example, a spectator who has asked many questions about the rules in the past can be provided with detailed explanations about the rules. The question receiving unit also analyzes the spectator's past question history and provides pre-prepared answers for frequently asked topics. For example, if there are many questions about a particular player, detailed information about that player can be provided. The question receiving unit also develops an algorithm for generating individually customized answers based on the spectator's past question history. For example, information related to topics in which the spectator has shown interest in the past can be preferentially provided. This makes it possible to provide more appropriate answers based on the spectator's past question history.

[0031] In order to gain a deeper understanding of the intent of the question, the answer generation unit allows the generation AI to ask follow-up questions in a dialogue format and provide a more specific answer. For example, when a spectator inputs a question, the answer generation unit asks follow-up questions to gain a deeper understanding of the intent of the question. For example, in response to the question, "What are the rules of this sport?", the generation AI asks, "Which rule specifically would you like to know about?" The generation AI also responds to the spectator's question in a dialogue format and provides a more specific answer. For example, in response to the question, "What is the current state of play?", the generation AI asks a follow-up question, "Which player's move would you like to know about?" In order to gain a deeper understanding of the intent of the question, the generation AI also asks follow-up questions in a dialogue format to collect more detailed information. For example, in response to the question, "What does this line mean?", the generation AI asks, "Which character's line would you like to know about?" This allows the generation AI to gain a deeper understanding of the intent of the question and provide a more specific answer.

[0032] The system can also answer questions from audience members at events other than sporting events. The system can answer questions from audience members at events such as concerts and festivals. For example, it can provide answers to questions about information about performing artists and schedules. We are also developing an AI chat system that can be used at events other than sporting events to answer audience questions. For example, it can provide information about festival venues and restaurants. We are also building an AI chat system specialized for events such as concerts and festivals to answer audience questions in real time. For example, it can provide information about event highlights and recommended activities. This will enable us to answer audience questions at events other than sporting events.

[0033] The question receiving unit adds a function that allows spectators to attach images or videos when inputting questions, making it possible to generate answers based on visual information. The question receiving unit adds a function that allows spectators to attach images or videos when inputting questions, making it possible to generate answers based on visual information. For example, an image taken of a specific scene from a game can be attached and commentary on that scene can be provided. A function for attaching images or videos can also be added, making it possible for a generation AI to generate answers based on visual information. For example, details of player movements and plays can be explained using images or videos. A system is also provided that allows spectators to attach images or videos when inputting questions, making it possible to generate answers based on visual information. For example, a video of highlights from a game can be provided and commentary on that can be provided. This makes it possible to provide more specific answers based on visual information.

[0034] When providing live audio and commentary simultaneously, the generation AI can adjust the tone and speed of the audio to make it easier for spectators to hear. For example, when providing live audio and commentary simultaneously, the commentary provider can adjust the tone and speed of the audio to make it easier for spectators to hear. For example, it can slow down the speed of the commentary to make it easier for spectators to understand. The generation AI can also adjust the tone and speed of the audio in real time to provide commentary that is easier for spectators to hear. For example, it can emphasize the tone of the audio during important scenes in the game to attract spectators' attention. In addition, an algorithm will be developed that allows the generation AI to adjust the tone and speed of the audio when providing live audio and commentary simultaneously. For example, it can dynamically adjust the tone and speed of the audio based on spectator reactions. This makes it easier for spectators to hear the commentary.

[0035] The commentary providing unit can customize the content of the commentary according to the audience's interests and knowledge level, and switch between commentary for beginners and experts. For example, the commentary providing unit builds a system that customizes the content of the commentary according to the audience's interests and knowledge level, and switches between commentary for beginners and experts. For example, it provides explanations of basic rules to beginners and tactical explanations to experts. It also analyzes the audience's interests and knowledge level and provides commentary according to them. For example, it provides appropriate commentary based on the audience's past question history. It also develops an algorithm that customizes the content of the commentary according to the audience's interests and knowledge level. For example, it provides commentary based on topics of interest selected by the audience. This makes it possible to provide appropriate commentary according to the audience's interests and knowledge level.

[0036] The commentary providing unit can simultaneously provide commentary content in multiple languages, thereby catering to international audiences. The commentary providing unit, for example, builds a system that simultaneously provides commentary content in multiple languages, thereby catering to international audiences. For example, commentary is provided in multiple languages, such as English, French, and Chinese. In addition, a system is developed that automatically switches the commentary content according to the audience's language setting. For example, commentary is provided in the language selected by the audience. In addition, an algorithm is developed to simultaneously provide commentary in multiple languages, thereby catering to international audiences. For example, commentary translated in real time is provided. This allows catering to international audiences.

[0037] The commentary provision unit enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, when explaining classical performing arts, the generating AI provides detailed commentary including historical background and cultural context. For example, it explains the history of Kabuki performances and the cultural elements behind them. In addition, the generating AI uses its detailed knowledge of classical performing arts to provide commentary including historical background and cultural context in response to audience questions. For example, it explains the origins and meanings of Noh performances. In addition, we will develop an algorithm that enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, it provides information related to topics that interest the audience. This will deepen understanding of classical performing arts.

[0038] The explanation providing unit can customize the depth and detail of the explanation according to the audience's interests and knowledge level. The explanation providing unit builds a system that customizes the depth and detail of the explanation according to the audience's interests and knowledge level, for example. For example, it provides basic information to beginners and detailed explanations to experts. It also analyzes the audience's interests and knowledge level and provides explanations according to them. For example, it provides appropriate explanations based on the audience's past question history. It also develops an algorithm that customizes the depth and detail of the explanation according to the audience's interests and knowledge level. For example, it provides explanations based on topics of interest selected by the audience. This makes it possible to provide appropriate explanations according to the audience's interests and knowledge level.

[0039] The system can also be applied to explaining traditional culture other than classical performing arts, allowing the audience to ask the AI ​​questions about parts they don't understand. The system can also be applied to explaining traditional culture other than classical performing arts (for example, tea ceremony and flower arranging), providing a system that allows the audience to ask the AI ​​questions about parts they don't understand. For example, it will provide explanations for questions about the etiquette and history of tea ceremony. We will also develop a system that provides explanations about traditional culture and responds to audience questions. For example, it will provide explanations for questions about the techniques and history of flower arranging. We will also build an AI chat system that can handle traditional culture other than classical performing arts, answering audience questions in real time. For example, we will provide a system that allows the audience to ask questions during a tea ceremony or flower arranging demonstration. This will make it possible to handle traditional culture other than classical performing arts.

[0040] The commentary providing unit can add a function to simultaneously display related images and videos to visually complement the content of the commentary. The commentary providing unit adds a function to simultaneously display related images and videos to visually complement the content of the commentary. For example, when providing commentary on a Kabuki performance, a video of the performance is displayed. Furthermore, a system is constructed that displays images and videos related to the content of the commentary in order to provide visual information. For example, when providing commentary on a Noh performance, an image of the performance is displayed. Furthermore, a function to visually complement the content of the commentary is added to make it easier for the audience to understand. For example, when providing commentary on the history and background of classical performing arts, related images and videos are displayed. This allows the provision of visual information to deepen the understanding of the commentary.

[0041] The commentary provision unit enables the generation AI to provide a translation that takes into account the emotions and tone of the characters when translating foreign language films. For example, when translating a movie, the commentary provision unit enables the generation AI to provide a translation that takes into account the emotions and tone of the characters. For example, in emotional scenes, the generation AI provides a translation that emphasizes emotions. In addition, a system is built in which the generation AI performs emotion analysis to provide a translation that takes into account the emotions and tone of the characters. For example, the system analyzes the characters' facial expressions and tone of voice and provides a translation based on that. In addition, an algorithm is developed in which the generation AI provides a translation that takes into account the emotions and tone of the characters when translating a movie. For example, in emotional scenes, the generation AI provides a translation that emphasizes emotions. This makes it possible to provide an appropriate translation that takes into account the emotions and tone of the characters.

[0042] The explanation providing unit can customize the details and explanations of the translation according to the audience's interests and knowledge level. The explanation providing unit builds a system that customizes the details and explanations of the translation according to the audience's interests and knowledge level, for example. For example, it provides a basic translation for beginners and a detailed translation for experts. It also analyzes the audience's interests and knowledge level and provides a translation according to them. For example, it provides an appropriate translation based on the audience's past question history. It also develops an algorithm that customizes the details and explanations of the translation according to the audience's interests and knowledge level. For example, it provides a translation based on topics of interest selected by the audience. This makes it possible to provide an appropriate translation according to the audience's interests and knowledge level.

[0043] The system can also be applied to translating media other than movies, allowing the audience to ask the AI ​​questions about parts they don't understand. The system can also be applied to translating media other than movies (such as TV dramas and documentaries), providing a system that allows the audience to ask the AI ​​questions about parts they don't understand. For example, it provides translations for questions about specific scenes in TV dramas. We will also develop a system that provides translations related to media and responds to audience questions. For example, it will provide translations for questions about the content of documentaries. We will also build an AI chat system that can be used for media other than movies, answering audience questions in real time. For example, we will provide a system that allows the audience to ask questions while watching TV dramas or documentaries. This will enable us to support media other than movies.

[0044] The commentary providing unit can add a function to simultaneously display related subtitles and annotations to visually complement the content of the translation. The commentary providing unit, for example, adds a function to simultaneously display related subtitles and annotations to visually complement the content of the translation. For example, annotations related to a specific scene in a movie are displayed. Furthermore, a system is constructed that displays subtitles and annotations related to the content of the translation to provide visual information. For example, annotations related to a specific scene in a television drama are displayed. Furthermore, a function to visually complement the content of the translation is added to make it easier for the audience to understand. For example, annotations related to the content of a documentary are displayed. This allows for a deeper understanding of the translation by providing visual information.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The question reception unit will add a function that allows spectators to attach images or videos when entering questions, making it possible to generate answers based on visual information. For example, an image taken of a specific scene from a match can be attached and commentary on that scene can be provided. In addition, a function for attaching images or videos will be added, and the generation AI will generate answers based on visual information. For example, details of players' movements and plays can be explained using images or videos. In addition, a function will be provided that allows spectators to attach images or videos when entering questions, building a system that generates answers based on visual information. For example, highlights from a match can be provided in video and commentary on them can be provided. This will make it possible to provide more specific answers based on visual information.

[0047] The commentary provider can provide commentary content in multiple languages ​​simultaneously to accommodate international audiences. For example, a system will be built to provide commentary content in multiple languages ​​simultaneously to accommodate international audiences. For example, commentary will be provided in multiple languages, such as English, French, and Chinese. A system will also be developed that automatically switches the commentary content according to the audience's language setting. For example, commentary will be provided in the language selected by the audience. An algorithm will also be developed to provide commentary in multiple languages ​​simultaneously to accommodate international audiences. For example, commentary translated in real time will be provided. This will allow for international audiences to be accommodated.

[0048] The commentary providing unit can add a function to simultaneously display related images and videos to visually complement the content of the commentary. For example, when providing commentary on a Kabuki performance, a video of the performance is displayed. Furthermore, a system is constructed that displays images and videos related to the content of the commentary in order to provide visual information. For example, when providing commentary on a Noh performance, an image of the performance is displayed. Furthermore, a function to visually complement the content of the commentary can be added to make it easier for the audience to understand. For example, when providing commentary on the history and background of a classical performing art, related images and videos are displayed. This allows the provision of visual information to deepen the audience's understanding of the commentary.

[0049] The commentary provision unit enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, when explaining classical performing arts, the generating AI provides detailed commentary including historical background and cultural context. For example, it explains the history of Kabuki performances and the cultural elements behind them. In addition, the generating AI uses its detailed knowledge of classical performing arts to provide commentary including historical background and cultural context in response to audience questions. For example, it explains the origins and meanings of Noh performances. In addition, we will develop an algorithm that enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, it will provide information related to topics that interest the audience. This will deepen understanding of classical performing arts.

[0050] The system can also be applied to translating media other than movies, allowing the audience to ask the AI ​​questions about parts they don't understand. For example, it can be applied to translating media other than movies (such as TV dramas and documentaries), providing a system that allows the audience to ask the AI ​​questions about parts they don't understand. For example, it can provide translations for questions about specific scenes in TV dramas. We will also develop a system that provides translations related to media and responds to audience questions. For example, it can provide translations for questions about the content of documentaries. We will also build an AI chat system that can be used for media other than movies, answering audience questions in real time. For example, we will provide a system that allows the audience to ask questions while watching TV dramas or documentaries. This will enable it to be used for media other than movies.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The question reception unit accepts questions from spectators. For example, spectators can input questions in text format using a smartphone or tablet. The question reception unit also supports voice input, allowing spectators to input questions simply by speaking. For example, if a spectator asks, "What are the rules of this sport?", the question reception unit will accept the question. Step 2: The answer generation unit generates an answer based on the question received by the question reception unit. For example, the generation AI provides an appropriate answer to a spectator's question. The generation AI generates an answer to the question using a text generation AI (e.g., LLM). The generation AI can also provide additional information in response to a spectator's question. For example, if a spectator asks, "Please tell me the current situation of the play," the generation AI will explain the situation. Step 3: The explanation provider provides the answer generated by the answer generator. For example, the explanation provider provides the answer generated by the generation AI to the audience by voice. The explanation provider can also display the answer in text format. For example, if the audience asks, "What does this line mean?", the explanation provider will explain its meaning by voice.

[0053] (Example 2) The AI ​​chat consultation system according to the embodiment of the present invention allows spectators to listen to commentators' explanations while enjoying live sound, and if they have any questions, they can simply speak to the system and receive an immediate answer. This allows spectators to enjoy watching sports, classical performing arts, and foreign language films more deeply.

[0054] An AI chat consultation system according to an embodiment includes a question receiving unit, an answer generating unit, and an explanation providing unit. The question receiving unit receives questions from spectators. For example, spectators input questions in text format using a smartphone or tablet. The question receiving unit also supports voice input, allowing spectators to input questions simply by speaking. For example, when a spectator asks, "What are the rules of this sport?", the question receiving unit receives the question. The answer generating unit generates an answer based on the question received by the question receiving unit. For example, the generation AI provides an appropriate answer to the spectator's question. The generation AI generates an answer to the question using a text generation AI (e.g., LLM). The generation AI can also provide additional information in response to the spectator's question. For example, when a spectator asks, "What is the current play situation?", the generation AI explains the situation. The explanation providing unit provides the answer generated by the answer generating unit. For example, the explanation providing unit provides the answer generated by the generation AI to the spectator by voice. The explanation providing unit can also display the answer in text format. For example, if a spectator asks, "What does this line mean?", the commentary provider will explain the meaning aloud. This means that spectators can get an instant answer by simply inputting their question.

[0055] The question receiving unit can generate individually customized answers based on the spectator's past question history. For example, the question receiving unit stores questions previously asked by spectators in a database and generates individually customized answers based on that history. For example, a spectator who has asked many questions about the rules in the past can be provided with detailed explanations about the rules. The question receiving unit also analyzes the spectator's past question history and provides pre-prepared answers for frequently asked topics. For example, if there are many questions about a particular player, detailed information about that player can be provided. The question receiving unit also develops an algorithm for generating individually customized answers based on the spectator's past question history. For example, information related to topics in which the spectator has shown interest in the past can be preferentially provided. This makes it possible to provide more appropriate answers based on the spectator's past question history.

[0056] In order to gain a deeper understanding of the intent of the question, the answer generation unit allows the generation AI to ask follow-up questions in a dialogue format and provide a more specific answer. For example, when a spectator inputs a question, the answer generation unit asks follow-up questions to gain a deeper understanding of the intent of the question. For example, in response to the question, "What are the rules of this sport?", the generation AI asks, "Which rule specifically would you like to know about?" The generation AI also responds to the spectator's question in a dialogue format and provides a more specific answer. For example, in response to the question, "What is the current state of play?", the generation AI asks a follow-up question, "Which player's move would you like to know about?" In order to gain a deeper understanding of the intent of the question, the generation AI also asks follow-up questions in a dialogue format to collect more detailed information. For example, in response to the question, "What does this line mean?", the generation AI asks, "Which character's line would you like to know about?" This allows the generation AI to gain a deeper understanding of the intent of the question and provide a more specific answer.

[0057] The answer generation unit uses the emotion estimation function to generate answers according to the emotional state of the audience, and can provide a concise and easy-to-understand answer if the audience is excited. The answer generation unit, for example, uses the emotion estimation function to analyze the emotional state of the audience in real time and generate answers based on the results. For example, a concise and easy-to-understand answer is provided to an excited audience. The emotion estimation function is also utilized to generate answers according to the emotional state of the audience. For example, an answer that relaxes a nervous audience is provided. The emotion estimation function is also used to analyze the emotional state of the audience and adjust the tone and content of the answer based on the results. For example, an answer that further enhances the emotion of an emotionally moved audience is provided. In this way, an appropriate answer can be provided according to the emotional state of the audience.

[0058] The system can also answer questions from audience members at events other than sporting events. The system can answer questions from audience members at events such as concerts and festivals. For example, it can provide answers to questions about information about performing artists and schedules. We are also developing an AI chat system that can be used at events other than sporting events to answer audience questions. For example, it can provide information about festival venues and restaurants. We are also building an AI chat system specialized for events such as concerts and festivals to answer audience questions in real time. For example, it can provide information about event highlights and recommended activities. This will enable us to answer audience questions at events other than sporting events.

[0059] The question receiving unit adds a function that allows spectators to attach images or videos when inputting questions, making it possible to generate answers based on visual information. The question receiving unit adds a function that allows spectators to attach images or videos when inputting questions, making it possible to generate answers based on visual information. For example, an image taken of a specific scene from a game can be attached and commentary on that scene can be provided. A function for attaching images or videos can also be added, making it possible for a generation AI to generate answers based on visual information. For example, details of player movements and plays can be explained using images or videos. A system is also provided that allows spectators to attach images or videos when inputting questions, making it possible to generate answers based on visual information. For example, a video of highlights from a game can be provided and commentary on that can be provided. This makes it possible to provide more specific answers based on visual information.

[0060] The answer generation unit can use the emotion estimation function to analyze the emotions of audience members when they input questions in real time and make suggestions to elicit positive emotions. For example, the answer generation unit uses the emotion estimation function to analyze the emotions of audience members when they input questions in real time and make suggestions to elicit positive emotions based on the results. For example, encouraging messages or positive feedback can be provided. A system can also be constructed that analyzes the emotional state of audience members in real time and makes suggestions to elicit positive emotions. For example, if an audience member is excited, a suggestion can be made to further enhance that emotion. The emotion estimation function can also be used to analyze the emotions of audience members when they input questions and provide an interface to elicit positive emotions. For example, music or images that help the audience relax can be displayed. This makes it possible to keep the audience's emotions positive.

[0061] When providing live audio and commentary simultaneously, the generation AI can adjust the tone and speed of the audio to make it easier for spectators to hear. For example, when providing live audio and commentary simultaneously, the commentary provider can adjust the tone and speed of the audio to make it easier for spectators to hear. For example, it can slow down the speed of the commentary to make it easier for spectators to understand. The generation AI can also adjust the tone and speed of the audio in real time to provide commentary that is easier for spectators to hear. For example, it can emphasize the tone of the audio during important scenes in the game to attract spectators' attention. In addition, an algorithm will be developed that allows the generation AI to adjust the tone and speed of the audio when providing live audio and commentary simultaneously. For example, it can dynamically adjust the tone and speed of the audio based on spectator reactions. This makes it easier for spectators to hear the commentary.

[0062] The commentary providing unit can customize the content of the commentary according to the audience's interests and knowledge level, and switch between commentary for beginners and experts. For example, the commentary providing unit builds a system that customizes the content of the commentary according to the audience's interests and knowledge level, and switches between commentary for beginners and experts. For example, it provides explanations of basic rules to beginners and tactical explanations to experts. It also analyzes the audience's interests and knowledge level and provides commentary according to them. For example, it provides appropriate commentary based on the audience's past question history. It also develops an algorithm that customizes the content of the commentary according to the audience's interests and knowledge level. For example, it provides commentary based on topics of interest selected by the audience. This makes it possible to provide appropriate commentary according to the audience's interests and knowledge level.

[0063] The commentary providing unit uses the emotion estimation function to provide commentary according to the emotional state of the audience, and can provide commentary that relaxes the audience if they are nervous. The commentary providing unit, for example, uses the emotion estimation function to analyze the emotional state of the audience in real time and provides commentary based on the results. For example, commentary that relaxes a nervous audience is provided. The emotion estimation function is also utilized to provide commentary according to the emotional state of the audience. For example, commentary that further enhances the excitement of an excited audience is provided. The emotion estimation function is also used to analyze the emotional state of the audience and adjust the tone and content of the commentary based on the results. For example, commentary that further enhances the excitement of an emotionally moved audience is provided. In this way, appropriate commentary according to the emotional state of the audience can be provided.

[0064] The commentary providing unit can simultaneously provide commentary content in multiple languages, thereby catering to international audiences. The commentary providing unit, for example, builds a system that simultaneously provides commentary content in multiple languages, thereby catering to international audiences. For example, commentary is provided in multiple languages, such as English, French, and Chinese. In addition, a system is developed that automatically switches the commentary content according to the audience's language setting. For example, commentary is provided in the language selected by the audience. In addition, an algorithm is developed to simultaneously provide commentary in multiple languages, thereby catering to international audiences. For example, commentary translated in real time is provided. This allows catering to international audiences.

[0065] The commentary providing unit can use the emotion estimation function to analyze the emotions of the audience when they are listening to the commentary in real time and adjust the content and tone of the commentary. For example, the commentary providing unit uses the emotion estimation function to analyze the emotions of the audience when they are listening to the commentary in real time and adjust the content and tone of the commentary based on the results. For example, if the audience is excited, the commentary is provided to further enhance that emotion. In addition, a system is constructed that analyzes the audience's emotional state in real time and adjusts the content and tone of the commentary. For example, commentary is provided that allows the audience to relax. In addition, the emotion estimation function is used to analyze the audience's emotional state and dynamically adjust the tone and content of the commentary based on the results. For example, commentary is provided to an audience that is moved by something that further enhances that emotion. In this way, appropriate commentary can be provided according to the audience's emotional state.

[0066] The commentary provision unit enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, when explaining classical performing arts, the generating AI provides detailed commentary including historical background and cultural context. For example, it explains the history of Kabuki performances and the cultural elements behind them. In addition, the generating AI uses its detailed knowledge of classical performing arts to provide commentary including historical background and cultural context in response to audience questions. For example, it explains the origins and meanings of Noh performances. In addition, we will develop an algorithm that enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, it provides information related to topics that interest the audience. This will deepen understanding of classical performing arts.

[0067] The explanation providing unit can customize the depth and detail of the explanation according to the audience's interests and knowledge level. The explanation providing unit builds a system that customizes the depth and detail of the explanation according to the audience's interests and knowledge level, for example. For example, it provides basic information to beginners and detailed explanations to experts. It also analyzes the audience's interests and knowledge level and provides explanations according to them. For example, it provides appropriate explanations based on the audience's past question history. It also develops an algorithm that customizes the depth and detail of the explanation according to the audience's interests and knowledge level. For example, it provides explanations based on topics of interest selected by the audience. This makes it possible to provide appropriate explanations according to the audience's interests and knowledge level.

[0068] The commentary providing unit uses the emotion estimation function to provide commentary according to the emotional state of the audience, and if the audience is moved, commentary can be provided that further enhances that emotion. The commentary providing unit, for example, uses the emotion estimation function to analyze the emotional state of the audience in real time and provide commentary based on the results. For example, for an audience that is moved, commentary that further enhances that emotion is provided. Furthermore, the emotion estimation function is utilized to provide commentary according to the emotional state of the audience. For example, for an excited audience, commentary that further enhances that emotion is provided. Furthermore, the emotion estimation function is used to analyze the emotional state of the audience, and the tone and content of the commentary are adjusted based on the results. For example, for an emotional audience, commentary that further enhances that emotion is provided. In this way, appropriate commentary can be provided according to the emotional state of the audience.

[0069] The system can also be applied to explaining traditional culture other than classical performing arts, allowing the audience to ask the AI ​​questions about parts they don't understand. The system can also be applied to explaining traditional culture other than classical performing arts (for example, tea ceremony and flower arranging), providing a system that allows the audience to ask the AI ​​questions about parts they don't understand. For example, it will provide explanations for questions about the etiquette and history of tea ceremony. We will also develop a system that provides explanations about traditional culture and responds to audience questions. For example, it will provide explanations for questions about the techniques and history of flower arranging. We will also build an AI chat system that can handle traditional culture other than classical performing arts, answering audience questions in real time. For example, we will provide a system that allows the audience to ask questions during a tea ceremony or flower arranging demonstration. This will make it possible to handle traditional culture other than classical performing arts.

[0070] The commentary providing unit can add a function to simultaneously display related images and videos to visually complement the content of the commentary. The commentary providing unit adds a function to simultaneously display related images and videos to visually complement the content of the commentary. For example, when providing commentary on a Kabuki performance, a video of the performance is displayed. Furthermore, a system is constructed that displays images and videos related to the content of the commentary in order to provide visual information. For example, when providing commentary on a Noh performance, an image of the performance is displayed. Furthermore, a function to visually complement the content of the commentary is added to make it easier for the audience to understand. For example, when providing commentary on the history and background of classical performing arts, related images and videos are displayed. This allows the provision of visual information to deepen the understanding of the commentary.

[0071] The commentary providing unit can use the emotion estimation function to analyze the emotions of the audience when they are listening to the commentary in real time and adjust the content and tone of the commentary. For example, the commentary providing unit uses the emotion estimation function to analyze the emotions of the audience when they are listening to the commentary in real time and adjust the content and tone of the commentary based on the results. For example, if the audience is moved, the commentary is provided to further enhance that emotion. In addition, a system is constructed that analyzes the audience's emotional state in real time and adjusts the content and tone of the commentary. For example, commentary is provided that allows the audience to relax. In addition, the emotion estimation function is used to analyze the audience's emotional state and dynamically adjust the tone and content of the commentary based on the results. For example, if an audience is moved, commentary is provided that further enhances that emotion. In this way, appropriate commentary can be provided according to the audience's emotional state.

[0072] The commentary provision unit enables the generation AI to provide a translation that takes into account the emotions and tone of the characters when translating foreign language films. For example, when translating a movie, the commentary provision unit enables the generation AI to provide a translation that takes into account the emotions and tone of the characters. For example, in emotional scenes, the generation AI provides a translation that emphasizes emotions. In addition, a system is built in which the generation AI performs emotion analysis to provide a translation that takes into account the emotions and tone of the characters. For example, the system analyzes the characters' facial expressions and tone of voice and provides a translation based on that. In addition, an algorithm is developed in which the generation AI provides a translation that takes into account the emotions and tone of the characters when translating a movie. For example, in emotional scenes, the generation AI provides a translation that emphasizes emotions. This makes it possible to provide an appropriate translation that takes into account the emotions and tone of the characters.

[0073] The explanation providing unit can customize the details and explanations of the translation according to the audience's interests and knowledge level. The explanation providing unit builds a system that customizes the details and explanations of the translation according to the audience's interests and knowledge level, for example. For example, it provides a basic translation for beginners and a detailed translation for experts. It also analyzes the audience's interests and knowledge level and provides a translation according to them. For example, it provides an appropriate translation based on the audience's past question history. It also develops an algorithm that customizes the details and explanations of the translation according to the audience's interests and knowledge level. For example, it provides a translation based on topics of interest selected by the audience. This makes it possible to provide an appropriate translation according to the audience's interests and knowledge level.

[0074] The commentary providing unit uses the emotion estimation function to provide a translation that corresponds to the emotional state of the audience, and if the audience is moved, a translation that further enhances that emotion. The commentary providing unit, for example, uses the emotion estimation function to analyze the emotional state of the audience in real time and provides a translation based on the results. For example, for an audience that is moved, a translation that further enhances that emotion is provided. The emotion estimation function is also utilized to provide a translation that corresponds to the emotional state of the audience. For example, for an excited audience, a translation that further enhances that emotion is provided. The emotion estimation function is also used to analyze the emotional state of the audience, and the tone and content of the translation are adjusted based on the results. For example, for an emotional audience, a translation that further enhances that emotion is provided. In this way, an appropriate translation can be provided according to the emotional state of the audience.

[0075] The system can also be applied to translating media other than movies, allowing the audience to ask the AI ​​questions about parts they don't understand. The system can also be applied to translating media other than movies (such as TV dramas and documentaries), providing a system that allows the audience to ask the AI ​​questions about parts they don't understand. For example, it provides translations for questions about specific scenes in TV dramas. We will also develop a system that provides translations related to media and responds to audience questions. For example, it will provide translations for questions about the content of documentaries. We will also build an AI chat system that can be used for media other than movies, answering audience questions in real time. For example, we will provide a system that allows the audience to ask questions while watching TV dramas or documentaries. This will enable us to support media other than movies.

[0076] The commentary providing unit can add a function to simultaneously display related subtitles and annotations to visually complement the content of the translation. The commentary providing unit, for example, adds a function to simultaneously display related subtitles and annotations to visually complement the content of the translation. For example, annotations related to a specific scene in a movie are displayed. Furthermore, a system is constructed that displays subtitles and annotations related to the content of the translation to provide visual information. For example, annotations related to a specific scene in a television drama are displayed. Furthermore, a function to visually complement the content of the translation is added to make it easier for the audience to understand. For example, annotations related to the content of a documentary are displayed. This allows for a deeper understanding of the translation by providing visual information.

[0077] The commentary providing unit can use the emotion estimation function to analyze the emotions of the audience when they are listening to the translation in real time and adjust the content and tone of the translation. For example, the commentary providing unit uses the emotion estimation function to analyze the emotions of the audience when they are listening to the translation in real time and adjust the content and tone of the translation based on the results. For example, if the audience is moved, a translation that further enhances that emotion is provided. A system is also constructed that analyzes the audience's emotional state in real time and adjusts the content and tone of the translation. For example, a translation that allows the audience to relax is provided. The emotion estimation function is also used to analyze the audience's emotional state and dynamically adjust the tone and content of the translation based on the results. For example, if an audience is moved, a translation that further enhances that emotion is provided. This makes it possible to provide an appropriate translation according to the audience's emotional state.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The question reception unit will add a function that allows spectators to attach images or videos when entering questions, making it possible to generate answers based on visual information. For example, an image taken of a specific scene from a match can be attached and commentary on that scene can be provided. In addition, a function for attaching images or videos will be added, and the generation AI will generate answers based on visual information. For example, details of players' movements and plays can be explained using images or videos. In addition, a function will be provided that allows spectators to attach images or videos when entering questions, building a system that generates answers based on visual information. For example, highlights from a match can be provided in video and commentary on them can be provided. This will make it possible to provide more specific answers based on visual information.

[0080] The commentary provider can provide commentary content in multiple languages ​​simultaneously to accommodate international audiences. For example, a system will be built to provide commentary content in multiple languages ​​simultaneously to accommodate international audiences. For example, commentary will be provided in multiple languages, such as English, French, and Chinese. A system will also be developed that automatically switches the commentary content according to the audience's language setting. For example, commentary will be provided in the language selected by the audience. An algorithm will also be developed to provide commentary in multiple languages ​​simultaneously to accommodate international audiences. For example, commentary translated in real time will be provided. This will allow for international audiences to be accommodated.

[0081] The commentary providing unit can add a function to simultaneously display related images and videos to visually complement the content of the commentary. For example, when providing commentary on a Kabuki performance, a video of the performance is displayed. Furthermore, a system is constructed that displays images and videos related to the content of the commentary in order to provide visual information. For example, when providing commentary on a Noh performance, an image of the performance is displayed. Furthermore, a function to visually complement the content of the commentary can be added to make it easier for the audience to understand. For example, when providing commentary on the history and background of a classical performing art, related images and videos are displayed. This allows the provision of visual information to deepen the audience's understanding of the commentary.

[0082] The commentary provision unit enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, when explaining classical performing arts, the generating AI provides detailed commentary including historical background and cultural context. For example, it explains the history of Kabuki performances and the cultural elements behind them. In addition, the generating AI uses its detailed knowledge of classical performing arts to provide commentary including historical background and cultural context in response to audience questions. For example, it explains the origins and meanings of Noh performances. In addition, we will develop an algorithm that enables the generating AI to provide detailed commentary including historical background and cultural context when explaining classical performing arts. For example, it will provide information related to topics that interest the audience. This will deepen understanding of classical performing arts.

[0083] The system can also be applied to translating media other than movies, allowing the audience to ask the AI ​​questions about parts they don't understand. For example, it can be applied to translating media other than movies (such as TV dramas and documentaries), providing a system that allows the audience to ask the AI ​​questions about parts they don't understand. For example, it can provide translations for questions about specific scenes in TV dramas. We will also develop a system that provides translations related to media and responds to audience questions. For example, it can provide translations for questions about the content of documentaries. We will also build an AI chat system that can be used for media other than movies, answering audience questions in real time. For example, we will provide a system that allows the audience to ask questions while watching TV dramas or documentaries. This will enable it to be used for media other than movies.

[0084] The answer generation unit uses the emotion estimation function to generate answers according to the emotional state of the audience, and can provide a concise and easy-to-understand answer if the audience is excited. For example, the emotion estimation function is used to analyze the emotional state of the audience in real time and generate answers based on the results. For example, a concise and easy-to-understand answer is provided to an excited audience. The emotion estimation function is also utilized to generate answers according to the emotional state of the audience. For example, an answer that relaxes a nervous audience is provided. The emotion estimation function is also used to analyze the emotional state of the audience and adjust the tone and content of the answer based on the results. For example, an answer that further enhances the emotions of an emotionally moved audience is provided. In this way, an appropriate answer can be provided according to the emotional state of the audience.

[0085] The answer generation unit can use the emotion estimation function to analyze the emotions of audience members when they input questions in real time and make suggestions to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions of audience members when they input questions in real time and make suggestions to elicit positive emotions based on the results. For example, encouraging messages or positive feedback can be provided. A system can also be constructed that analyzes the emotional state of audience members in real time and makes suggestions to elicit positive emotions. For example, if an audience member is excited, suggestions can be made to further enhance that emotion. The emotion estimation function can also be used to analyze the emotions of audience members when they input questions and provide an interface to elicit positive emotions. For example, music or images that help the audience relax can be displayed. This makes it possible to keep the audience's emotions positive.

[0086] The commentary providing unit uses the emotion estimation function to provide commentary that corresponds to the emotional state of the audience, and can provide commentary that relaxes them if they are nervous. For example, the emotion estimation function is used to analyze the emotional state of the audience in real time, and commentary is provided based on the results. For example, commentary that relaxes a nervous audience is provided. The emotion estimation function is also utilized to provide commentary that corresponds to the emotional state of the audience. For example, commentary that further enhances an excited audience is provided for an excited audience. The emotion estimation function is also used to analyze the emotional state of the audience, and the tone and content of the commentary are adjusted based on the results. For example, commentary that further enhances an emotional audience is provided for an emotionally moved audience. In this way, appropriate commentary can be provided according to the emotional state of the audience.

[0087] The commentary providing unit can use the emotion estimation function to analyze the emotions of the audience when they are listening to the commentary in real time and adjust the content and tone of the commentary. For example, the emotion estimation function can be used to analyze the emotions of the audience when they are listening to the commentary in real time, and the content and tone of the commentary can be adjusted based on the results. For example, if the audience is excited, commentary can be provided that further enhances that emotion. In addition, a system can be constructed that analyzes the audience's emotional state in real time and adjusts the content and tone of the commentary. For example, commentary can be provided that allows the audience to relax. In addition, the emotion estimation function can be used to analyze the audience's emotional state, and the tone and content of the commentary can be dynamically adjusted based on the results. For example, if an audience member is moved, commentary can be provided that further enhances that emotion. In this way, appropriate commentary can be provided according to the audience's emotional state.

[0088] The commentary providing unit uses the emotion estimation function to provide a translation that corresponds to the emotional state of the audience, and if the audience is moved, a translation that further enhances that emotion. For example, the emotion estimation function is used to analyze the emotional state of the audience in real time, and a translation is provided based on the results. For example, a translation that further enhances the emotion of an audience that is moved is provided. The emotion estimation function is also utilized to provide a translation that corresponds to the emotional state of the audience. For example, a translation that further enhances the emotion of an excited audience is provided. The emotion estimation function is also used to analyze the emotional state of the audience, and the tone and content of the translation are adjusted based on the results. For example, a translation that further enhances the emotion of an emotional audience is provided. This makes it possible to provide an appropriate translation that corresponds to the emotional state of the audience.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The question reception unit accepts questions from spectators. For example, spectators can input questions in text format using a smartphone or tablet. The question reception unit also supports voice input, allowing spectators to input questions simply by speaking. For example, if a spectator asks, "What are the rules of this sport?", the question reception unit will accept the question. Step 2: The answer generation unit generates an answer based on the question received by the question reception unit. For example, the generation AI provides an appropriate answer to a spectator's question. The generation AI generates an answer to the question using a text generation AI (e.g., LLM). The generation AI can also provide additional information in response to a spectator's question. For example, if a spectator asks, "Please tell me the current situation of the play," the generation AI will explain the situation. Step 3: The explanation provider provides the answer generated by the answer generator. For example, the explanation provider provides the answer generated by the generation AI to the audience by voice. The explanation provider can also display the answer in text format. For example, if the audience asks, "What does this line mean?", the explanation provider will explain its meaning by voice.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 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 section to accept questions from the audience, an answer generation unit that generates an answer based on the question received by the question receiving unit; an explanation providing unit that provides the answer generated by the answer generating unit; A system characterized by:

2. The question reception department A feature has been added that allows spectators to attach images and videos when entering questions, allowing answers to be generated based on visual information.

2. The system of claim 1.

3. The commentary section is When live audio and commentary are provided simultaneously, generative AI adjusts the tone and speed of the audio to make it easier for the audience to hear.

2. The system of claim 1.

4. The commentary section is When explaining classical performing arts, generative AI provides detailed explanations, including historical background and cultural context.

2. The system of claim 1.

5. The answer generation unit: Generate answers based on the audience's emotional state, providing concise and easy-to-understand answers when they are excited 2. The system of claim 1.

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