System

Abstract

An object of the system according to the embodiment is to allow a viewer to ask a question in real time during network broadcasting and obtain an answer on the spot.SOLUTION: A system according to an embodiment includes a dialogue reception unit, a data analysis unit, and an answer generation unit. The dialogue reception unit receives a question from a viewer. The data analysis unit analyzes data on the basis of the question received by the dialogue reception unit. The answer generation unit generates an answer based on the data analyzed by the data analysis 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 the drawback of making it difficult for viewers to ask questions in real time during an online broadcast and receive answers on the spot.

[0005] The system according to the embodiment aims to enable viewers to ask questions in real time during an internet broadcast and receive answers on the spot. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue receiving unit, a data analysis unit, and an answer generation unit. The dialogue receiving unit receives questions from viewers. The data analysis unit analyzes data based on the questions received by the dialogue receiving unit. The answer generation unit generates answers based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows viewers to ask questions in real time during an online broadcast and receive answers on the spot. [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 interactive support system according to an embodiment of the present invention is a system that allows viewers to interactively ask questions about players during a soccer match and check data such as possession and pass success rate of players in real time during the match. As a result, the interactive support system allows viewers to check data in real time during the match and learn more about players.

[0029] The interactive support system according to the embodiment includes a dialogue receiving unit, a data analysis unit, and an answer generation unit. The dialogue receiving unit receives questions from viewers. For example, when a viewer inputs a question such as, "How many goals has this player scored this season?", the dialogue receiving unit receives the question. The dialogue receiving unit can also handle questions input by voice from viewers. The data analysis unit analyzes data based on the questions received by the dialogue receiving unit. For example, the data analysis unit analyzes data such as the number of goals scored by a player or the possession rate during a game based on the viewer's question. The data analysis unit can also collect and analyze data during a game in real time. The answer generation unit generates answers based on the data analyzed by the data analysis unit. For example, the answer generation unit generates an answer such as, "This player has scored 10 goals this season" in response to the viewer's question. The answer generation unit can also provide detailed data in response to the viewer's question. This allows the interactive support system according to the embodiment to provide answers to viewer's questions in real time. For example, if a viewer asks "What is the current possession rate?" during a match, the system will analyze the data in real time and provide an answer, allowing viewers to get a detailed understanding of the progress of the match.

[0030] The dialogue reception unit can predict the viewer's interests based on the viewer's past question history and automatically provide related information. For example, the dialogue reception unit stores questions that the viewer has previously asked in a database, and the generation AI analyzes that history. For example, if a viewer has asked many questions about a particular player in the past, the latest information about that player will be automatically provided. The dialogue reception unit also analyzes the viewer's question history to predict the viewer's interests and provides related information. For example, it automatically provides information that the viewer is likely to be interested in based on the content of questions the viewer has asked in the past. This makes it possible to improve the viewing experience by providing information based on the viewer's interests.

[0031] The dialogue receiving unit can suggest follow-up questions to the viewer depending on the content of the question, thereby eliciting deeper information. For example, when a viewer inputs a question, the dialogue receiving unit uses the generation AI to suggest follow-up questions related to that question. For example, in response to the question, "How many goals has this player scored this season?", the dialogue receiving unit suggests follow-up questions such as, "How many assists has this player had?" The dialogue receiving unit can also automatically generate follow-up questions that may interest the viewer. For example, if a viewer asks a question about a specific player, the dialogue receiving unit suggests follow-up questions to provide other information related to that player. This makes it possible to provide more detailed information by suggesting follow-up questions to the viewer.

[0032] The dialogue reception unit can also be applied to other sports and entertainment fields, making it usable with a wide range of content. For example, the dialogue reception unit can apply the interactive dialogue function to other sports such as basketball and baseball, allowing viewers to ask questions about players and the game during the game. For example, the generation AI provides an answer to a question such as, "How many points has this player scored this season?" The dialogue reception unit can also be applied to the entertainment field, allowing viewers to ask questions about movies and music. For example, the generation AI provides an answer to a question such as, "Who is the director of this movie?" This makes it possible to use the system with a wide range of content, improving the versatility of the system.

[0033] The dialogue reception unit can add a function that allows viewers to upload images and videos during a dialogue, thereby realizing a dialogue that incorporates visual information. For example, the dialogue reception unit can add a function that allows viewers to upload images during a dialogue, and the generation AI can analyze the image and provide an answer. For example, if a viewer uploads a photo of a player and wants to know more about that player, the generation AI can provide a profile of that player. The dialogue reception unit can also add a function that allows viewers to upload videos, and the generation AI can analyze the video and provide an answer. For example, if a viewer uploads a highlight video of a game and wants to know more about that game, the generation AI can provide data about that game. This can improve the viewing experience by realizing a dialogue that incorporates visual information.

[0034] The data analysis unit analyzes data during a match and can provide predictions and tactical advice to viewers. For example, the data analysis unit analyzes data during a match in real time, and the generation AI provides predictions to viewers. For example, in response to a question such as "Who will win this match?", the generation AI provides a prediction based on the data. The data analysis unit can also provide tactical advice to viewers based on data during a match. For example, in response to a question such as "Should this player's position be changed?", the generation AI provides advice based on the data. This makes it possible to provide predictions and tactical advice to viewers, thereby improving the viewing experience.

[0035] The data analysis unit can generate interactive graphs and charts for visually displaying real-time data. The data analysis unit, for example, analyzes data during a match in real time and generates interactive graphs for the generation AI to visually display. For example, it provides a graph that displays possession rate and pass success rate in real time. The data analysis unit can also generate interactive charts so that viewers can intuitively understand the data. For example, it provides a chart that displays detailed data when the viewer clicks. In this way, by visually displaying the data, it is possible to enable viewers to intuitively understand the data.

[0036] The data analysis unit can apply the real-time data provision function to other sports and events, making it usable for a wide range of applications. For example, the data analysis unit can apply the real-time data provision function to other sports such as basketball and baseball, allowing viewers to check data during a game in real time. For example, it can provide data on the scores and player performance during the game. The data analysis unit can also apply the real-time data provision function to events such as concerts and conferences, allowing viewers to check data during the event in real time. For example, it can provide a list of songs played during a concert or the content of comments made during a conference. This makes it possible to use the system for a wide range of applications, thereby improving the versatility of the system.

[0037] The data analysis unit can enable viewers to create customizable dashboards and display data tailored to their interests. The data analysis unit, for example, enables viewers to create customizable dashboards and display data tailored to their interests. For example, data that interests the viewer, such as possession rate or pass success rate, can be selected and displayed. The data analysis unit can also provide a function that enables viewers to change the layout of the dashboard. For example, the viewer can add widgets or change their placement. This can improve the viewing experience by enabling viewers to display data tailored to their interests.

[0038] The answer generation unit can analyze a player's past performance data and show the player's growth and trends to viewers. The answer generation unit, for example, analyzes a player's past performance data, and the generation AI shows the player's growth to viewers. For example, the change in the player's number of goals and assists is displayed in a graph. The answer generation unit can also analyze a player's trends and show those trends to viewers. For example, it analyzes the fluctuations in a player's performance and the evolution of their technique and provides this to viewers. This can improve the viewing experience by showing the player's growth and trends to viewers.

[0039] The answer generation unit can analyze player interviews and social media posts to provide viewers with the latest information on players. For example, the answer generation unit analyzes player interviews, and the generation AI provides viewers with the latest information on players. For example, it provides the player's recent comments and opinions. The answer generation unit can also analyze social media posts to provide viewers with the latest information on players. For example, it analyzes the player's Twitter tweets and Instagram posts and provides them to viewers. This can improve the viewing experience by providing viewers with the latest information on players.

[0040] The answer generation unit can apply the player information provision function to personal information in other sports and entertainment fields, making it usable for a wide range of content. For example, the answer generation unit can apply the player information provision function to other sports such as basketball and baseball, allowing viewers to check information about players in real time. For example, it can provide player profiles and past performances. The answer generation unit can also be applied to the entertainment field, allowing viewers to check information about actors and artists in real time. For example, it can provide actor profiles and activity histories. This makes it possible to use the system in a wide range of content, improving the versatility of the system.

[0041] The answer generation unit can add a function that allows viewers to share player information and promote the spread of information on social media. The answer generation unit can, for example, add a function that allows viewers to share player information on social media and promote the spread of information. For example, it can provide a button that shares a player's profile or past performance. The answer generation unit can also provide a function that allows viewers to send player information by email. For example, a viewer can send the latest player information to a friend by email. This allows viewers to share player information and promote the spread of information.

[0042] The answer generation unit can automatically detect important moments during a match and provide highlights to viewers in real time. For example, the answer generation unit automatically detects important moments during a match, and the generation AI provides highlights to viewers in real time. For example, it automatically detects goal scenes and important plays and notifies viewers. The answer generation unit can also provide highlights to viewers in real time based on data from the match. For example, it displays goal scenes and decisive plays during the match in real time. This can improve the viewing experience by providing important moments during the match in real time.

[0043] The answer generation unit can customize the content of the highlights according to the viewer's preferences to meet individual needs. The answer generation unit, for example, customizes the content of the highlights according to the viewer's preferences to meet individual needs. For example, if a viewer is interested in a particular player or team, highlights of that player or team are preferentially displayed. The answer generation unit can also adjust the content of the highlights based on the viewer's preferences. For example, if a viewer is interested in goal scenes, highlights that emphasize goal scenes are provided. This makes it possible to improve the viewing experience by providing highlights that meet the viewer's preferences.

[0044] The answer generation unit can apply the game highlight function to events in other sports and entertainment fields, making it usable for a wide range of content. For example, the answer generation unit applies the game highlight function to other sports such as basketball and baseball, ensuring that viewers do not miss important moments in the game. For example, goal scenes and important plays are provided as highlights. The answer generation unit can also apply the game highlight function to events in the entertainment field, ensuring that viewers do not miss important moments in concerts and festivals. For example, important performances during concerts and highlights during festivals are provided. This makes it possible to use the system for a wide range of content, thereby improving the versatility of the system.

[0045] The answer generation unit can promote user-generated content by adding a function that allows viewers to edit their own highlights. The answer generation unit can promote user-generated content by, for example, adding a function that allows viewers to edit their own highlights. For example, a viewer can select specific scenes from a game and create their own highlights. The answer generation unit can also provide a function that allows viewers to share the highlights they have edited on social media. For example, the highlights they have created can be shared on Twitter or Instagram. This allows viewers to edit their own highlights, thereby promoting user-generated content.

[0046] The feedback analysis unit analyzes viewer feedback, and the generation AI automatically generates improvement suggestions for the broadcast content. The feedback analysis unit, for example, analyzes viewer feedback, and the generation AI automatically generates improvement suggestions for the broadcast content. For example, in response to feedback such as "I want more detailed data," the feedback analysis unit makes a suggestion to improve the way the data is displayed. The feedback analysis unit can also generate suggestions to improve the quality of the broadcast content based on viewer feedback. For example, if a viewer feels that "the commentary is difficult to understand," the feedback analysis unit makes a suggestion to improve the content and method of the commentary. In this way, by generating improvement suggestions for the broadcast content based on viewer feedback, the quality of the broadcast can be improved.

[0047] The feedback analysis unit can ask additional questions to viewers depending on the content of the feedback, thereby collecting more detailed opinions. For example, in the feedback analysis unit, the generation AI asks additional questions to viewers depending on the content of the feedback, thereby collecting more detailed opinions. For example, in response to feedback such as "The explanation was difficult to understand," the AI ​​can ask an additional question such as "Which part was difficult to understand?" The feedback analysis unit can also automatically generate additional questions based on viewer feedback. For example, if a viewer feels that "I would like more detailed data," the AI ​​can ask an additional question about the specific content of that data. In this way, by asking additional questions to viewers, more detailed opinions can be collected.

[0048] The feedback analysis unit can apply the viewer feedback function to content in other sports and entertainment fields, making it usable for a wide range of applications. For example, the feedback analysis unit can apply the viewer feedback function to other sports, such as basketball and baseball, to allow viewers to provide feedback on games and broadcasts. For example, the feedback analysis unit can provide feedback such as "The commentary was easy to understand" or "I would like more detailed data." The feedback analysis unit can also be applied to the entertainment field, allowing viewers to provide feedback on movies and music. For example, the feedback analysis unit can provide feedback such as "The story of this movie was good" or "I liked the music." This makes it usable for a wide range of applications, improving the versatility of the system.

[0049] The feedback analysis unit can add a function to analyze feedback in real time and reflect the results immediately during broadcast. The feedback analysis unit can add a function to analyze feedback in real time and reflect the results immediately during broadcast. For example, in response to feedback such as "I want more detailed data," the method of displaying data can be improved during broadcast. The feedback analysis unit can also adjust broadcast content in real time based on viewer feedback. For example, if a viewer feels that the commentary is difficult to understand, the content and method of the commentary can be improved during broadcast. In this way, the quality of broadcast content can be improved by analyzing feedback in real time and reflecting it immediately.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The interactive support system can add a function that allows viewers to evaluate player performance in real time during a match. For example, viewers can rate a player's play as "good" or "bad," and the ratings are tallied and displayed in real time. Viewers can also leave comments on specific player plays, which are shared with other viewers. Viewers can also vote on player performance, with the highest-rated player announced after the match. This allows viewers to participate more actively in the match and improves the viewing experience.

[0052] The interactive support system can add a function that allows viewers to chat with other viewers in real time during a match. For example, viewers can exchange opinions with other viewers during the match and discuss the progress of the match. Viewers can also share information with other viewers about specific players or plays. Furthermore, viewers can send support messages together with other viewers during the match, and these messages can be displayed on the screen. This allows viewers to enjoy the match more and improves the viewing experience.

[0053] The interactive support system can add a function that allows viewers to predict player performance during a match. For example, viewers can predict the next player who will score or provide the next assist, and if their prediction is correct, they will be awarded points. Viewers can also predict the outcome of a match, and if their prediction is correct, they will be given a reward. Furthermore, viewers can predict player performance during a match, and their predictions can be shared with other viewers. This allows viewers to participate more actively in the match and improves the viewing experience.

[0054] The interactive support system can add the ability for viewers to analyze player performance in real time during a match. For example, viewers can check data such as a player's running distance and number of sprints in real time. They can also visually check a player's performance in graphs and charts. Furthermore, viewers can compare a player's performance with other players, with the comparison results displayed in real time. This allows viewers to gain a deeper understanding of the match and improves their viewing experience.

[0055] The interactive support system can add a feature that allows viewers to evaluate player performance in real time during a match and share the results of their evaluation with other viewers. For example, viewers can rate a player's play as "good" or "bad," and the evaluation results are displayed in real time. Viewers can also leave comments on specific player plays, which can be shared with other viewers. Furthermore, viewers can vote on player performance, and the player with the highest rating can be announced after the match. This allows viewers to participate more actively in the match and improves the viewing experience.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The dialogue reception unit receives questions from viewers. For example, if a viewer inputs a question such as "How many goals has this player scored this season?", the dialogue reception unit will receive the question. It can also handle cases where viewers input questions by voice. Step 2: The data analysis unit analyzes the data based on the questions received by the dialogue reception unit. For example, the data analysis unit analyzes data such as the number of goals scored by players and possession rates during a match based on the viewer's questions. The data analysis unit can also collect and analyze data during a match in real time. Step 3: The answer generation unit generates an answer based on the data analyzed by the data analysis unit. For example, the answer generation unit generates an answer such as "This player has scored 10 points this season" in response to a viewer's question. The answer generation unit can also provide detailed data in response to a viewer's question.

[0058] (Example 2) The interactive support system according to an embodiment of the present invention is a system that allows viewers to interactively ask questions about players during a soccer match and check data such as possession and pass success rate of players in real time during the match. As a result, the interactive support system allows viewers to check data in real time during the match and learn more about players.

[0059] The interactive support system according to the embodiment includes a dialogue receiving unit, a data analysis unit, and an answer generation unit. The dialogue receiving unit receives questions from viewers. For example, when a viewer inputs a question such as, "How many goals has this player scored this season?", the dialogue receiving unit receives the question. The dialogue receiving unit can also handle questions input by voice from viewers. The data analysis unit analyzes data based on the questions received by the dialogue receiving unit. For example, the data analysis unit analyzes data such as the number of goals scored by a player or the possession rate during a game based on the viewer's question. The data analysis unit can also collect and analyze data during a game in real time. The answer generation unit generates answers based on the data analyzed by the data analysis unit. For example, the answer generation unit generates an answer such as, "This player has scored 10 goals this season" in response to the viewer's question. The answer generation unit can also provide detailed data in response to the viewer's question. This allows the interactive support system according to the embodiment to provide answers to viewer's questions in real time. For example, if a viewer asks "What is the current possession rate?" during a match, the system will analyze the data in real time and provide an answer, allowing viewers to get a detailed understanding of the progress of the match.

[0060] The dialogue reception unit can predict the viewer's interests based on the viewer's past question history and automatically provide related information. For example, the dialogue reception unit stores questions that the viewer has previously asked in a database, and the generation AI analyzes that history. For example, if a viewer has asked many questions about a particular player in the past, the latest information about that player will be automatically provided. The dialogue reception unit also analyzes the viewer's question history to predict the viewer's interests and provides related information. For example, it automatically provides information that the viewer is likely to be interested in based on the content of questions the viewer has asked in the past. This makes it possible to improve the viewing experience by providing information based on the viewer's interests.

[0061] The dialogue receiving unit can suggest follow-up questions to the viewer depending on the content of the question, thereby eliciting deeper information. For example, when a viewer inputs a question, the dialogue receiving unit uses the generation AI to suggest follow-up questions related to that question. For example, in response to the question, "How many goals has this player scored this season?", the dialogue receiving unit suggests follow-up questions such as, "How many assists has this player had?" The dialogue receiving unit can also automatically generate follow-up questions that may interest the viewer. For example, if a viewer asks a question about a specific player, the dialogue receiving unit suggests follow-up questions to provide other information related to that player. This makes it possible to provide more detailed information by suggesting follow-up questions to the viewer.

[0062] The dialogue reception unit can use the emotion estimation function to generate answers according to the viewer's emotional state, improving the viewing experience. The dialogue reception unit, for example, analyzes the viewer's emotional state in real time, and the generation AI provides answers according to that emotion. For example, if the viewer is excited, the dialogue reception unit provides answers in a positive tone. The dialogue reception unit can also adjust the content of the answers according to the viewer's emotional state. For example, if the viewer is depressed, the dialogue reception unit provides answers that include words of encouragement. This makes it possible to improve the viewing experience by providing answers according to the viewer's emotional state.

[0063] The dialogue reception unit can also be applied to other sports and entertainment fields, making it usable with a wide range of content. For example, the dialogue reception unit can apply the interactive dialogue function to other sports such as basketball and baseball, allowing viewers to ask questions about players and the game during the game. For example, the generation AI provides an answer to a question such as, "How many points has this player scored this season?" The dialogue reception unit can also be applied to the entertainment field, allowing viewers to ask questions about movies and music. For example, the generation AI provides an answer to a question such as, "Who is the director of this movie?" This makes it possible to use the system with a wide range of content, improving the versatility of the system.

[0064] The dialogue reception unit can add a function that allows viewers to upload images and videos during a dialogue, thereby realizing a dialogue that incorporates visual information. For example, the dialogue reception unit can add a function that allows viewers to upload images during a dialogue, and the generation AI can analyze the image and provide an answer. For example, if a viewer uploads a photo of a player and wants to know more about that player, the generation AI can provide a profile of that player. The dialogue reception unit can also add a function that allows viewers to upload videos, and the generation AI can analyze the video and provide an answer. For example, if a viewer uploads a highlight video of a game and wants to know more about that game, the generation AI can provide data about that game. This can improve the viewing experience by realizing a dialogue that incorporates visual information.

[0065] The dialogue receiving unit can use the emotion estimation function to provide a customized dialogue based on the viewer's emotions, thereby meeting individual needs. The dialogue receiving unit can, for example, use the emotion estimation function to provide a customized dialogue based on the viewer's emotions. For example, if the viewer is excited, the dialogue receiving unit can provide a response in a positive tone. The dialogue receiving unit can also adjust the content of the dialogue depending on the viewer's emotions. For example, if the viewer is depressed, the dialogue receiving unit can provide a dialogue including words of encouragement. This makes it possible to meet individual needs by providing a customized dialogue based on the viewer's emotions.

[0066] The data analysis unit analyzes data during a match and can provide predictions and tactical advice to viewers. For example, the data analysis unit analyzes data during a match in real time, and the generation AI provides predictions to viewers. For example, in response to a question such as "Who will win this match?", the generation AI provides a prediction based on the data. The data analysis unit can also provide tactical advice to viewers based on data during a match. For example, in response to a question such as "Should this player's position be changed?", the generation AI provides advice based on the data. This makes it possible to provide predictions and tactical advice to viewers, thereby improving the viewing experience.

[0067] The data analysis unit can generate interactive graphs and charts for visually displaying real-time data. The data analysis unit, for example, analyzes data during a match in real time and generates interactive graphs for the generation AI to visually display. For example, it provides a graph that displays possession rate and pass success rate in real time. The data analysis unit can also generate interactive charts so that viewers can intuitively understand the data. For example, it provides a chart that displays detailed data when the viewer clicks. In this way, by visually displaying the data, it is possible to enable viewers to intuitively understand the data.

[0068] The data analysis unit can use the emotion estimation function to prioritize display of data according to the viewer's interests and concerns. The data analysis unit, for example, uses the emotion estimation function to prioritize display of data according to the viewer's interests and concerns. For example, if the viewer is excited, game highlights and important data are prioritized to be displayed. The data analysis unit can also adjust the display order of data based on the viewer's emotions. For example, if the viewer is interested in a particular player, data related to that player is prioritized to be displayed. This makes it possible to improve the viewing experience by prioritized display of data according to the viewer's interests and concerns.

[0069] The data analysis unit can apply the real-time data provision function to other sports and events, making it usable for a wide range of applications. For example, the data analysis unit can apply the real-time data provision function to other sports such as basketball and baseball, allowing viewers to check data during a game in real time. For example, it can provide data on the scores and player performance during the game. The data analysis unit can also apply the real-time data provision function to events such as concerts and conferences, allowing viewers to check data during the event in real time. For example, it can provide a list of songs played during a concert or the content of comments made during a conference. This makes it possible to use the system for a wide range of applications, thereby improving the versatility of the system.

[0070] The data analysis unit can enable viewers to create customizable dashboards and display data tailored to their interests. The data analysis unit, for example, enables viewers to create customizable dashboards and display data tailored to their interests. For example, data that interests the viewer, such as possession rate or pass success rate, can be selected and displayed. The data analysis unit can also provide a function that enables viewers to change the layout of the dashboard. For example, the viewer can add widgets or change their placement. This can improve the viewing experience by enabling viewers to display data tailored to their interests.

[0071] The data analysis unit can use the emotion estimation function to provide data highlights based on the viewer's emotions, thereby improving the viewing experience. The data analysis unit, for example, uses the emotion estimation function to provide data highlights based on the viewer's emotions. For example, if the viewer is excited, game highlights and important data are preferentially displayed. The data analysis unit can also adjust the display order of data based on the viewer's emotions. For example, if the viewer is interested in a particular player, data related to that player is preferentially displayed. This makes it possible to improve the viewing experience by providing data highlights based on the viewer's emotions.

[0072] The answer generation unit can analyze a player's past performance data and show the player's growth and trends to viewers. The answer generation unit, for example, analyzes a player's past performance data, and the generation AI shows the player's growth to viewers. For example, the change in the player's number of goals and assists is displayed in a graph. The answer generation unit can also analyze a player's trends and show those trends to viewers. For example, it analyzes the fluctuations in a player's performance and the evolution of their technique and provides this to viewers. This can improve the viewing experience by showing the player's growth and trends to viewers.

[0073] The answer generation unit can analyze player interviews and social media posts to provide viewers with the latest information on players. For example, the answer generation unit analyzes player interviews, and the generation AI provides viewers with the latest information on players. For example, it provides the player's recent comments and opinions. The answer generation unit can also analyze social media posts to provide viewers with the latest information on players. For example, it analyzes the player's Twitter tweets and Instagram posts and provides them to viewers. This can improve the viewing experience by providing viewers with the latest information on players.

[0074] The answer generation unit can use the emotion estimation function to customize player information according to the viewer's emotions and provide more personalized information. The answer generation unit, for example, uses the emotion estimation function to customize player information according to the viewer's emotions. For example, if the viewer is excited, the answer generation unit can provide information that emphasizes the player's highlights and important moments. The answer generation unit can also adjust the content of the player information based on the viewer's emotions. For example, if the viewer is depressed, the answer generation unit can provide player information that includes words of encouragement. This can improve the viewing experience by providing player information according to the viewer's emotions.

[0075] The answer generation unit can apply the player information provision function to personal information in other sports and entertainment fields, making it usable for a wide range of content. For example, the answer generation unit can apply the player information provision function to other sports such as basketball and baseball, allowing viewers to check information about players in real time. For example, it can provide player profiles and past performances. The answer generation unit can also be applied to the entertainment field, allowing viewers to check information about actors and artists in real time. For example, it can provide actor profiles and activity histories. This makes it possible to use the system in a wide range of content, improving the versatility of the system.

[0076] The answer generation unit can add a function that allows viewers to share player information and promote the spread of information on social media. The answer generation unit can, for example, add a function that allows viewers to share player information on social media and promote the spread of information. For example, it can provide a button that shares a player's profile or past performance. The answer generation unit can also provide a function that allows viewers to send player information by email. For example, a viewer can send the latest player information to a friend by email. This allows viewers to share player information and promote the spread of information.

[0077] The answer generation unit can use the emotion estimation function to provide highlights of player information based on the viewer's emotions, thereby improving the viewing experience. The answer generation unit, for example, uses the emotion estimation function to provide highlights of player information based on the viewer's emotions. For example, if the viewer is excited, the answer generation unit can provide information that emphasizes highlights and important moments of the players. The answer generation unit can also adjust the content of the player information based on the viewer's emotions. For example, if the viewer is depressed, the answer generation unit can provide player information that includes words of encouragement. This can improve the viewing experience by providing highlights of player information based on the viewer's emotions.

[0078] The answer generation unit can automatically detect important moments during a match and provide highlights to viewers in real time. For example, the answer generation unit automatically detects important moments during a match, and the generation AI provides highlights to viewers in real time. For example, it automatically detects goal scenes and important plays and notifies viewers. The answer generation unit can also provide highlights to viewers in real time based on data from the match. For example, it displays goal scenes and decisive plays during the match in real time. This can improve the viewing experience by providing important moments during the match in real time.

[0079] The answer generation unit can customize the content of the highlights according to the viewer's preferences to meet individual needs. The answer generation unit, for example, customizes the content of the highlights according to the viewer's preferences to meet individual needs. For example, if a viewer is interested in a particular player or team, highlights of that player or team are preferentially displayed. The answer generation unit can also adjust the content of the highlights based on the viewer's preferences. For example, if a viewer is interested in goal scenes, highlights that emphasize goal scenes are provided. This makes it possible to improve the viewing experience by providing highlights that meet the viewer's preferences.

[0080] The answer generation unit can use the emotion estimation function to generate highlights based on the viewer's emotions, thereby improving the viewing experience. The answer generation unit, for example, uses the emotion estimation function to generate highlights based on the viewer's emotions. For example, if the viewer is excited, the answer generation unit provides highlights that emphasize highlights of the game and important moments. The answer generation unit can also adjust the content of the highlights based on the viewer's emotions. For example, if the viewer is depressed, the answer generation unit provides highlights that include words of encouragement. This makes it possible to improve the viewing experience by providing highlights based on the viewer's emotions.

[0081] The answer generation unit can apply the game highlight function to events in other sports and entertainment fields, making it usable for a wide range of content. For example, the answer generation unit applies the game highlight function to other sports such as basketball and baseball, ensuring that viewers do not miss important moments in the game. For example, goal scenes and important plays are provided as highlights. The answer generation unit can also apply the game highlight function to events in the entertainment field, ensuring that viewers do not miss important moments in concerts and festivals. For example, important performances during concerts and highlights during festivals are provided. This makes it possible to use the system for a wide range of content, thereby improving the versatility of the system.

[0082] The answer generation unit can promote user-generated content by adding a function that allows viewers to edit their own highlights. The answer generation unit can promote user-generated content by, for example, adding a function that allows viewers to edit their own highlights. For example, a viewer can select specific scenes from a game and create their own highlights. The answer generation unit can also provide a function that allows viewers to share the highlights they have edited on social media. For example, the highlights they have created can be shared on Twitter or Instagram. This allows viewers to edit their own highlights, thereby promoting user-generated content.

[0083] The answer generation unit can use the emotion estimation function to provide highlight recommendations based on the viewer's emotions, thereby improving the viewing experience. The answer generation unit, for example, uses the emotion estimation function to provide highlight recommendations based on the viewer's emotions. For example, if the viewer is excited, the answer generation unit can provide highlights that highlight highlights of the game and important moments. The answer generation unit can also adjust the content of the highlights based on the viewer's emotions. For example, if the viewer is depressed, the answer generation unit can provide highlights that include words of encouragement. This makes it possible to improve the viewing experience by providing highlight recommendations based on the viewer's emotions.

[0084] The feedback analysis unit analyzes viewer feedback, and the generation AI automatically generates improvement suggestions for the broadcast content. The feedback analysis unit, for example, analyzes viewer feedback, and the generation AI automatically generates improvement suggestions for the broadcast content. For example, in response to feedback such as "I want more detailed data," the feedback analysis unit makes a suggestion to improve the way the data is displayed. The feedback analysis unit can also generate suggestions to improve the quality of the broadcast content based on viewer feedback. For example, if a viewer feels that "the commentary is difficult to understand," the feedback analysis unit makes a suggestion to improve the content and method of the commentary. In this way, by generating improvement suggestions for the broadcast content based on viewer feedback, the quality of the broadcast can be improved.

[0085] The feedback analysis unit can ask additional questions to viewers depending on the content of the feedback, thereby collecting more detailed opinions. For example, in the feedback analysis unit, the generation AI asks additional questions to viewers depending on the content of the feedback, thereby collecting more detailed opinions. For example, in response to feedback such as "The explanation was difficult to understand," the AI ​​can ask an additional question such as "Which part was difficult to understand?" The feedback analysis unit can also automatically generate additional questions based on viewer feedback. For example, if a viewer feels that "I would like more detailed data," the AI ​​can ask an additional question about the specific content of that data. In this way, by asking additional questions to viewers, more detailed opinions can be collected.

[0086] The feedback analysis unit uses the emotion estimation function to prioritize analysis of feedback based on the viewer's emotions, thereby improving the quality of broadcast content. The feedback analysis unit, for example, uses the emotion estimation function to prioritize analysis of feedback based on the viewer's emotions, thereby improving the quality of broadcast content. For example, if a viewer is excited, the feedback is analyzed with priority, thereby improving the broadcast content. The feedback analysis unit can also evaluate the importance of feedback based on the viewer's emotions. For example, if a viewer has strong emotions, the feedback is analyzed with priority. In this way, the quality of broadcast content can be improved by prioritizing analysis of feedback based on the viewer's emotions.

[0087] The feedback analysis unit can apply the viewer feedback function to content in other sports and entertainment fields, making it usable for a wide range of applications. For example, the feedback analysis unit can apply the viewer feedback function to other sports, such as basketball and baseball, to allow viewers to provide feedback on games and broadcasts. For example, the feedback analysis unit can provide feedback such as "The commentary was easy to understand" or "I would like more detailed data." The feedback analysis unit can also be applied to the entertainment field, allowing viewers to provide feedback on movies and music. For example, the feedback analysis unit can provide feedback such as "The story of this movie was good" or "I liked the music." This makes it usable for a wide range of applications, improving the versatility of the system.

[0088] The feedback analysis unit can add a function to analyze feedback in real time and reflect the results immediately during broadcast. The feedback analysis unit can add a function to analyze feedback in real time and reflect the results immediately during broadcast. For example, in response to feedback such as "I want more detailed data," the method of displaying data can be improved during broadcast. The feedback analysis unit can also adjust broadcast content in real time based on viewer feedback. For example, if a viewer feels that the commentary is difficult to understand, the content and method of the commentary can be improved during broadcast. In this way, the quality of broadcast content can be improved by analyzing feedback in real time and reflecting it immediately.

[0089] The feedback analysis unit can use the emotion estimation function to provide feedback highlights based on the viewer's emotions, thereby improving the viewing experience. The feedback analysis unit, for example, uses the emotion estimation function to provide feedback highlights based on the viewer's emotions. For example, if the viewer is excited, the feedback is analyzed with priority, and the broadcast content is improved. The feedback analysis unit can also evaluate the importance of feedback based on the viewer's emotions. For example, if the viewer has strong emotions, the feedback is analyzed with priority. This makes it possible to provide feedback highlights based on the viewer's emotions, thereby improving the viewing experience.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The interactive support system can add a function that allows viewers to evaluate player performance in real time during a match. For example, viewers can rate a player's play as "good" or "bad," and the ratings are tallied and displayed in real time. Viewers can also leave comments on specific player plays, which are shared with other viewers. Viewers can also vote on player performance, with the highest-rated player announced after the match. This allows viewers to participate more actively in the match and improves the viewing experience.

[0092] The interactive support system can add a function that allows viewers to chat with other viewers in real time during a match. For example, viewers can exchange opinions with other viewers during the match and discuss the progress of the match. Viewers can also share information with other viewers about specific players or plays. Furthermore, viewers can send support messages together with other viewers during the match, and these messages can be displayed on the screen. This allows viewers to enjoy the match more and improves the viewing experience.

[0093] The interactive support system can add a function that allows viewers to predict player performance during a match. For example, viewers can predict the next player who will score or provide the next assist, and if their prediction is correct, they will be awarded points. Viewers can also predict the outcome of a match, and if their prediction is correct, they will be given a reward. Furthermore, viewers can predict player performance during a match, and their predictions can be shared with other viewers. This allows viewers to participate more actively in the match and improves the viewing experience.

[0094] The interactive support system can add the ability for viewers to analyze player performance in real time during a match. For example, viewers can check data such as a player's running distance and number of sprints in real time. They can also visually check a player's performance in graphs and charts. Furthermore, viewers can compare a player's performance with other players, with the comparison results displayed in real time. This allows viewers to gain a deeper understanding of the match and improves their viewing experience.

[0095] The interactive support system can add a feature that allows viewers to evaluate player performance in real time during a match and share the results of their evaluation with other viewers. For example, viewers can rate a player's play as "good" or "bad," and the evaluation results are displayed in real time. Viewers can also leave comments on specific player plays, which can be shared with other viewers. Furthermore, viewers can vote on player performance, and the player with the highest rating can be announced after the match. This allows viewers to participate more actively in the match and improves the viewing experience.

[0096] The interactive support system can use its emotion estimation function to provide customized advertisements based on the viewer's emotions. For example, if the viewer is excited, an energetic advertisement can be displayed. If the viewer is calm, an advertisement with a relaxed atmosphere can be displayed. Furthermore, the content and timing of advertisements can be adjusted based on the viewer's emotions. This maximizes the effectiveness of advertisements and improves the viewing experience by providing advertisements that match the viewer's emotions.

[0097] The interactive support system can use its emotion estimation function to provide customized cheering messages based on the viewer's emotions. For example, if the viewer is excited, a positive cheering message can be displayed. If the viewer is depressed, a cheering message containing encouraging words can be displayed. Furthermore, the content and timing of the cheering message can be adjusted based on the viewer's emotions. This makes it possible to improve the viewing experience by providing cheering messages that correspond to the viewer's emotions.

[0098] The interactive support system can use the emotion estimation function to provide customized game commentary based on the viewer's emotions. For example, if the viewer is excited, the game commentary can be delivered in an energetic tone. If the viewer is calm, the game commentary can be delivered in a calm tone. Furthermore, the content and tone of the game commentary can be adjusted based on the viewer's emotions. This improves the viewing experience by providing game commentary that suits the viewer's emotions.

[0099] The interactive support system can use the emotion estimation function to provide customized match highlights based on the viewer's emotions. For example, if the viewer is excited, it can provide highlights that emphasize the highlights and important moments of the match. On the other hand, if the viewer is calm, it can provide highlights that emphasize the overall flow of the match. Furthermore, it can adjust the content and display timing of highlights based on the viewer's emotions. This can improve the viewing experience by providing match highlights that correspond to the viewer's emotions.

[0100] The interactive support system can use emotion estimation to provide customized player information based on the viewer's emotions. For example, if the viewer is excited, it can provide information emphasizing the player's highlights and important moments. If the viewer is calm, it can provide a detailed profile of the player and their past performance. Furthermore, it can adjust the content and display timing of player information based on the viewer's emotions. This improves the viewing experience by providing player information that matches the viewer's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The dialogue reception unit receives questions from viewers. For example, if a viewer inputs a question such as "How many goals has this player scored this season?", the dialogue reception unit will receive the question. It can also handle cases where viewers input questions by voice. Step 2: The data analysis unit analyzes the data based on the questions received by the dialogue reception unit. For example, the data analysis unit analyzes data such as the number of goals scored by players and possession rates during a match based on the viewer's questions. The data analysis unit can also collect and analyze data during a match in real time. Step 3: The answer generation unit generates an answer based on the data analyzed by the data analysis unit. For example, the answer generation unit generates an answer such as "This player has scored 10 points this season" in response to a viewer's question. The answer generation unit can also provide detailed data in response to a viewer's question.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 dialogue reception section that accepts questions from viewers; a data analysis unit that analyzes data based on the question received by the dialogue reception unit; an answer generation unit that generates an answer based on the data analyzed by the data analysis unit; A system characterized by:

2. The dialogue reception unit Based on the viewer's past question history, the viewer's interests are predicted and related information is automatically provided.

2. The system of claim 1.

3. The data analysis unit Analyzing in-game data and providing predictions and tactical advice to said viewers 2. The system of claim 1.

4. The answer generation unit Analyzing a player's past performance data and showing the player's growth and trends to the viewers 2. The system of claim 1.

5. The feedback analysis part The AI ​​analyzes the viewer feedback and automatically generates suggestions for improving the broadcast content.

2. The system of claim 1.

6. The dialogue reception unit Using emotion estimation capabilities, responses are generated according to the viewer's emotional state to enhance the viewing experience.

2. The system of claim 1.

7. The data analysis unit Using emotion estimation function, data corresponding to the viewer's interests and concerns is displayed preferentially.

2. The system of claim 1.

8. The answer generation unit Using emotion estimation function, player information is customized according to the viewer's emotions, providing more personalized information.

2. The system of claim 1.

Images