system

Abstract

A system according to an embodiment aims to provide a viewer with a personalized viewing experience.SOLUTION: A system includes an SNS cooperation part, an explanation generation part, and a highlight generation part. The SNS cooperation unit collects SNS comments of viewers in real time. The explanation generation unit analyzes the progress of the game based on the comments collected by the SNS cooperation unit, and generates an explanation. The highlight generation unit automatically detects an important scene of the game based on the explanation generated by the explanation generation unit, and generates a highlight.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the drawback of lacking detailed real-time commentary and personalized highlights when watching sporting events.

[0005] The system according to the embodiment aims to provide a personalized viewing experience to the viewer. [Means for solving the problem]

[0006] The system according to the embodiment includes an SNS linking unit, a commentary generating unit, and a highlight generating unit. The SNS linking unit collects SNS comments from viewers in real time. The commentary generating unit analyzes the progress of the match based on the comments collected by the SNS linking unit and generates commentary. The highlight generating unit automatically detects important scenes in the match based on the commentary generated by the commentary generating unit and generates highlights. [Effects of the Invention]

[0007] Systems according to embodiments can provide viewers with a personalized viewing experience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The SpoLive system according to an embodiment of the present invention is a system that collects viewers' SNS comments in real time, generates commentary using a generation AI that analyzes the progress of the match, and automatically detects important scenes to generate highlights. As a result, the SpoLive system generates commentary and highlights in real time based on viewers' SNS comments, improving the viewing experience.

[0029] The sports live system according to the embodiment includes an SNS linking unit, a commentary generating unit, and a highlight generating unit. The SNS linking unit collects viewers' SNS comments in real time. For example, the SNS linking unit collects comments from SNS platforms such as Twitter and Facebook. The SNS linking unit can obtain data using an API and update the data every second. The SNS linking unit can analyze viewers' comments and extract information related to the progress of a match. The commentary generating unit analyzes the progress of a match based on the comments collected by the SNS linking unit and generates commentary. For example, the commentary generating unit uses a generation AI to analyze the match score, player movements, match time, etc., and provides detailed commentary. The commentary generating unit can also generate commentary using natural language generation technology. The commentary generating unit can also generate commentary using a template-based generation method. The highlight generating unit automatically detects important scenes in a match based on the commentary generated by the commentary generating unit and generates highlights. For example, the highlight generating unit automatically detects goal scenes, foul scenes, audience reactions, etc., and generates highlights. The highlight generation unit can also use generation AI to analyze game video data and event data to extract important scenes. The highlight generation unit can also customize highlights according to viewer preferences. This allows the SPOLIVE system according to the embodiment to generate commentary and highlights in real time based on viewers' SNS comments, improving the viewing experience. For example, viewers can participate in the game in real time and obtain detailed commentary and background information. They can also enjoy highlights tailored to their preferences.

[0030] The SNS integration unit can analyze viewers' SNS activity and provide relevant match information in real time based on hashtags and keywords. For example, the SNS integration unit can analyze viewers' SNS activity and provide relevant match information based on specific hashtags and keywords. For example, if a viewer uses a specific hashtag, match information related to that hashtag is displayed in real time. The SNS integration unit can also analyze SNS activity such as posting frequency, number of likes, and number of retweets. The SNS integration unit can also analyze the frequency of appearance of trending hashtags and specific keywords. This can improve the viewing experience by providing relevant match information based on the viewer's SNS activity.

[0031] The SNS linking unit can automatically display additional information that will interest viewers based on the content of SNS comments. For example, the SNS linking unit analyzes the content of SNS comments and automatically displays additional information that will interest viewers. For example, if a viewer comments on a particular player, the SNS linking unit can display interviews with that player or memorable scenes from the past. The SNS linking unit can also display additional information such as the player's profile, past match data, and related news. The SNS linking unit can also build a system that analyzes viewer comments and automatically displays related information. This can improve the viewing experience by automatically displaying additional information that will interest viewers.

[0032] The SNS integration unit adds a function that allows viewers to participate in voting and surveys in real time, and can provide elements that influence the progress of the match. The SNS integration unit adds a function that allows viewers to participate in voting and surveys in real time, for example. For example, viewers can vote on player performance during the match, and the results are displayed during the match. The SNS integration unit can also clarify voting options, survey questions, and tallying methods. The SNS integration unit can also provide elements that influence the progress of the match based on the viewer voting results. This allows viewers to participate in voting and surveys in real time, and can provide elements that influence the progress of the match.

[0033] The SNS integration unit can integrate comments and reactions across different SNS platforms, allowing viewers to view all reactions on a single screen. For example, the SNS integration unit can integrate comments and reactions across different SNS platforms and build a system that allows viewers to view all reactions on a single screen. For example, it can display comments from Twitter and Facebook on a single screen. The SNS integration unit can also use an API to obtain data from different SNS platforms and integrate the databases. The SNS integration unit can also display viewer comments and reactions in real time. This allows comments and reactions across different SNS platforms to be integrated, allowing viewers to view all reactions on a single screen.

[0034] The commentary generation unit can analyze player movements during a match in real time and provide tactical commentary. The commentary generation unit can, for example, analyze player movements during a match in real time and provide tactical commentary. For example, the commentary generation unit can analyze player positioning and movement patterns and reflect the results in the commentary. The commentary generation unit can also analyze player movements using motion capture technology and video analysis technology. The commentary generation unit can also clarify the type of tactics and the level of detail of the commentary. This allows the viewing experience to be improved by analyzing player movements during a match in real time and providing tactical commentary.

[0035] The commentary generation unit can provide commentary on predicted developments and tactics based on past match data and comparing it with the progress of the current match. The commentary generation unit, for example, provides commentary on predicted developments and tactics based on past match data and comparing it with the progress of the current match. For example, the commentary generation unit analyzes past match data and compares it with the progress of the current match. The commentary generation unit can also provide commentary on predicted developments and tactics using machine learning algorithms and statistical analysis. The commentary generation unit can also obtain past match data such as match scores, player statistics, and match highlights. This allows for a better viewing experience by comparing the progress of the current match with the past match data and providing commentary on predicted developments and tactics.

[0036] The commentary generation unit can add commentary functions corresponding to different sports events, enabling use in a wide range of sporting events. The commentary generation unit can add commentary functions corresponding to different sports events, enabling use in a wide range of sporting events. For example, commentary functions for soccer, basketball, baseball, etc. can be added. The commentary generation unit can also clarify the content and format of the commentary for each sporting event. The commentary generation unit can also provide commentary based on the tactics and rules of each sporting event. In this way, by adding commentary functions corresponding to different sports events, it is possible to enable use in a wide range of sporting events.

[0037] The commentary generation unit can automatically translate the commentary content into multiple languages ​​to accommodate an international audience. The commentary generation unit can, for example, automatically translate the commentary content into multiple languages ​​to accommodate an international audience. For example, automatic translation into languages ​​such as English, Spanish, and French is performed. The commentary generation unit can also translate the commentary content using a machine translation algorithm or a translation memory. The commentary generation unit can also use a dictionary of technical terms and phrases to improve translation accuracy. This allows the commentary content to be automatically translated into multiple languages ​​to accommodate an international audience.

[0038] The highlight generation unit can analyze viewer reactions during a match in real time and prioritize extract as highlights those scenes that have received particularly strong reactions. The highlight generation unit, for example, analyzes viewer reactions during a match in real time and prioritize extracts as highlights those scenes that have received particularly strong reactions. For example, it analyzes viewers' social media comments and reactions and selects scenes that have received many reactions as highlights. The highlight generation unit can also analyze viewer behavior data and comments to analyze viewer reactions in real time. The highlight generation unit can also identify important scenes based on viewer reactions and generate highlights. In this way, the viewing experience can be improved by analyzing viewer reactions in real time and priority extracting scenes that have received particularly strong reactions as highlights.

[0039] When generating highlights, the highlight generation unit can analyze biometric data such as the player's heart rate and amount of exercise, and select the most tense scene. When generating highlights, the highlight generation unit can, for example, analyze biometric data such as the player's heart rate and amount of exercise, and select the most tense scene. For example, the highlight generation unit can select a scene in which the player's heart rate suddenly rises as a highlight. The highlight generation unit can also obtain the player's biometric data using a wearable device and analyze it in real time. The highlight generation unit can also identify tense scenes based on fluctuations in the player's amount of exercise and heart rate. In this way, the viewing experience can be improved by analyzing the player's biometric data and selecting the most tense scene.

[0040] The highlight generation unit generates highlights that combine footage from different perspectives, thereby providing viewers with a new visual experience. The highlight generation unit generates highlights that combine footage from different perspectives, for example, thereby providing viewers with a new visual experience. For example, highlights are generated by combining footage from players' perspectives and drone footage. The highlight generation unit can also edit footage using multiple camera angles and combine footage from different perspectives. The highlight generation unit can also use video editing techniques to generate visually appealing highlights. This makes it possible to provide viewers with a new visual experience by generating highlights that combine footage from different perspectives.

[0041] The highlight generation unit may add a function that enables the highlights to be customized according to the viewer's preferences, thereby providing highlights that focus on specific players or plays. The highlight generation unit may add a function that enables the highlights to be customized according to the viewer's preferences, thereby providing highlights that focus on specific players or plays. For example, when a viewer selects a player, highlights are generated that focus on the plays of that player. The highlight generation unit may also analyze viewer preference data and provide customization options. The highlight generation unit may also edit highlights based on the viewer's preferences and focus on specific plays. In this way, by adding a function that enables the highlights to be customized according to the viewer's preferences, it is possible to provide highlights that focus on specific players or plays.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The commentary generation unit can analyze the movements of players during a match in real time and provide viewers with information about the players' fatigue levels and physical fitness. For example, the commentary generation unit can analyze the players' running distance and number of sprints to evaluate their fatigue levels. The commentary generation unit can also analyze the players' biometric data, such as their heart rate and breathing rate, to evaluate their physical fitness. Furthermore, the commentary generation unit can analyze the players' movement patterns and provide viewers with information about the players' fatigue levels and physical fitness. This allows viewers to understand the players' fatigue levels and physical fitness levels in real time during a match.

[0044] The highlight generation unit can automatically generate highlights of specific players based on the performance data of the players during the game. For example, it can automatically extract the player's scoring and assist scenes and generate them as highlights. The highlight generation unit can also analyze the player's performance data and generate highlights centered on the play of a specific player. Furthermore, the highlight generation unit can customize highlights of specific players according to the viewer's preferences. This allows viewers to enjoy highlights centered on the performance of a specific player.

[0045] The commentary generation unit can analyze the movements of players during a match in real time and provide the viewers with the tactical movements of the players. For example, the commentary generation unit can analyze the positioning and movement patterns of players and reflect the tactical movements in the commentary. The commentary generation unit can also analyze data on the movements of players and provide the viewers with the tactical movements. Furthermore, the commentary generation unit can also analyze the movement patterns of players and provide the viewers with the tactical movements. This allows the viewers to understand the tactical movements of players during a match in real time.

[0046] The highlight generation unit can automatically generate highlights of specific plays based on the performance data of players during a match. For example, it can automatically extract scenes of players scoring or making assists and generate them as highlights. The highlight generation unit can also analyze the performance data of players and generate highlights centered on specific plays. Furthermore, the highlight generation unit can customize highlights of specific plays according to the viewer's preferences. This allows viewers to enjoy highlights centered on specific plays.

[0047] The commentary generation unit can analyze the movements of players during a match in real time and provide viewers with information about the players' performance. For example, the commentary generation unit can analyze the player's running distance and number of sprints to evaluate their performance. The commentary generation unit can also analyze the player's biometric data, such as their heart rate and breathing rate, to evaluate their performance. Furthermore, the commentary generation unit can analyze the player's movement patterns and provide viewers with information about their performance. This allows viewers to understand the player's performance in real time during a match.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The social media integration unit collects viewers' social media comments in real time. For example, it collects comments from social media platforms such as Twitter and Facebook, retrieves data using APIs, and updates it every second. It also analyzes viewers' comments and extracts information related to the progress of the match. Step 2: The commentary generation unit analyzes the progress of the game based on the comments collected by the SNS integration unit and generates commentary. For example, it uses generation AI to analyze the game score, player movements, game time, etc., and provides detailed commentary. Commentary can also be generated using natural language generation technology or template-based generation methods. Step 3: The highlight generation unit automatically detects important scenes in the match based on the commentary generated by the commentary generation unit and generates highlights. For example, it automatically detects goal scenes, foul scenes, and audience reactions to generate highlights. It can also use the generation AI to analyze match video data and event data to extract important scenes. It can also customize highlights according to viewer preferences.

[0050] (Example 2) The SpoLive system according to an embodiment of the present invention is a system that collects viewers' SNS comments in real time, generates commentary using a generation AI that analyzes the progress of the match, and automatically detects important scenes to generate highlights. As a result, the SpoLive system generates commentary and highlights in real time based on viewers' SNS comments, improving the viewing experience.

[0051] The sports live system according to the embodiment includes an SNS linking unit, a commentary generating unit, and a highlight generating unit. The SNS linking unit collects viewers' SNS comments in real time. For example, the SNS linking unit collects comments from SNS platforms such as Twitter and Facebook. The SNS linking unit can obtain data using an API and update the data every second. The SNS linking unit can analyze viewers' comments and extract information related to the progress of a match. The commentary generating unit analyzes the progress of a match based on the comments collected by the SNS linking unit and generates commentary. For example, the commentary generating unit uses a generation AI to analyze the match score, player movements, match time, etc., and provides detailed commentary. The commentary generating unit can also generate commentary using natural language generation technology. The commentary generating unit can also generate commentary using a template-based generation method. The highlight generating unit automatically detects important scenes in a match based on the commentary generated by the commentary generating unit and generates highlights. For example, the highlight generating unit automatically detects goal scenes, foul scenes, audience reactions, etc., and generates highlights. The highlight generation unit can also use generation AI to analyze game video data and event data to extract important scenes. The highlight generation unit can also customize highlights according to viewer preferences. This allows the SPOLIVE system according to the embodiment to generate commentary and highlights in real time based on viewers' SNS comments, improving the viewing experience. For example, viewers can participate in the game in real time and obtain detailed commentary and background information. They can also enjoy highlights tailored to their preferences.

[0052] The SNS linking unit can perform sentiment analysis of SNS comments and prioritize displaying positive comments. For example, the SNS linking unit collects SNS comments in real time and performs sentiment analysis. For example, it calculates sentiment scores for comments using natural language processing technology and selects comments with high scores. The SNS linking unit can also analyze the frequency of appearance of positive words in order to prioritize displaying positive comments. The SNS linking unit can also classify the sentiment of comments using a machine learning algorithm. This can improve viewer engagement by prioritize displaying positive comments.

[0053] The SNS integration unit can analyze viewers' SNS activity and provide relevant match information in real time based on hashtags and keywords. For example, the SNS integration unit can analyze viewers' SNS activity and provide relevant match information based on specific hashtags and keywords. For example, if a viewer uses a specific hashtag, match information related to that hashtag is displayed in real time. The SNS integration unit can also analyze SNS activity such as posting frequency, number of likes, and number of retweets. The SNS integration unit can also analyze the frequency of appearance of trending hashtags and specific keywords. This can improve the viewing experience by providing relevant match information based on the viewer's SNS activity.

[0054] The SNS linking unit can automatically display additional information that will interest viewers based on the content of SNS comments. For example, the SNS linking unit analyzes the content of SNS comments and automatically displays additional information that will interest viewers. For example, if a viewer comments on a particular player, the SNS linking unit can display interviews with that player or memorable scenes from the past. The SNS linking unit can also display additional information such as the player's profile, past match data, and related news. The SNS linking unit can also build a system that analyzes viewer comments and automatically displays related information. This can improve the viewing experience by automatically displaying additional information that will interest viewers.

[0055] The SNS integration unit adds a function that allows viewers to participate in voting and surveys in real time, and can provide elements that influence the progress of the match. The SNS integration unit adds a function that allows viewers to participate in voting and surveys in real time, for example. For example, viewers can vote on player performance during the match, and the results are displayed during the match. The SNS integration unit can also clarify voting options, survey questions, and tallying methods. The SNS integration unit can also provide elements that influence the progress of the match based on the viewer voting results. This allows viewers to participate in voting and surveys in real time, and can provide elements that influence the progress of the match.

[0056] The SNS integration unit can integrate comments and reactions across different SNS platforms, allowing viewers to view all reactions on a single screen. For example, the SNS integration unit can integrate comments and reactions across different SNS platforms and build a system that allows viewers to view all reactions on a single screen. For example, it can display comments from Twitter and Facebook on a single screen. The SNS integration unit can also use an API to obtain data from different SNS platforms and integrate the databases. The SNS integration unit can also display viewer comments and reactions in real time. This allows comments and reactions across different SNS platforms to be integrated, allowing viewers to view all reactions on a single screen.

[0057] The SNS integration unit can use the emotion estimation function to automatically generate cheering messages and emotes according to the viewer's emotions and display them during the game. For example, the SNS integration unit can use the emotion estimation function to automatically generate cheering messages and emotes according to the viewer's emotions. For example, if a viewer is excited, a message such as "Amazing!" is displayed. The SNS integration unit can also estimate the viewer's emotions using machine learning algorithms and natural language processing technology. The SNS integration unit can also use standard phrases or generate customizable messages. This can improve the viewing experience by automatically generating cheering messages and emotes according to the viewer's emotions.

[0058] The commentary generation unit can analyze player movements during a match in real time and provide tactical commentary. The commentary generation unit can, for example, analyze player movements during a match in real time and provide tactical commentary. For example, the commentary generation unit can analyze player positioning and movement patterns and reflect the results in the commentary. The commentary generation unit can also analyze player movements using motion capture technology and video analysis technology. The commentary generation unit can also clarify the type of tactics and the level of detail of the commentary. This allows the viewing experience to be improved by analyzing player movements during a match in real time and providing tactical commentary.

[0059] The commentary generation unit can provide commentary on predicted developments and tactics based on past match data and comparing it with the progress of the current match. The commentary generation unit, for example, provides commentary on predicted developments and tactics based on past match data and comparing it with the progress of the current match. For example, the commentary generation unit analyzes past match data and compares it with the progress of the current match. The commentary generation unit can also provide commentary on predicted developments and tactics using machine learning algorithms and statistical analysis. The commentary generation unit can also obtain past match data such as match scores, player statistics, and match highlights. This allows for a better viewing experience by comparing the progress of the current match with the past match data and providing commentary on predicted developments and tactics.

[0060] The commentary generation unit can use the emotion estimation function to adjust the tone and content of the commentary according to the viewer's emotions, thereby providing a friendly commentary. The commentary generation unit, for example, uses the emotion estimation function to adjust the tone and content of the commentary according to the viewer's emotions. For example, if the viewer is excited, the tone of the commentary can be increased. The commentary generation unit can also change the tone based on the emotion score and adjust the content based on viewer feedback. The commentary generation unit can also estimate the viewer's emotions using machine learning algorithms and natural language processing technology. This makes it possible to provide a friendly commentary by adjusting the tone and content of the commentary according to the viewer's emotions.

[0061] The commentary generation unit can add commentary functions corresponding to different sports events, enabling use in a wide range of sporting events. The commentary generation unit can add commentary functions corresponding to different sports events, enabling use in a wide range of sporting events. For example, commentary functions for soccer, basketball, baseball, etc. can be added. The commentary generation unit can also clarify the content and format of the commentary for each sporting event. The commentary generation unit can also provide commentary based on the tactics and rules of each sporting event. In this way, by adding commentary functions corresponding to different sports events, it is possible to enable use in a wide range of sporting events.

[0062] The commentary generation unit can automatically translate the commentary content into multiple languages ​​to accommodate an international audience. The commentary generation unit can, for example, automatically translate the commentary content into multiple languages ​​to accommodate an international audience. For example, automatic translation into languages ​​such as English, Spanish, and French is performed. The commentary generation unit can also translate the commentary content using a machine translation algorithm or a translation memory. The commentary generation unit can also use a dictionary of technical terms and phrases to improve translation accuracy. This allows the commentary content to be automatically translated into multiple languages ​​to accommodate an international audience.

[0063] The commentary generation unit can use the emotion estimation function to provide commentary focusing on players and plays that are of particular interest to the viewer. For example, the commentary generation unit can use the emotion estimation function to provide commentary focusing on players and plays that are of particular interest to the viewer. For example, if a viewer shows strong emotions toward a particular player, detailed commentary about that player can be provided. The commentary generation unit can also analyze the viewer's comments and viewing history to identify players and plays that are of particular interest to the viewer. The commentary generation unit can also estimate the viewer's emotions using an emotion estimation algorithm. This can improve the viewing experience by providing commentary focusing on players and plays that are of particular interest to the viewer.

[0064] The highlight generation unit can analyze viewer reactions during a match in real time and prioritize extract as highlights those scenes that have received particularly strong reactions. The highlight generation unit, for example, analyzes viewer reactions during a match in real time and prioritize extracts as highlights those scenes that have received particularly strong reactions. For example, it analyzes viewers' social media comments and reactions and selects scenes that have received many reactions as highlights. The highlight generation unit can also analyze viewer behavior data and comments to analyze viewer reactions in real time. The highlight generation unit can also identify important scenes based on viewer reactions and generate highlights. In this way, the viewing experience can be improved by analyzing viewer reactions in real time and priority extracting scenes that have received particularly strong reactions as highlights.

[0065] When generating highlights, the highlight generation unit can analyze biometric data such as the player's heart rate and amount of exercise, and select the most tense scene. When generating highlights, the highlight generation unit can, for example, analyze biometric data such as the player's heart rate and amount of exercise, and select the most tense scene. For example, the highlight generation unit can select a scene in which the player's heart rate suddenly rises as a highlight. The highlight generation unit can also obtain the player's biometric data using a wearable device and analyze it in real time. The highlight generation unit can also identify tense scenes based on fluctuations in the player's amount of exercise and heart rate. In this way, the viewing experience can be improved by analyzing the player's biometric data and selecting the most tense scene.

[0066] The highlight generation unit can use the emotion estimation function to identify scenes that moved or excited the viewer and generate highlights based on the identified scenes. The highlight generation unit, for example, uses the emotion estimation function to identify scenes that moved or excited the viewer and generate highlights based on the identified scenes. For example, the highlight generation unit analyzes the viewer's emotion score and selects scenes that are highly moved or excited as highlights. The highlight generation unit can also identify moving or exciting scenes using viewer comment analysis and an emotion estimation algorithm. The highlight generation unit can also extract important scenes based on viewer emotion data and generate highlights. This allows the viewing experience to be improved by identifying scenes that moved or excited the viewer and generating highlights based on the identified scenes.

[0067] The highlight generation unit generates highlights that combine footage from different perspectives, thereby providing viewers with a new visual experience. The highlight generation unit generates highlights that combine footage from different perspectives, for example, thereby providing viewers with a new visual experience. For example, highlights are generated by combining footage from players' perspectives and drone footage. The highlight generation unit can also edit footage using multiple camera angles and combine footage from different perspectives. The highlight generation unit can also use video editing techniques to generate visually appealing highlights. This makes it possible to provide viewers with a new visual experience by generating highlights that combine footage from different perspectives.

[0068] The highlight generation unit may add a function that enables the highlights to be customized according to the viewer's preferences, thereby providing highlights that focus on specific players or plays. The highlight generation unit may add a function that enables the highlights to be customized according to the viewer's preferences, thereby providing highlights that focus on specific players or plays. For example, when a viewer selects a player, highlights are generated that focus on the plays of that player. The highlight generation unit may also analyze viewer preference data and provide customization options. The highlight generation unit may also edit highlights based on the viewer's preferences and focus on specific plays. In this way, by adding a function that enables the highlights to be customized according to the viewer's preferences, it is possible to provide highlights that focus on specific players or plays.

[0069] The highlight generation unit can use the emotion estimation function to automatically generate a special highlight collection based on the scenes that moved the viewer the most and provide it after the end of the match. The highlight generation unit can, for example, use the emotion estimation function to automatically generate a special highlight collection based on the scenes that moved the viewer the most and provide it after the end of the match. For example, the highlight generation unit can analyze the viewer's emotion score and generate a highlight collection that brings together scenes that moved the viewer the most. The highlight generation unit can also identify moving scenes using an analysis of viewer comments and an emotion estimation algorithm. The highlight generation unit can also compile a special highlight collection based on the viewer's emotion data and provide it after the end of the match. This can improve the viewing experience by automatically generating a special highlight collection based on the scenes that moved the viewer the most and providing it after the end of the match.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The SNS linkage unit can evaluate player performance during a match in real time based on viewers' SNS comments and provide feedback to viewers. For example, if viewers post many positive comments about a particular player, the player's performance rating can be displayed as high. The SNS linkage unit can also analyze viewers' comments and generate statistical data on player performance. Furthermore, the SNS linkage unit can evaluate player performance in real time based on viewer feedback and provide feedback to viewers. This allows viewers to evaluate player performance during a match and receive feedback in real time.

[0072] The commentary generation unit can analyze the movements of players during a match in real time and provide viewers with information about the players' fatigue levels and physical fitness. For example, the commentary generation unit can analyze the players' running distance and number of sprints to evaluate their fatigue levels. The commentary generation unit can also analyze the players' biometric data, such as their heart rate and breathing rate, to evaluate their physical fitness. Furthermore, the commentary generation unit can analyze the players' movement patterns and provide viewers with information about the players' fatigue levels and physical fitness. This allows viewers to understand the players' fatigue levels and physical fitness levels in real time during a match.

[0073] The highlight generation unit can automatically generate highlights of specific players based on the performance data of the players during the game. For example, it can automatically extract the player's scoring and assist scenes and generate them as highlights. The highlight generation unit can also analyze the player's performance data and generate highlights centered on the play of a specific player. Furthermore, the highlight generation unit can customize highlights of specific players according to the viewer's preferences. This allows viewers to enjoy highlights centered on the performance of a specific player.

[0074] The SNS linkage unit can estimate the motivation of players during a match based on viewers' SNS comments and provide feedback to viewers. For example, if viewers post many positive comments about a particular player, it can estimate that the player is highly motivated. The SNS linkage unit can also analyze viewers' comments and generate statistical data on players' motivation. Furthermore, the SNS linkage unit can estimate players' motivation in real time based on viewers' feedback and provide feedback to viewers. This allows viewers to estimate players' motivation during a match and receive feedback in real time.

[0075] The commentary generation unit can analyze the movements of players during a match in real time and provide the viewers with the tactical movements of the players. For example, the commentary generation unit can analyze the positioning and movement patterns of players and reflect the tactical movements in the commentary. The commentary generation unit can also analyze data on the movements of players and provide the viewers with the tactical movements. Furthermore, the commentary generation unit can also analyze the movement patterns of players and provide the viewers with the tactical movements. This allows the viewers to understand the tactical movements of players during a match in real time.

[0076] The SNS linkage unit can estimate the emotions of players during a game based on viewers' SNS comments and provide feedback to viewers. For example, if viewers post many positive comments about a particular player, it can estimate that the player is emotionally excited. The SNS linkage unit can also analyze viewers' comments and generate statistical data on players' emotions. Furthermore, the SNS linkage unit can estimate players' emotions in real time based on viewers' feedback and provide feedback to viewers. This allows viewers to estimate players' emotions during a game and receive feedback in real time.

[0077] The highlight generation unit can automatically generate highlights of specific plays based on the performance data of players during a match. For example, it can automatically extract scenes of players scoring or making assists and generate them as highlights. The highlight generation unit can also analyze the performance data of players and generate highlights centered on specific plays. Furthermore, the highlight generation unit can customize highlights of specific plays according to the viewer's preferences. This allows viewers to enjoy highlights centered on specific plays.

[0078] The SNS linkage unit can estimate the stress level of players during a match based on viewers' SNS comments and provide feedback to viewers. For example, if viewers post many negative comments about a particular player, it can estimate that the player has a high stress level. The SNS linkage unit can also analyze viewers' comments and generate statistical data on players' stress levels. Furthermore, the SNS linkage unit can estimate players' stress levels in real time based on viewers' feedback and provide feedback to viewers. This allows viewers to estimate players' stress levels during a match and receive feedback in real time.

[0079] The commentary generation unit can analyze the movements of players during a match in real time and provide viewers with information about the players' performance. For example, the commentary generation unit can analyze the player's running distance and number of sprints to evaluate their performance. The commentary generation unit can also analyze the player's biometric data, such as their heart rate and breathing rate, to evaluate their performance. Furthermore, the commentary generation unit can analyze the player's movement patterns and provide viewers with information about their performance. This allows viewers to understand the player's performance in real time during a match.

[0080] The highlight generation unit can analyze viewers' emotions during a match in real time and prioritize extracting particularly emotional scenes as highlights. For example, it can analyze viewers' social media comments and reactions and select emotionally charged scenes as highlights. The highlight generation unit can also analyze viewers' behavioral data and comments to analyze emotionally charged scenes in real time. Furthermore, the highlight generation unit can identify important scenes based on viewers' emotions and generate highlights. This allows for an improved viewing experience by analyzing viewers' emotions in real time and priority extraction of particularly emotionally charged scenes as highlights.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The social media integration unit collects viewers' social media comments in real time. For example, it collects comments from social media platforms such as Twitter and Facebook, retrieves data using APIs, and updates it every second. It also analyzes viewers' comments and extracts information related to the progress of the match. Step 2: The commentary generation unit analyzes the progress of the game based on the comments collected by the SNS integration unit and generates commentary. For example, it uses generation AI to analyze the game score, player movements, game time, etc., and provides detailed commentary. Commentary can also be generated using natural language generation technology or template-based generation methods. Step 3: The highlight generation unit automatically detects important scenes in the match based on the commentary generated by the commentary generation unit and generates highlights. For example, it automatically detects goal scenes, foul scenes, and audience reactions to generate highlights. It can also use the generation AI to analyze match video data and event data to extract important scenes. It can also customize highlights according to viewer preferences.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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. The SNS Collaboration Department collects viewers' SNS comments in real time, a commentary generation unit that analyzes the progress of the game based on the comments collected by the SNS linkage unit and generates commentary; a highlight generation unit that automatically detects important scenes in a match based on the commentary generated by the commentary generation unit and generates highlights. A system characterized by:

2. The SNS linking unit is Analyze the sentiment of the social media comments and prioritize positive comments.

2. The system of claim 1.

3. The SNS linking unit is Adding a feature that allows the viewers to participate in real-time voting and surveys, providing an element that influences the progress of the match 2. The system of claim 1.

4. The explanation generation unit Analyzing the movements of the players during the match in real time and providing tactical commentary 2. The system of claim 1.

5. The explanation generation unit Adding commentary features for different sports will enable use at a wide range of sporting events 2. The system of claim 1.

6. The highlight generation unit The reactions of the viewers during the game are analyzed in real time, and the scenes that have generated particularly large reactions are preferentially extracted as the highlights.

2. The system of claim 1.

7. The highlight generation unit The highlight is generated by combining images from different viewpoints, providing the viewer with a new visual experience.

2. The system of claim 1.

8. The SNS linking unit is Automatically generate cheering messages and emotes according to the emotions of the viewers and display them during the game.

2. The system of claim 1.

Images