System

Abstract

An object of the system according to the embodiment is to enable a visually impaired person to understand the detailed trend and explanation of a game in real time.SOLUTION: A system includes an analysis unit, a generation unit, a vocalization unit, a reception unit, and an answer generation unit. The analysis unit analyzes a trend of a game. The generation unit generates an explanation on the basis of a result analyzed by the analysis unit. The vocalization unit vocalizes the explanation generated by the generation unit. The reception unit receives a question from a user. The answer generation unit generates an answer to the question received by the reception 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 problem that it is difficult for visually impaired people to understand detailed game trends and commentary in real time.

[0005] The system according to the embodiment aims to enable visually impaired people to understand detailed game progress and commentary in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a voice generation unit, a reception unit, and an answer generation unit. The analysis unit analyzes the progress of a match. The generation unit generates commentary based on the results of the analysis by the analysis unit. The voice generation unit voices the commentary generated by the generation unit. The reception unit receives questions from users. The answer generation unit generates answers to the questions received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable visually impaired people to understand detailed trends and commentary of a match in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sports commentary system according to an embodiment of the present invention allows visually impaired people to share in the excitement of a game and enhance their sports experience. The sports commentary system utilizes a generative AI to provide audio commentary services for sports games. The generative AI instantly analyzes game trends and results and generates detailed audio commentary in real time. This helps visually impaired people understand the game in a richer, more user-friendly way. For example, the sports commentary system grasps the progress of the game, players' movements, and score fluctuations in real time. Then, based on the analysis results, it generates and audio-generates detailed commentary. For example, it provides commentary on scoring scenes and player plays. In this case, it imitates the voices of multiple announcers with different perspectives and commentary styles, providing a richer sports experience for visually impaired people. Furthermore, the sports commentary system utilizes the natural language processing capabilities of AI to allow users to freely ask questions about the game. For example, the generative AI instantly answers questions such as, "Who scored that play?" or "What is this player's past performance?" This allows users to gain a deeper understanding of specific moments and plays in the game. This allows the sports commentary system to share the excitement of the game with the visually impaired and improve the sports experience. For example, a visually impaired person can listen to the commentary and understand the details of the goals scored and the players' plays. In addition, users can ask questions about the game, which deepens their understanding of the game and provides a richer sports experience.

[0029] A sports commentary system according to an embodiment includes an analysis unit, a generation unit, a voice generation unit, a reception unit, and an answer generation unit. The analysis unit analyzes the progress of a game. The progress of a game includes, but is not limited to, the progress of the game, player movements, and score fluctuations. For example, the analysis unit grasps the progress of the game in real time. The analysis unit can also analyze player movements and grasp score fluctuations. For example, the analysis unit monitors the progress of the game in real time and analyzes player movements. The generation unit generates commentary based on the results of the analysis by the analysis unit. The commentary includes, but is not limited to, detailed commentary on scoring scenes and player plays. For example, the generation unit provides detailed commentary on scoring scenes. The generation unit can also generate commentary on player plays. For example, the generation unit provides detailed commentary on scoring scenes and analyzes player plays. The voice generation unit converts the commentary generated by the generation unit into voice. For example, speech synthesis technology is used for the voice generation, but is not limited to, the example. The voice generation unit, for example, voices the generated commentary in real time. The voice generation unit can also provide the generated commentary in high-quality voice. For example, the voice generation unit voices the generated commentary in real time and provides it in high-quality voice. The reception unit receives questions from the user. Questions include, for example, voice questions and text questions, but are not limited to these examples. The reception unit, for example, receives voice questions from the user. The reception unit can also receive text questions from the user. For example, the reception unit receives voice questions and text questions from the user. The answer generation unit generates answers to the questions received by the reception unit. Answers include, for example, detailed answers and concise answers, but are not limited to these examples. The answer generation unit, for example, generates detailed answers to the user's questions. The answer generation unit can also generate concise answers to the user's questions. For example, the answer generation unit generates detailed answers and concise answers to the user's questions. This allows the sports commentary system according to the embodiment to allow visually impaired people to share in the excitement of the game and improve the sports experience.For example, visually impaired people can listen to the commentary of a game and understand the details of the goals scored and the players' plays. Users can also ask questions about the game, deepening their understanding of the game and providing a richer sports experience.

[0030] The generation unit can imitate multiple announcers with different perspectives or commentary styles. For example, the generation unit generates commentary by imitating the perspective of the attacking team. The generation unit can also generate commentary by imitating the perspective of the defending team. For example, the generation unit generates commentary by imitating the perspective of a spectator. The generation unit can also generate commentary by imitating a technical commentary style. For example, the generation unit can generate commentary by imitating an emotional commentary style. The generation unit can also generate commentary by imitating a humorous commentary style. This can provide a richer sports experience for visually impaired people. For example, visually impaired people can enjoy different perspectives and commentary styles. Furthermore, visually impaired people can understand the details of a game from multiple angles.

[0031] The answer generation unit can generate detailed answers to user questions. The answer generation unit, for example, provides specific data in response to the user questions. The answer generation unit can also provide examples in response to the user questions. For example, the answer generation unit provides background information in response to the user questions. The answer generation unit can also provide detailed explanations in response to the user questions. For example, the answer generation unit provides specific data and examples in response to the user questions. This allows the user to obtain detailed answers to questions about a game. For example, the user can gain a deeper understanding of specific moments or plays in a game. The user can also learn background information and detailed data about the game.

[0032] The analysis unit can grasp the progress of the game, the movements of the players, and the fluctuations in the score in real time. The analysis unit, for example, grasps the passage of time in the game in real time. The analysis unit can also grasp the fluctuations in the score in real time. For example, the analysis unit grasps the movements of the players in real time. The analysis unit can also grasp the progress of the game in real time. For example, the analysis unit grasps the passage of time in the game in real time and the fluctuations in the score in real time. This makes it possible to grasp the trends of the game in real time. For example, the progress of the game and the movements of the players can be grasped in real time. The fluctuations in the score can be grasped in real time.

[0033] The generation unit can generate commentary on scoring scenes and player plays. For example, the generation unit provides detailed commentary on scoring scenes. The generation unit can also generate commentary on player plays. For example, the generation unit provides detailed commentary on scoring scenes and analyzes the player plays. The generation unit can also generate commentary on scoring scenes and player plays. For example, the generation unit provides detailed commentary on scoring scenes and analyzes the player plays. This makes it possible to provide detailed commentary on scoring scenes and player plays. For example, a visually impaired person can understand a scoring scene or a player's play in detail. Furthermore, a visually impaired person can share in the excitement of a game.

[0034] The vocalization unit can vocalize the generated commentary in real time. For example, the vocalization unit vocalizes the generated commentary in real time. Furthermore, the vocalization unit can also provide the generated commentary in high-quality audio. For example, the vocalization unit vocalizes the generated commentary in real time and provides it in high-quality audio. This allows the commentary to be vocalized in real time. For example, a visually impaired person can listen to commentary on a match in real time. Furthermore, the visually impaired person can share in the excitement of the match.

[0035] When analyzing the progress of a match, the analysis unit can improve the accuracy of the analysis based on the player's physical strength or fatigue level. For example, the analysis unit acquires real-time heart rate data of the player and reflects the fatigue level in the analysis. The analysis unit can also refer to the player's past match data and reflect the physical strength consumption pattern in the analysis. For example, the analysis unit analyzes the speed and frequency of the player's movements during the match and estimates the fatigue level. This makes it possible to improve the accuracy of the analysis by taking the player's physical strength and fatigue level into consideration. For example, the analysis unit can acquire real-time heart rate data of the player and reflect the fatigue level in the analysis. Also, the analysis unit can refer to the player's past match data and reflect the physical strength consumption pattern in the analysis.

[0036] When analyzing trends in a match, the analysis unit can refer to past match data to predict the outcome of the current match. For example, the analysis unit refers to data from past matches involving the same matchups to predict the outcome of the current match. The analysis unit can also predict the outcome of the current match based on past performance data of players. For example, the analysis unit analyzes scoring patterns in past matches to predict the scores of the current match. This makes it possible to predict the outcome of the current match by referring to past match data. For example, it is possible to refer to data from past matches involving the same matchups to predict the outcome of the current match. It is also possible to predict the outcome of the current match based on past performance data of players.

[0037] The analysis unit can estimate the psychological state of a player when analyzing the trends of a match and reflect this in the analysis results. The analysis unit, for example, analyzes the player's facial expressions and movements to estimate the psychological state. The analysis unit can also refer to the player's past interviews and comments to estimate the psychological state. For example, the analysis unit analyzes the player's behavioral patterns during a match to estimate the psychological state. This allows the player's psychological state to be reflected in the analysis results, enabling more accurate analysis. For example, the analysis unit can analyze the player's facial expressions and movements to estimate the psychological state. The analysis unit can also refer to the player's past interviews and comments to estimate the psychological state.

[0038] The analysis unit can incorporate spectator reaction data when analyzing trends in a match to improve the accuracy of the analysis. For example, the analysis unit can analyze the volume of spectator cheers and applause to reflect the excitement of the match. The analysis unit can also analyze spectator social media posts to identify key points in the match. For example, the analysis unit can analyze spectator reactions in real time and reflect these in the trends of the match. This allows the analysis unit to incorporate spectator reaction data to improve the accuracy of the analysis. For example, the analysis unit can analyze the volume of spectator cheers and applause to reflect the excitement of the match. The analysis unit can also analyze spectator social media posts to identify key points in the match.

[0039] The analysis unit can perform analysis based on weather or environmental conditions when analyzing the trends of a match. For example, the analysis unit acquires weather data at the match venue and analyzes the impact on player performance. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue. For example, the analysis unit analyzes the movement of the ball by taking into account the wind speed and wind direction at the match venue. This makes it possible to perform analysis by taking into account weather and environmental conditions. For example, weather data at the match venue can be acquired and the impact on player performance can be analyzed. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue.

[0040] When analyzing trends in a game, the analysis unit can improve the accuracy of the analysis by referring to the player's past performance data. For example, the analysis unit can refer to the player's past game data to predict his / her current performance. The analysis unit can also analyze the player's past scoring patterns to predict the scores for the current game. For example, the analysis unit can refer to the player's past match results to predict the trends for the current game. This makes it possible to improve the accuracy of the analysis by referring to the player's past performance data. For example, the analysis unit can refer to the player's past game data to predict his / her current performance. The analysis unit can also analyze the player's past scoring patterns to predict the scores for the current game.

[0041] When generating commentary, the generation unit can adjust the level of detail of the commentary based on the importance of the match. For example, the generation unit provides detailed commentary for important matches. The generation unit can also provide concise commentary for friendly matches. For example, the generation unit includes background information on players and tactical commentary for the final match. This makes it possible to adjust the level of detail of the commentary based on the importance of the match. For example, the generation unit can provide detailed commentary for important matches. The generation unit can also provide concise commentary for friendly matches.

[0042] When generating commentary, the generation unit can incorporate commentary styles of different sports to provide a variety of commentary. For example, the generation unit incorporates a basketball commentary style into a soccer game. The generation unit can also incorporate a tennis commentary style into a baseball game. For example, the generation unit incorporates an American football commentary style into a rugby game. This makes it possible to incorporate commentary styles of different sports to provide a variety of commentary. For example, a basketball commentary style can be incorporated into a soccer game. A tennis commentary style can be incorporated into a baseball game.

[0043] When generating commentary, the generation unit can incorporate personal information and background information about the player to increase the depth of the commentary. For example, the generation unit can include the player's past performance and career in the commentary. The generation unit can also include information about the player's training methods and daily life in the commentary. For example, the generation unit can include interviews and comments about the player in the commentary. This allows the generation unit to incorporate personal information and background information about the player to increase the depth of the commentary. For example, the player's past performance and career can be included in the commentary. The generation unit can also include information about the player's training methods and daily life in the commentary.

[0044] When generating commentary, the generation unit can determine the priority of commentary based on the progress of the match. For example, the generation unit can give priority to commentary on scoring scenes. The generation unit can also give priority to commentary on important plays and tactical moves. For example, the generation unit can give priority to commentary on the flow of the match. This makes it possible to determine the priority of commentary based on the progress of the match. For example, the generation unit can give priority to commentary on scoring scenes. The generation unit can also give priority to commentary on important plays and tactical moves.

[0045] The generation unit can provide explanations in different languages ​​when generating explanations. For example, the generation unit provides explanations in English. The generation unit can also provide explanations in Spanish. For example, the generation unit provides explanations in Japanese. This enables multilingual support by providing explanations in different languages. For example, the generation unit can provide explanations in English. The generation unit can also provide explanations in Spanish.

[0046] When generating an explanation, the generation unit can adjust the use of technical terms in the explanation according to the user's level of expertise. For example, the generation unit can provide an explanation that avoids technical terms for beginners. The generation unit can also provide an explanation that uses technical terms moderately for intermediate learners. For example, the generation unit can provide an explanation that uses a lot of technical terms for advanced learners. This makes it possible to adjust the use of technical terms in the explanation according to the user's level of expertise. For example, it is possible to provide an explanation that avoids technical terms for beginners. It is also possible to provide an explanation that uses technical terms moderately for intermediate learners.

[0047] During audio generation, the audio generation unit can emphasize audio in accordance with important moments in a match. For example, the audio generation unit emphasizes audio in scoring scenes. The audio generation unit can also emphasize audio in important plays. For example, the audio generation unit emphasizes audio in the climax of a match. This makes it possible to emphasize audio in accordance with important moments in a match. For example, the audio generation unit can emphasize audio in scoring scenes. The audio generation unit can also emphasize audio in important plays.

[0048] The voice generation unit can provide a variety of voices by incorporating different voice qualities and accents during voice generation. For example, the voice generation unit can provide voice with a male voice quality. The voice generation unit can also provide voice with a female voice quality. For example, the voice generation unit can provide voice with accents from different regions. This allows a variety of voices to be provided by incorporating different voice qualities and accents. For example, the voice generation unit can provide voice with a male voice quality. The voice generation unit can also provide voice with a female voice quality.

[0049] The audio generation unit can incorporate background sounds and sound effects during audio generation to increase the sense of realism. For example, the audio generation unit incorporates the cheers of spectators as background sounds. The audio generation unit can also incorporate sound effects of the game. For example, the audio generation unit incorporates environmental sounds of the game venue. This allows the incorporation of background sounds and sound effects to increase the sense of realism. For example, the cheers of spectators can be incorporated as background sounds. The sound effects of the game can also be incorporated.

[0050] The voice generation unit can provide optimal voice output by taking into consideration device information of the user when generating voice. For example, the voice generation unit can provide optimal voice output for playback on a smartphone. The voice generation unit can also provide optimal voice output for playback on a tablet. For example, the voice generation unit can provide optimal voice output for playback on a smart speaker. This makes it possible to provide optimal voice output by taking into consideration device information of the user. For example, it is possible to provide optimal voice output for playback on a smartphone. It is also possible to provide optimal voice output for playback on a tablet.

[0051] The voice generation unit can adjust the frequency of the voice according to the hearing characteristics of the user when generating the voice. For example, if the user has difficulty hearing high-pitched sounds, the voice generation unit can provide voice with an emphasis on the low-pitched sounds. Furthermore, if the user has difficulty hearing low-pitched sounds, the voice generation unit can also provide voice with an emphasis on the high-pitched sounds. For example, the voice generation unit adjusts the optimal frequency balance based on the hearing characteristics of the user. This makes it possible to adjust the frequency of the voice according to the hearing characteristics of the user. For example, if the user has difficulty hearing high-pitched sounds, the voice generation unit can provide voice with an emphasis on the low-pitched sounds. Furthermore, if the user has difficulty hearing low-pitched sounds, the voice generation unit can provide voice with an emphasis on the high-pitched sounds.

[0052] The voice generation unit can provide voice output in different languages ​​during voice generation. For example, the voice generation unit provides voice output in English. The voice generation unit can also provide voice output in Spanish. For example, the voice generation unit provides voice output in Japanese. This allows for multilingual support by providing voice output in different languages. For example, voice output can be provided in English. Voice output can also be provided in Spanish.

[0053] When accepting a question, the acceptance unit can select the optimal acceptance method by referring to the user's past question history. For example, the acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the acceptance unit predicts and suggests questions that will be asked in a specific time period based on the user's past question history. This makes it possible to select the optimal question acceptance method by referring to the user's past question history. For example, the acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past.

[0054] When receiving a question, the reception unit can filter the questions based on the user's current field of interest. For example, the reception unit preferentially receives questions about players in which the user is currently interested. The reception unit can also preferentially receive questions about the status of a game in which the user is currently interested. For example, the reception unit filters questions related to the user's field of interest based on the user's past search history. This makes it possible to filter questions based on the user's current field of interest. For example, it is possible to preferentially receive questions about players in which the user is currently interested. It is also possible to preferentially receive questions about the status of a game in which the user is currently interested.

[0055] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question in text, the acceptance unit can also accept the question using text analysis technology. For example, when the user inputs a question using an image, the acceptance unit accepts the question using image recognition technology. This makes it possible to select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the question can be accepted using voice recognition technology. Furthermore, when the user inputs a question in text, the question can be accepted using text analysis technology.

[0056] When accepting questions, the acceptance unit can prioritize accepting highly relevant questions in consideration of the user's geographical location information. For example, when the user is at a game venue, the acceptance unit can prioritize accepting questions about the game. Furthermore, when the user is at home, the acceptance unit can also prioritize accepting questions about the television broadcast of the game. For example, when the user is traveling, the acceptance unit can prioritize accepting questions about the radio broadcast of the game. This makes it possible to prioritize accepting highly relevant questions in consideration of the user's geographical location information. For example, when the user is at a game venue, the acceptance unit can prioritize accepting questions about the game. Furthermore, when the user is at home, the acceptance unit can prioritize accepting questions about the television broadcast of the game.

[0057] When accepting a question, the acceptance unit can analyze the user's social media activity and accept related questions. For example, the acceptance unit can preferentially accept questions about players that the user is talking about on social media. The acceptance unit can also preferentially accept questions about games that the user has shared on social media. For example, the acceptance unit accepts related questions by referring to the activity of the user's friends on social media. This makes it possible to analyze the user's social media activity and accept related questions. For example, it can preferentially accept questions about players that the user is talking about on social media. It can also preferentially accept questions about games that the user has shared on social media.

[0058] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit can give priority to using reception methods that have been well-received by users in the past. The reception unit can also avoid reception methods that have caused dissatisfaction by users in the past. For example, the reception unit can suggest the optimal reception method based on the user's past feedback. This makes it possible to customize the reception method by reflecting the user's past feedback. For example, the reception unit can give priority to using reception methods that have been well-received by users in the past. The reception unit can also avoid reception methods that have caused dissatisfaction by users in the past.

[0059] When generating an answer, the answer generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the answer generation unit provides a detailed answer to an important question. The answer generation unit can also provide a concise answer to a general question. For example, the answer generation unit provides a quick answer to a question with a high degree of urgency. This makes it possible to adjust the level of detail of the answer depending on the importance of the question. For example, a detailed answer can be provided to an important question. A concise answer can be provided to a general question.

[0060] When generating an answer, the answer generation unit can apply different answer algorithms depending on the category of the question. For example, the answer generation unit can generate an answer by referring to a player database for a question about players. The answer generation unit can also generate an answer by referring to a rule book for a question about the rules of a match. For example, the answer generation unit generates an answer by applying a tactical analysis algorithm for a question about the tactics of a match. This makes it possible to apply different answer algorithms depending on the category of the question. For example, the answer can be generated by referring to a player database for a question about players. The answer can be generated by referring to a rule book for a question about the rules of a match.

[0061] When generating an answer, the answer generation unit can improve the accuracy of the answer by referring to the user's past answer results. The answer generation unit, for example, refers to answers that the user was satisfied with in the past, and provides a highly accurate answer to a similar question. The answer generation unit can also provide a highly accurate answer by avoiding answers that the user was dissatisfied with in the past. For example, the answer generation unit analyzes the user's past answer history and provides an optimal answer. This makes it possible to improve the accuracy of the answer by referring to the user's past answer results. For example, it is possible to provide a highly accurate answer to a similar question by referring to answers that the user was satisfied with in the past. It is also possible to provide a highly accurate answer by avoiding answers that the user was dissatisfied with in the past.

[0062] When generating answers, the answer generation unit can determine the priority of answers based on the time when the questions were submitted. The answer generation unit, for example, provides quick answers to questions asked during a match. The answer generation unit can also provide detailed answers to questions asked after a match. For example, the answer generation unit provides answers based on predictions to questions asked before a match. This makes it possible to determine the priority of answers based on the time when the questions were submitted. For example, it is possible to provide quick answers to questions asked during a match. It is also possible to provide detailed answers to questions asked after a match.

[0063] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit provides answers preferentially to questions directly related to the match. Furthermore, the answer generation unit can also provide answers later to general questions. For example, the answer generation unit provides answers quickly to questions with high urgency. This makes it possible to adjust the order of answers based on the relevance of the questions. For example, answers can be provided preferentially to questions directly related to the match. Furthermore, answers can be provided later to general questions.

[0064] When generating an answer, the answer generation unit can adjust the use of technical terminology in the answer according to the user's level of expertise. For example, the answer generation unit provides an answer that avoids technical terminology for beginners. The answer generation unit can also provide an answer that uses technical terminology moderately for intermediate learners. For example, the answer generation unit provides an answer that uses a lot of technical terminology for advanced learners. This makes it possible to adjust the use of technical terminology in the answer according to the user's level of expertise. For example, it is possible to provide an answer that avoids technical terminology for beginners. It is also possible to provide an answer that uses technical terminology moderately for intermediate learners.

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

[0066] When analyzing the trends of a match, the analysis unit can estimate the psychological state of a player and reflect this in the analysis results. For example, the analysis unit can analyze the player's facial expressions and movements to estimate the psychological state. It can also refer to the player's past interviews and comments to estimate the psychological state. For example, the analysis unit can analyze the player's behavioral patterns during a match to estimate the psychological state. This allows the player's psychological state to be reflected in the analysis results, making for more accurate analysis. For example, the analysis unit can analyze the player's facial expressions and movements to estimate the psychological state. It can also refer to the player's past interviews and comments to estimate the psychological state.

[0067] When generating commentary, the generation unit can incorporate commentary styles of different sports to provide a variety of commentary. For example, a basketball commentary style can be incorporated into a soccer game. The generation unit can also incorporate a tennis commentary style into a baseball game. For example, an American football commentary style can be incorporated into a rugby game. This makes it possible to incorporate commentary styles of different sports to provide a variety of commentary. For example, a basketball commentary style can be incorporated into a soccer game. And a tennis commentary style can be incorporated into a baseball game.

[0068] When analyzing trends in a match, the analysis unit can incorporate spectator reaction data to improve the accuracy of the analysis. For example, it can analyze the volume of spectator cheers and applause to reflect the excitement of the match. It can also analyze spectator social media posts to identify key points in the match. For example, it can analyze spectator reactions in real time and reflect them in the trends of the match. This allows spectator reaction data to be incorporated to improve the accuracy of the analysis. For example, it can analyze the volume of spectator cheers and applause to reflect the excitement of the match. It can also analyze spectator social media posts to identify key points in the match.

[0069] When analyzing the trends of a match, the analysis unit can perform analysis based on weather or environmental conditions. For example, weather data at the match venue can be acquired and the impact on player performance can be analyzed. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue. For example, the analysis unit can analyze the movement of the ball by taking into account the wind speed and direction at the match venue. This makes it possible to perform analysis by taking into account weather and environmental conditions. For example, weather data at the match venue can be acquired and the impact on player performance can be analyzed. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue.

[0070] When analyzing trends in a match, the analysis unit can improve the accuracy of the analysis based on the player's physical strength or fatigue level. For example, the analysis unit can acquire the player's real-time heart rate data and reflect the fatigue level in the analysis. It can also refer to the player's past match data and reflect the physical strength consumption pattern in the analysis. For example, it can analyze the speed and frequency of the player's movements during a match and estimate the fatigue level. This makes it possible to improve the accuracy of the analysis by taking the player's physical strength and fatigue level into consideration. For example, the analysis unit can acquire the player's real-time heart rate data and reflect the fatigue level in the analysis. It can also refer to the player's past match data and reflect the physical strength consumption pattern in the analysis.

[0071] When generating commentary, the generation unit can incorporate personal information and background information about the player to increase the depth of the commentary. For example, the commentary can include the player's past performance and career. Information about the player's training methods and daily life can also be included in the commentary. For example, the commentary can include interviews and comments about the player. This allows the commentary to incorporate personal information and background information about the player to increase the depth of the commentary. For example, the commentary can include the player's past performance and career. Information about the player's training methods and daily life can also be included in the commentary.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The analysis unit analyzes the trends of the game. The trends of the game include, for example, the progress of the game, the movements of the players, and fluctuations in the score. The analysis unit grasps the progress of the game in real time, analyzes the movements of the players, and grasps fluctuations in the score. Step 2: The generation unit generates a commentary based on the results of the analysis by the analysis unit. The commentary includes, for example, detailed commentary on the scoring scene and the player's play. The generation unit provides detailed commentary on the scoring scene and analyzes the player's play. Step 3: The voice generation unit voices the commentary generated by the generation unit. For example, voice synthesis technology is used for voice generation. The voice generation unit voices the generated commentary in real time and provides it in high-quality voice. Step 4: The reception unit receives a question from the user. The question may be, for example, a voice question or a text question. The reception unit receives a voice question and a text question from the user. Step 5: The answer generation unit generates an answer to the question received by the reception unit. The answer includes, for example, a detailed answer and a concise answer. The answer generation unit generates a detailed answer and a concise answer to the user's question.

[0074] (Example 2) A sports commentary system according to an embodiment of the present invention allows visually impaired people to share in the excitement of a game and enhance their sports experience. The sports commentary system utilizes a generative AI to provide audio commentary services for sports games. The generative AI instantly analyzes game trends and results and generates detailed audio commentary in real time. This helps visually impaired people understand the game in a richer, more user-friendly way. For example, the sports commentary system grasps the progress of the game, players' movements, and score fluctuations in real time. Then, based on the analysis results, it generates and audio-generates detailed commentary. For example, it provides commentary on scoring scenes and player plays. In this case, it imitates the voices of multiple announcers with different perspectives and commentary styles, providing a richer sports experience for visually impaired people. Furthermore, the sports commentary system utilizes the natural language processing capabilities of AI to allow users to freely ask questions about the game. For example, the generative AI instantly answers questions such as, "Who scored that play?" or "What is this player's past performance?" This allows users to gain a deeper understanding of specific moments and plays in the game. This allows the sports commentary system to share the excitement of the game with the visually impaired and improve the sports experience. For example, a visually impaired person can listen to the commentary and understand the details of the goals scored and the players' plays. In addition, users can ask questions about the game, which deepens their understanding of the game and provides a richer sports experience.

[0075] A sports commentary system according to an embodiment includes an analysis unit, a generation unit, a voice generation unit, a reception unit, and an answer generation unit. The analysis unit analyzes the progress of a game. The progress of a game includes, but is not limited to, the progress of the game, player movements, and score fluctuations. For example, the analysis unit grasps the progress of the game in real time. The analysis unit can also analyze player movements and grasp score fluctuations. For example, the analysis unit monitors the progress of the game in real time and analyzes player movements. The generation unit generates commentary based on the results of the analysis by the analysis unit. The commentary includes, but is not limited to, detailed commentary on scoring scenes and player plays. For example, the generation unit provides detailed commentary on scoring scenes. The generation unit can also generate commentary on player plays. For example, the generation unit provides detailed commentary on scoring scenes and analyzes player plays. The voice generation unit converts the commentary generated by the generation unit into voice. For example, speech synthesis technology is used for the voice generation, but is not limited to, the example. The voice generation unit, for example, voices the generated commentary in real time. The voice generation unit can also provide the generated commentary in high-quality voice. For example, the voice generation unit voices the generated commentary in real time and provides it in high-quality voice. The reception unit receives questions from the user. Questions include, for example, voice questions and text questions, but are not limited to these examples. The reception unit, for example, receives voice questions from the user. The reception unit can also receive text questions from the user. For example, the reception unit receives voice questions and text questions from the user. The answer generation unit generates answers to the questions received by the reception unit. Answers include, for example, detailed answers and concise answers, but are not limited to these examples. The answer generation unit, for example, generates detailed answers to the user's questions. The answer generation unit can also generate concise answers to the user's questions. For example, the answer generation unit generates detailed answers and concise answers to the user's questions. This allows the sports commentary system according to the embodiment to allow visually impaired people to share in the excitement of the game and improve the sports experience.For example, visually impaired people can listen to the commentary of a game and understand the details of the goals scored and the players' plays. Users can also ask questions about the game, deepening their understanding of the game and providing a richer sports experience.

[0076] The generation unit can imitate multiple announcers with different perspectives or commentary styles. For example, the generation unit generates commentary by imitating the perspective of the attacking team. The generation unit can also generate commentary by imitating the perspective of the defending team. For example, the generation unit generates commentary by imitating the perspective of a spectator. The generation unit can also generate commentary by imitating a technical commentary style. For example, the generation unit can generate commentary by imitating an emotional commentary style. The generation unit can also generate commentary by imitating a humorous commentary style. This can provide a richer sports experience for visually impaired people. For example, visually impaired people can enjoy different perspectives and commentary styles. Furthermore, visually impaired people can understand the details of a game from multiple angles.

[0077] The answer generation unit can generate detailed answers to user questions. The answer generation unit, for example, provides specific data in response to the user questions. The answer generation unit can also provide examples in response to the user questions. For example, the answer generation unit provides background information in response to the user questions. The answer generation unit can also provide detailed explanations in response to the user questions. For example, the answer generation unit provides specific data and examples in response to the user questions. This allows the user to obtain detailed answers to questions about a game. For example, the user can gain a deeper understanding of specific moments or plays in a game. The user can also learn background information and detailed data about the game.

[0078] The analysis unit can grasp the progress of the game, the movements of the players, and the fluctuations in the score in real time. The analysis unit, for example, grasps the passage of time in the game in real time. The analysis unit can also grasp the fluctuations in the score in real time. For example, the analysis unit grasps the movements of the players in real time. The analysis unit can also grasp the progress of the game in real time. For example, the analysis unit grasps the passage of time in the game in real time and the fluctuations in the score in real time. This makes it possible to grasp the trends of the game in real time. For example, the progress of the game and the movements of the players can be grasped in real time. The fluctuations in the score can be grasped in real time.

[0079] The generation unit can generate commentary on scoring scenes and player plays. For example, the generation unit provides detailed commentary on scoring scenes. The generation unit can also generate commentary on player plays. For example, the generation unit provides detailed commentary on scoring scenes and analyzes the player plays. The generation unit can also generate commentary on scoring scenes and player plays. For example, the generation unit provides detailed commentary on scoring scenes and analyzes the player plays. This makes it possible to provide detailed commentary on scoring scenes and player plays. For example, a visually impaired person can understand a scoring scene or a player's play in detail. Furthermore, a visually impaired person can share in the excitement of a game.

[0080] The vocalization unit can vocalize the generated commentary in real time. For example, the vocalization unit vocalizes the generated commentary in real time. Furthermore, the vocalization unit can also provide the generated commentary in high-quality audio. For example, the vocalization unit vocalizes the generated commentary in real time and provides it in high-quality audio. This allows the commentary to be vocalized in real time. For example, a visually impaired person can listen to commentary on a match in real time. Furthermore, the visually impaired person can share in the excitement of the match.

[0081] The analysis unit can estimate the user's emotions and adjust the priority of the analysis of the trends of the match based on the estimated user's emotions. For example, if the user is excited, the analysis unit can prioritize analyzing scoring scenes and important plays. Furthermore, if the user is relaxed, the analysis unit can also prioritize analyzing the overall flow of the match. For example, if the user is nervous, the analysis unit can prioritize analyzing player movements and tactical elements. This makes it possible to adjust the priority of the analysis of the trends of the match according to the user's emotions. For example, if the user is excited, the analysis can prioritize analyzing scoring scenes and important plays. Furthermore, if the user is relaxed, the analysis can prioritize analyzing the overall flow of the match.

[0082] When analyzing the progress of a match, the analysis unit can improve the accuracy of the analysis based on the player's physical strength or fatigue level. For example, the analysis unit acquires real-time heart rate data of the player and reflects the fatigue level in the analysis. The analysis unit can also refer to the player's past match data and reflect the physical strength consumption pattern in the analysis. For example, the analysis unit analyzes the speed and frequency of the player's movements during the match and estimates the fatigue level. This makes it possible to improve the accuracy of the analysis by taking the player's physical strength and fatigue level into consideration. For example, the analysis unit can acquire real-time heart rate data of the player and reflect the fatigue level in the analysis. Also, the analysis unit can refer to the player's past match data and reflect the physical strength consumption pattern in the analysis.

[0083] When analyzing trends in a match, the analysis unit can refer to past match data to predict the outcome of the current match. For example, the analysis unit refers to data from past matches involving the same matchups to predict the outcome of the current match. The analysis unit can also predict the outcome of the current match based on past performance data of players. For example, the analysis unit analyzes scoring patterns in past matches to predict the scores of the current match. This makes it possible to predict the outcome of the current match by referring to past match data. For example, it is possible to refer to data from past matches involving the same matchups to predict the outcome of the current match. It is also possible to predict the outcome of the current match based on past performance data of players.

[0084] The analysis unit can estimate the psychological state of a player when analyzing the trends of a match and reflect this in the analysis results. The analysis unit, for example, analyzes the player's facial expressions and movements to estimate the psychological state. The analysis unit can also refer to the player's past interviews and comments to estimate the psychological state. For example, the analysis unit analyzes the player's behavioral patterns during a match to estimate the psychological state. This allows the player's psychological state to be reflected in the analysis results, enabling more accurate analysis. For example, the analysis unit can analyze the player's facial expressions and movements to estimate the psychological state. The analysis unit can also refer to the player's past interviews and comments to estimate the psychological state.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is excited, the analysis unit can highlight and display important scenes. Furthermore, if the user is relaxed, the analysis unit can also display the overall flow in an easy-to-understand manner. For example, if the user is nervous, the analysis unit can display detailed data to give the user a sense of security. This makes it possible to adjust the display method of the analysis results according to the user's emotions. For example, if the user is excited, the analysis unit can highlight and display important scenes. Furthermore, if the user is relaxed, the analysis unit can display the overall flow in an easy-to-understand manner.

[0086] The analysis unit can incorporate spectator reaction data when analyzing trends in a match to improve the accuracy of the analysis. For example, the analysis unit can analyze the volume of spectator cheers and applause to reflect the excitement of the match. The analysis unit can also analyze spectator social media posts to identify key points in the match. For example, the analysis unit can analyze spectator reactions in real time and reflect these in the trends of the match. This allows the analysis unit to incorporate spectator reaction data to improve the accuracy of the analysis. For example, the analysis unit can analyze the volume of spectator cheers and applause to reflect the excitement of the match. The analysis unit can also analyze spectator social media posts to identify key points in the match.

[0087] The analysis unit can perform analysis based on weather or environmental conditions when analyzing the trends of a match. For example, the analysis unit acquires weather data at the match venue and analyzes the impact on player performance. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue. For example, the analysis unit analyzes the movement of the ball by taking into account the wind speed and wind direction at the match venue. This makes it possible to perform analysis by taking into account weather and environmental conditions. For example, weather data at the match venue can be acquired and the impact on player performance can be analyzed. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue.

[0088] When analyzing trends in a game, the analysis unit can improve the accuracy of the analysis by referring to the player's past performance data. For example, the analysis unit can refer to the player's past game data to predict his / her current performance. The analysis unit can also analyze the player's past scoring patterns to predict the scores for the current game. For example, the analysis unit can refer to the player's past match results to predict the trends for the current game. This makes it possible to improve the accuracy of the analysis by referring to the player's past performance data. For example, the analysis unit can refer to the player's past game data to predict his / her current performance. The analysis unit can also analyze the player's past scoring patterns to predict the scores for the current game.

[0089] The generation unit can estimate the user's emotions and adjust the manner in which the commentary is expressed based on the estimated user's emotions. For example, if the user is excited, the generation unit can provide the commentary using expressive expressions. Furthermore, if the user is relaxed, the generation unit can also provide the commentary in a calm tone. For example, if the user is nervous, the generation unit can provide the commentary using expressions that give the user a sense of security. This makes it possible to adjust the manner in which the commentary is expressed depending on the user's emotions. For example, if the user is excited, the generation unit can provide the commentary using expressive expressions. Furthermore, if the user is relaxed, the generation unit can provide the commentary in a calm tone.

[0090] When generating commentary, the generation unit can adjust the level of detail of the commentary based on the importance of the match. For example, the generation unit provides detailed commentary for important matches. The generation unit can also provide concise commentary for friendly matches. For example, the generation unit includes background information on players and tactical commentary for the final match. This makes it possible to adjust the level of detail of the commentary based on the importance of the match. For example, the generation unit can provide detailed commentary for important matches. The generation unit can also provide concise commentary for friendly matches.

[0091] When generating commentary, the generation unit can incorporate commentary styles of different sports to provide a variety of commentary. For example, the generation unit incorporates a basketball commentary style into a soccer game. The generation unit can also incorporate a tennis commentary style into a baseball game. For example, the generation unit incorporates an American football commentary style into a rugby game. This makes it possible to incorporate commentary styles of different sports to provide a variety of commentary. For example, a basketball commentary style can be incorporated into a soccer game. A tennis commentary style can be incorporated into a baseball game.

[0092] When generating commentary, the generation unit can incorporate personal information and background information about the player to increase the depth of the commentary. For example, the generation unit can include the player's past performance and career in the commentary. The generation unit can also include information about the player's training methods and daily life in the commentary. For example, the generation unit can include interviews and comments about the player in the commentary. This allows the generation unit to incorporate personal information and background information about the player to increase the depth of the commentary. For example, the player's past performance and career can be included in the commentary. The generation unit can also include information about the player's training methods and daily life in the commentary.

[0093] The generation unit can estimate the user's emotion and adjust the length of the commentary based on the estimated user's emotion. For example, if the user is excited, the generation unit can provide a short and to-the-point commentary. Also, if the user is relaxed, the generation unit can provide a detailed commentary. For example, if the user is nervous, the generation unit can provide a commentary that gives the user a sense of security. In this way, the length of the commentary can be adjusted according to the user's emotion. For example, if the user is excited, the generation unit can provide a short and to-the-point commentary. Also, if the user is relaxed, the generation unit can provide a detailed commentary.

[0094] When generating commentary, the generation unit can determine the priority of commentary based on the progress of the match. For example, the generation unit can give priority to commentary on scoring scenes. The generation unit can also give priority to commentary on important plays and tactical moves. For example, the generation unit can give priority to commentary on the flow of the match. This makes it possible to determine the priority of commentary based on the progress of the match. For example, the generation unit can give priority to commentary on scoring scenes. The generation unit can also give priority to commentary on important plays and tactical moves.

[0095] The generation unit can provide explanations in different languages ​​when generating explanations. For example, the generation unit provides explanations in English. The generation unit can also provide explanations in Spanish. For example, the generation unit provides explanations in Japanese. This enables multilingual support by providing explanations in different languages. For example, the generation unit can provide explanations in English. The generation unit can also provide explanations in Spanish.

[0096] When generating an explanation, the generation unit can adjust the use of technical terms in the explanation according to the user's level of expertise. For example, the generation unit can provide an explanation that avoids technical terms for beginners. The generation unit can also provide an explanation that uses technical terms moderately for intermediate learners. For example, the generation unit can provide an explanation that uses a lot of technical terms for advanced learners. This makes it possible to adjust the use of technical terms in the explanation according to the user's level of expertise. For example, it is possible to provide an explanation that avoids technical terms for beginners. It is also possible to provide an explanation that uses technical terms moderately for intermediate learners.

[0097] The voice generation unit can estimate the user's emotion and adjust the tone and speed of the voice based on the estimated user's emotion. For example, if the user is excited, the voice generation unit can provide voice in a fast tone. Also, if the user is relaxed, the voice generation unit can provide voice in a slow tone. For example, if the user is nervous, the voice generation unit can provide voice in a calm tone. In this way, the tone and speed of the voice can be adjusted according to the user's emotion. For example, if the user is excited, the voice can be provided in a fast tone. Also, if the user is relaxed, the voice can be provided in a slow tone.

[0098] During audio generation, the audio generation unit can emphasize audio in accordance with important moments in a match. For example, the audio generation unit emphasizes audio in scoring scenes. The audio generation unit can also emphasize audio in important plays. For example, the audio generation unit emphasizes audio in the climax of a match. This makes it possible to emphasize audio in accordance with important moments in a match. For example, the audio generation unit can emphasize audio in scoring scenes. The audio generation unit can also emphasize audio in important plays.

[0099] The voice generation unit can provide a variety of voices by incorporating different voice qualities and accents during voice generation. For example, the voice generation unit can provide voice with a male voice quality. The voice generation unit can also provide voice with a female voice quality. For example, the voice generation unit can provide voice with accents from different regions. This allows a variety of voices to be provided by incorporating different voice qualities and accents. For example, the voice generation unit can provide voice with a male voice quality. The voice generation unit can also provide voice with a female voice quality.

[0100] The audio generation unit can incorporate background sounds and sound effects during audio generation to increase the sense of realism. For example, the audio generation unit incorporates the cheers of spectators as background sounds. The audio generation unit can also incorporate sound effects of the game. For example, the audio generation unit incorporates environmental sounds of the game venue. This allows the incorporation of background sounds and sound effects to increase the sense of realism. For example, the cheers of spectators can be incorporated as background sounds. The sound effects of the game can also be incorporated.

[0101] The voice generation unit can estimate the user's emotion and adjust the volume of the voice based on the estimated user's emotion. For example, the voice generation unit can increase the volume of the voice when the user is excited. Also, the voice generation unit can decrease the volume of the voice when the user is relaxed. For example, the voice generation unit provides voice at an appropriate volume when the user is nervous. This makes it possible to adjust the volume of the voice according to the user's emotion. For example, the voice generation unit can increase the volume of the voice when the user is excited. Also, the voice generation unit can decrease the volume of the voice when the user is relaxed.

[0102] The voice generation unit can provide optimal voice output by taking into consideration device information of the user when generating voice. For example, the voice generation unit can provide optimal voice output for playback on a smartphone. The voice generation unit can also provide optimal voice output for playback on a tablet. For example, the voice generation unit can provide optimal voice output for playback on a smart speaker. This makes it possible to provide optimal voice output by taking into consideration device information of the user. For example, it is possible to provide optimal voice output for playback on a smartphone. It is also possible to provide optimal voice output for playback on a tablet.

[0103] The voice generation unit can adjust the frequency of the voice according to the hearing characteristics of the user when generating the voice. For example, if the user has difficulty hearing high-pitched sounds, the voice generation unit can provide voice with an emphasis on the low-pitched sounds. Furthermore, if the user has difficulty hearing low-pitched sounds, the voice generation unit can also provide voice with an emphasis on the high-pitched sounds. For example, the voice generation unit adjusts the optimal frequency balance based on the hearing characteristics of the user. This makes it possible to adjust the frequency of the voice according to the hearing characteristics of the user. For example, if the user has difficulty hearing high-pitched sounds, the voice generation unit can provide voice with an emphasis on the low-pitched sounds. Furthermore, if the user has difficulty hearing low-pitched sounds, the voice generation unit can provide voice with an emphasis on the high-pitched sounds.

[0104] The voice generation unit can provide voice output in different languages ​​during voice generation. For example, the voice generation unit provides voice output in English. The voice generation unit can also provide voice output in Spanish. For example, the voice generation unit provides voice output in Japanese. This allows for multilingual support by providing voice output in different languages. For example, voice output can be provided in English. Voice output can also be provided in Spanish.

[0105] The reception unit can estimate the user's emotions and adjust the method for receiving questions based on the estimated user's emotions. For example, if the user is excited, the reception unit can receive questions using a simple interface. Furthermore, if the user is relaxed, the reception unit can provide detailed input options. For example, if the user is nervous, the reception unit can receive questions using an interface that gives the user a sense of security. This makes it possible to adjust the method for receiving questions according to the user's emotions. For example, if the user is excited, the reception unit can receive questions using a simple interface. Furthermore, if the user is relaxed, the reception unit can provide detailed input options.

[0106] When accepting a question, the acceptance unit can select the optimal acceptance method by referring to the user's past question history. For example, the acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the acceptance unit predicts and suggests questions that will be asked in a specific time period based on the user's past question history. This makes it possible to select the optimal question acceptance method by referring to the user's past question history. For example, the acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past.

[0107] When receiving a question, the reception unit can filter the questions based on the user's current field of interest. For example, the reception unit preferentially receives questions about players in which the user is currently interested. The reception unit can also preferentially receive questions about the status of a game in which the user is currently interested. For example, the reception unit filters questions related to the user's field of interest based on the user's past search history. This makes it possible to filter questions based on the user's current field of interest. For example, it is possible to preferentially receive questions about players in which the user is currently interested. It is also possible to preferentially receive questions about the status of a game in which the user is currently interested.

[0108] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question in text, the acceptance unit can also accept the question using text analysis technology. For example, when the user inputs a question using an image, the acceptance unit accepts the question using image recognition technology. This makes it possible to select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the question can be accepted using voice recognition technology. Furthermore, when the user inputs a question in text, the question can be accepted using text analysis technology.

[0109] The reception unit can estimate the user's emotions and determine the priority of questions based on the estimated user's emotions. For example, when the user is excited, the reception unit prioritizes receiving important questions. Furthermore, when the user is relaxed, the reception unit can also receive all questions equally. For example, when the user is nervous, the reception unit prioritizes receiving questions that will give the user a sense of security. In this way, the priority of questions can be determined according to the user's emotions. For example, when the user is excited, the reception unit can prioritize receiving important questions. Furthermore, when the user is relaxed, the reception unit can also receive all questions equally.

[0110] When accepting questions, the acceptance unit can prioritize accepting highly relevant questions in consideration of the user's geographical location information. For example, when the user is at a game venue, the acceptance unit can prioritize accepting questions about the game. Furthermore, when the user is at home, the acceptance unit can also prioritize accepting questions about the television broadcast of the game. For example, when the user is traveling, the acceptance unit can prioritize accepting questions about the radio broadcast of the game. This makes it possible to prioritize accepting highly relevant questions in consideration of the user's geographical location information. For example, when the user is at a game venue, the acceptance unit can prioritize accepting questions about the game. Furthermore, when the user is at home, the acceptance unit can prioritize accepting questions about the television broadcast of the game.

[0111] When accepting a question, the acceptance unit can analyze the user's social media activity and accept related questions. For example, the acceptance unit can preferentially accept questions about players that the user is talking about on social media. The acceptance unit can also preferentially accept questions about games that the user has shared on social media. For example, the acceptance unit accepts related questions by referring to the activity of the user's friends on social media. This makes it possible to analyze the user's social media activity and accept related questions. For example, it can preferentially accept questions about players that the user is talking about on social media. It can also preferentially accept questions about games that the user has shared on social media.

[0112] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit can give priority to using reception methods that have been well-received by users in the past. The reception unit can also avoid reception methods that have caused dissatisfaction by users in the past. For example, the reception unit can suggest the optimal reception method based on the user's past feedback. This makes it possible to customize the reception method by reflecting the user's past feedback. For example, the reception unit can give priority to using reception methods that have been well-received by users in the past. The reception unit can also avoid reception methods that have caused dissatisfaction by users in the past.

[0113] The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is excited, the answer generation unit provides an answer using an emotional expression. Furthermore, if the user is relaxed, the answer generation unit can also provide an answer in a calm tone. For example, if the user is nervous, the answer generation unit provides an answer using an expression that gives a sense of security. This makes it possible to adjust the way the answer is expressed depending on the user's emotions. For example, if the user is excited, the answer can be provided using an emotional expression. Furthermore, if the user is relaxed, the answer can be provided in a calm tone.

[0114] When generating an answer, the answer generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the answer generation unit provides a detailed answer to an important question. The answer generation unit can also provide a concise answer to a general question. For example, the answer generation unit provides a quick answer to a question with a high degree of urgency. This makes it possible to adjust the level of detail of the answer depending on the importance of the question. For example, a detailed answer can be provided to an important question. A concise answer can be provided to a general question.

[0115] When generating an answer, the answer generation unit can apply different answer algorithms depending on the category of the question. For example, the answer generation unit can generate an answer by referring to a player database for a question about players. The answer generation unit can also generate an answer by referring to a rule book for a question about the rules of a match. For example, the answer generation unit generates an answer by applying a tactical analysis algorithm for a question about the tactics of a match. This makes it possible to apply different answer algorithms depending on the category of the question. For example, the answer can be generated by referring to a player database for a question about players. The answer can be generated by referring to a rule book for a question about the rules of a match.

[0116] When generating an answer, the answer generation unit can improve the accuracy of the answer by referring to the user's past answer results. The answer generation unit, for example, refers to answers that the user was satisfied with in the past, and provides a highly accurate answer to a similar question. The answer generation unit can also provide a highly accurate answer by avoiding answers that the user was dissatisfied with in the past. For example, the answer generation unit analyzes the user's past answer history and provides an optimal answer. This makes it possible to improve the accuracy of the answer by referring to the user's past answer results. For example, it is possible to provide a highly accurate answer to a similar question by referring to answers that the user was satisfied with in the past. It is also possible to provide a highly accurate answer by avoiding answers that the user was dissatisfied with in the past.

[0117] The answer generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is excited, the answer generation unit provides a short and to-the-point answer. Furthermore, if the user is relaxed, the answer generation unit can also provide a detailed answer. For example, if the user is nervous, the answer generation unit provides an answer that gives the user a sense of security. This makes it possible to adjust the length of the answer according to the user's emotions. For example, if the user is excited, the answer can be short and to-the-point. Furthermore, if the user is relaxed, the answer can be detailed.

[0118] When generating answers, the answer generation unit can determine the priority of answers based on the time when the questions were submitted. The answer generation unit, for example, provides quick answers to questions asked during a match. The answer generation unit can also provide detailed answers to questions asked after a match. For example, the answer generation unit provides answers based on predictions to questions asked before a match. This makes it possible to determine the priority of answers based on the time when the questions were submitted. For example, it is possible to provide quick answers to questions asked during a match. It is also possible to provide detailed answers to questions asked after a match.

[0119] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit provides answers preferentially to questions directly related to the match. Furthermore, the answer generation unit can also provide answers later to general questions. For example, the answer generation unit provides answers quickly to questions with high urgency. This makes it possible to adjust the order of answers based on the relevance of the questions. For example, answers can be provided preferentially to questions directly related to the match. Furthermore, answers can be provided later to general questions.

[0120] When generating an answer, the answer generation unit can adjust the use of technical terminology in the answer according to the user's level of expertise. For example, the answer generation unit provides an answer that avoids technical terminology for beginners. The answer generation unit can also provide an answer that uses technical terminology moderately for intermediate learners. For example, the answer generation unit provides an answer that uses a lot of technical terminology for advanced learners. This makes it possible to adjust the use of technical terminology in the answer according to the user's level of expertise. For example, it is possible to provide an answer that avoids technical terminology for beginners. It is also possible to provide an answer that uses technical terminology moderately for intermediate learners. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, voice generation unit, reception unit, and answer generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the voice generation unit is realized by the control unit 46A of the smart device 14. For example, the reception unit is realized by the control unit 46A of the smart device 14. For example, the answer generation unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, voice generation unit, reception unit, and answer generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the voice generation unit is realized by the control unit 46A of the smart glasses 214. For example, the reception unit is realized by the control unit 46A of the smart glasses 214. For example, the answer generation unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, voice generation unit, reception unit, and answer generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the voice generation unit is realized by the control unit 46A of the headset type terminal 314. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314. For example, the answer generation unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, voice generation unit, reception unit, and answer generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the voice generation unit is realized by the control unit 46A of the robot 414. For example, the reception unit is realized by the control unit 46A of the robot 414. For example, the answer generation unit is realized by the specific processing unit 290 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] When analyzing the trends of a match, the analysis unit can estimate the psychological state of a player and reflect this in the analysis results. For example, the analysis unit can analyze the player's facial expressions and movements to estimate the psychological state. It can also refer to the player's past interviews and comments to estimate the psychological state. For example, the analysis unit can analyze the player's behavioral patterns during a match to estimate the psychological state. This allows the player's psychological state to be reflected in the analysis results, making for more accurate analysis. For example, the analysis unit can analyze the player's facial expressions and movements to estimate the psychological state. It can also refer to the player's past interviews and comments to estimate the psychological state.

[0123] When generating commentary, the generation unit can incorporate commentary styles of different sports to provide a variety of commentary. For example, a basketball commentary style can be incorporated into a soccer game. The generation unit can also incorporate a tennis commentary style into a baseball game. For example, an American football commentary style can be incorporated into a rugby game. This makes it possible to incorporate commentary styles of different sports to provide a variety of commentary. For example, a basketball commentary style can be incorporated into a soccer game. And a tennis commentary style can be incorporated into a baseball game.

[0124] When analyzing trends in a match, the analysis unit can incorporate spectator reaction data to improve the accuracy of the analysis. For example, it can analyze the volume of spectator cheers and applause to reflect the excitement of the match. It can also analyze spectator social media posts to identify key points in the match. For example, it can analyze spectator reactions in real time and reflect them in the trends of the match. This allows spectator reaction data to be incorporated to improve the accuracy of the analysis. For example, it can analyze the volume of spectator cheers and applause to reflect the excitement of the match. It can also analyze spectator social media posts to identify key points in the match.

[0125] The generation unit can estimate the user's emotions and adjust the way in which the commentary is expressed based on the estimated user's emotions. For example, if the user is excited, the commentary can be given in an emotional expression. Also, if the user is relaxed, the commentary can be given in a calm tone. For example, if the user is nervous, the commentary can be given in an expression that gives a sense of security. This makes it possible to adjust the way in which the commentary is expressed depending on the user's emotions. For example, if the user is excited, the commentary can be given in an emotional expression. Also, if the user is relaxed, the commentary can be given in a calm tone.

[0126] The voice generation unit can estimate the user's emotion and adjust the tone and speed of the voice based on the estimated user's emotion. For example, if the user is excited, the voice can be provided in a fast tone. Also, if the user is relaxed, the voice can be provided in a slow tone. For example, if the user is nervous, the voice can be provided in a calm tone. In this way, the tone and speed of the voice can be adjusted according to the user's emotion. For example, if the user is excited, the voice can be provided in a fast tone. Also, if the user is relaxed, the voice can be provided in a slow tone.

[0127] When analyzing the trends of a match, the analysis unit can perform analysis based on weather or environmental conditions. For example, weather data at the match venue can be acquired and the impact on player performance can be analyzed. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue. For example, the analysis unit can analyze the movement of the ball by taking into account the wind speed and direction at the match venue. This makes it possible to perform analysis by taking into account weather and environmental conditions. For example, weather data at the match venue can be acquired and the impact on player performance can be analyzed. The analysis unit can also analyze the physical exhaustion of players by taking into account the temperature and humidity at the match venue.

[0128] The reception unit can estimate the user's emotions and adjust the method for receiving questions based on the estimated user's emotions. For example, if the user is excited, questions can be received using a simple interface. Also, if the user is relaxed, detailed input options can be provided. For example, if the user is nervous, questions can be received using an interface that gives a sense of security. In this way, the method for receiving questions can be adjusted according to the user's emotions. For example, if the user is excited, questions can be received using a simple interface. Also, if the user is relaxed, detailed input options can be provided.

[0129] The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is excited, the answer can be provided in an emotionally rich expression. Also, if the user is relaxed, the answer can be provided in a calm tone. For example, if the user is nervous, the answer can be provided in an expression that gives a sense of security. In this way, the way the answer is expressed can be adjusted according to the user's emotions. For example, if the user is excited, the answer can be provided in an emotionally rich expression. Also, if the user is relaxed, the answer can be provided in a calm tone.

[0130] When analyzing trends in a match, the analysis unit can improve the accuracy of the analysis based on the player's physical strength or fatigue level. For example, the analysis unit can acquire the player's real-time heart rate data and reflect the fatigue level in the analysis. It can also refer to the player's past match data and reflect the physical strength consumption pattern in the analysis. For example, it can analyze the speed and frequency of the player's movements during a match and estimate the fatigue level. This makes it possible to improve the accuracy of the analysis by taking the player's physical strength and fatigue level into consideration. For example, the analysis unit can acquire the player's real-time heart rate data and reflect the fatigue level in the analysis. It can also refer to the player's past match data and reflect the physical strength consumption pattern in the analysis.

[0131] When generating commentary, the generation unit can incorporate personal information and background information about the player to increase the depth of the commentary. For example, the commentary can include the player's past performance and career. Information about the player's training methods and daily life can also be included in the commentary. For example, the commentary can include interviews and comments about the player. This allows the commentary to incorporate personal information and background information about the player to increase the depth of the commentary. For example, the commentary can include the player's past performance and career. Information about the player's training methods and daily life can also be included in the commentary.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The analysis unit analyzes the trends of the game. The trends of the game include, for example, the progress of the game, the movements of the players, and fluctuations in the score. The analysis unit grasps the progress of the game in real time, analyzes the movements of the players, and grasps fluctuations in the score. Step 2: The generation unit generates a commentary based on the results of the analysis by the analysis unit. The commentary includes, for example, detailed commentary on the scoring scene and the player's play. The generation unit provides detailed commentary on the scoring scene and analyzes the player's play. Step 3: The voice generation unit voices the commentary generated by the generation unit. For example, voice synthesis technology is used for voice generation. The voice generation unit voices the generated commentary in real time and provides it in high-quality voice. Step 4: The reception unit receives a question from the user. The question may be, for example, a voice question or a text question. The reception unit receives a voice question and a text question from the user. Step 5: The answer generation unit generates an answer to the question received by the reception unit. The answer includes, for example, a detailed answer and a concise answer. The answer generation unit generates a detailed answer and a concise answer to the user's question.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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, in order to avoid confusion and to 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis department that analyzes the trends of the match, a generation unit that generates a commentary based on the result of the analysis by the analysis unit; a voice generation unit that voices the commentary generated by the generation unit; a reception unit that receives questions from users; an answer generation unit that generates an answer to the question received by the reception unit; A system characterized by:

2. The generation unit Imitate multiple announcers with different perspectives or commentary styles 2. The system of claim 1.

3. The answer generation unit Generate detailed answers to user questions 2. The system of claim 1.

4. The analysis unit Understand the progress of the game, player movements, and score fluctuations in real time 2. The system of claim 1.

5. The generation unit Generate commentary on goals and player plays 2. The system of claim 1.

6. The voice conversion unit Real-time audio translation of generated commentary 2. The system of claim 1.

7. The analysis unit Estimate user emotions and adjust the priority of game trend analysis based on the estimated user emotions.

2. The system of claim 1.

8. The analysis unit Improve the accuracy of analysis of the progress of a match based on the physical strength or fatigue level of the players.

2. The system of claim 1.

Images