System

Abstract

An object of the system according to the embodiment is to improve the quality of play-by-play and commentary of an esports game and provide an appealing viewing experience to viewers.SOLUTION: A system includes a collection part, an analysis part, a comment part, and a provision part. The collection unit collects data of a game. The analysis unit analyzes the data collected by the collection unit. The comment unit performs play-by-play and commentary based on the analysis result obtained by the analysis unit. The provision unit provides the viewer with the play-by-play / commentary made by the comment unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the quality of live commentary and commentary for esports matches was inconsistent, making it difficult to provide viewers with a satisfying viewing experience.

[0005] The system according to the embodiment aims to improve the quality of live commentary and commentary of esports matches and provide viewers with an engaging viewing experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a comment unit, and a provision unit. The collection unit collects match data. The analysis unit analyzes the data collected by the collection unit. The comment unit provides commentary and commentary based on the analysis results obtained by the analysis unit. The provision unit provides viewers with the commentary and commentary provided by the comment unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the quality of live commentary and commentary of esports matches and provide viewers with an engaging 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) An esports commentary system according to an embodiment of the present invention uses generative AI to collect and analyze game data, provide commentary, and provide it to viewers. The esports commentary system collects and analyzes game data, provides commentary, and provides it to viewers, thereby providing a realistic esports experience. For example, the esports commentary system collects data such as the progress of the game, player movements, and tactics. The esports commentary system then analyzes the collected data to analyze player tactics and the development of the game. The esports commentary system then provides commentary in a natural commentary style based on the analysis results. The esports commentary system then provides commentary to viewers in real time, allowing viewers to grasp the situation of the game in real time and gain a deeper understanding. The esports commentary system also automatically generates match highlights and provides them to viewers. This allows viewers to enjoy the game without missing any important moments. This allows the esports commentary system to provide viewers with a realistic esports experience. For example, even if a viewer starts watching a match in the middle, they can still understand the flow of the match by watching the highlights generated by AI. This makes it easier for viewers to understand the overall picture of the match, allowing them to enjoy esports even more.

[0029] An esports commentary system according to an embodiment includes a collection unit, an analysis unit, a commenting unit, and a providing unit. The collection unit collects match data. The match data includes, but is not limited to, scores, player movements, and tactics. The collection unit collects, for example, real-time match progress data. The collection unit can also collect player movements using a specific sensor. The collection unit can also collect tactical data for the match. For example, the collection unit collects player movement distance and speed. The collection unit can also collect player actions. The analysis unit analyzes the collected data. The analysis is performed using, for example, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes player tactics. The analysis unit can also analyze the progression of the match. The analysis unit can also perform tactical pattern recognition. For example, the analysis unit analyzes the success rate of tactics. The commenting unit provides commentary and analysis in a natural commentary style based on the analysis results. The commentary includes, but is not limited to, audio commentary and text commentary. For example, the commentary unit provides commentary on players' tactics. The commentary unit can also provide live commentary on the development of a match. The commentary unit can also automatically generate match highlights. For example, the commentary unit selects and edits important scenes. The providing unit provides the commentary to viewers in real time. The providing unit can provide, but is not limited to, streaming or on-demand delivery. For example, the providing unit can stream the commentary. The providing unit can also provide on-demand delivery. This allows the esports commentary system according to the embodiment to provide viewers with a realistic esports experience. For example, viewers can grasp the situation of the match in real time and gain a deeper understanding. Viewers can also enjoy the match without missing important scenes.

[0030] The collection unit can collect at least one of data on the progress of a match, player movements, and tactics. The collection unit, for example, collects data on the progress of a match. For example, the collection unit collects data on score fluctuations. The collection unit can also collect data on player positions. The collection unit can also collect data on player movements. For example, the collection unit can collect data on player movement. The collection unit can also collect data on player speeds. The collection unit can also collect data on player actions. For example, the collection unit can collect data on player attack patterns. The collection unit can also collect data on player defense patterns. The collection unit can also collect data on tactics. For example, the collection unit can collect data on formations. The collection unit can also collect data on attack patterns. The collection unit can also collect data on defense patterns. By collecting data on the progress of a match, player movements, tactics, and the like, detailed match information can be provided. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can collect the progress of the game in real time, input it into the AI, and have the AI ​​analyze the progress of the game.

[0031] The analysis unit can analyze the collected data and analyze player tactics and the development of the game. The analysis unit, for example, analyzes the collected data. For example, the analysis unit analyzes player tactics. The analysis unit can also analyze the development of the game. The analysis unit can also perform tactical pattern recognition. For example, the analysis unit analyzes the success rate of tactics. The analysis unit can also analyze the flow of the game. The analysis unit can also detect important events. For example, the analysis unit can detect important moments of the game. The analysis unit can also analyze player movements. The analysis unit can also analyze player actions. For example, the analysis unit analyzes player attack patterns. The analysis unit can also analyze player defensive patterns. By analyzing player tactics and the development of the game, detailed game commentary can be provided to viewers. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​analyze the players' tactics and the progress of the game.

[0032] The commentary unit can provide commentary and analysis in a natural commentary style based on the analysis results. The commentary unit, for example, provides commentary and analysis based on the analysis results. For example, the commentary unit provides commentary on players' tactics. The commentary unit can also provide commentary on the development of a match. The commentary unit can also automatically generate match highlights. For example, the commentary unit selects and edits important scenes. The commentary unit can also provide commentary and analysis using voice synthesis technology. The commentary unit can also provide commentary and analysis using natural language processing technology. For example, the commentary unit can provide commentary and analysis in a natural commentary style using voice synthesis technology. The commentary unit can also provide commentary in text using natural language processing technology. This allows for commentary and analysis in a natural commentary style to provide a realistic esports experience to viewers. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without AI. For example, the commentary unit can input the analysis results into AI and have the AI ​​perform commentary and analysis in a natural commentary style.

[0033] The providing unit can provide commentary and commentary to viewers in real time. For example, the providing unit provides commentary and commentary to viewers in real time. For example, the providing unit performs streaming distribution. The providing unit can also perform on-demand distribution. The providing unit can also perform real-time distribution. For example, the providing unit distributes commentary and commentary in real time. The providing unit can also use technology to minimize latency. For example, the providing unit distributes commentary and commentary using low-latency streaming technology. The providing unit can also increase the frequency of data updates. For example, the providing unit updates data in real time to provide viewers with the latest information. This allows viewers to enjoy a realistic esports experience by providing commentary and commentary in real time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input commentary and commentary data into AI and have the AI ​​perform real-time distribution.

[0034] The commentary unit can automatically generate match highlights and provide them to viewers. The commentary unit, for example, automatically generates match highlights. For example, the commentary unit selects and edits important scenes. The commentary unit can also set criteria for generating highlights. For example, the commentary unit sets criteria for selecting important scenes. The commentary unit can also set an editing method. For example, the commentary unit sets a scene switching method. The commentary unit can also set the length of the highlights. For example, the commentary unit adjusts the length of the highlights. By automatically generating match highlights, viewers can enjoy the highlights without missing any important scenes. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input match data into AI and have the AI ​​generate the highlights.

[0035] The collection unit can dynamically change the frequency of data collection depending on the progress of the match. The collection unit dynamically changes the frequency of data collection depending on, for example, the progress of the match. For example, in the early stages of the match, the collection unit frequently collects data related to players' initial placements and tactical selections. In addition, the collection unit can focus on collecting data related to battles and important events in the middle stages of the match. In addition, in the late stages of the match, the collection unit can intensively collect data related to decisive moments in victory or defeat and players' final tactics. In this way, important data can be efficiently collected by changing the frequency of data collection depending on the progress of the match. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the progress of the match into AI and cause the AI ​​to dynamically change the frequency of data collection.

[0036] The collection unit can collect past performance data of a player and compare it with the current game. The collection unit, for example, collects past performance data of a player. For example, the collection unit collects past game data of a player. The collection unit can also collect past tactical data of a player. The collection unit can also collect past win / loss data of a player. Next, the collection unit compares the collected past performance data with the current game. For example, the collection unit compares the past game data of a player with the current game data. The collection unit can also compare the past tactical data of a player with the current tactical data. The collection unit can also use the past win / loss data of a player to predict the outcome of the current game. In this way, collecting past performance data of a player and comparing it with the current game can provide viewers with a deeper understanding. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past performance data of a player into AI and have the AI ​​compare it with the current game.

[0037] The collection unit can enhance data collection using a specific event during a match as a trigger. For example, the collection unit enhances data collection using a specific event during a match as a trigger. For example, the collection unit collects detailed battle data the moment a player achieves a kill. The collection unit can also collect detailed information about tactics and player movements the moment a team scores a goal. The collection unit can also collect data for analyzing the impact of an important item the moment it is obtained. In this way, detailed data about important moments can be collected by enhancing data collection using a specific event as a trigger. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a specific event during a match into an AI and cause the AI ​​to enhance data collection.

[0038] The collection unit can collect data related to regional viewers based on the geographical location information of the match. The collection unit, for example, collects data related to regional viewers taking into account the geographical location information of the match. For example, the collection unit collects data on players and teams that regional viewers are interested in. The collection unit can also collect specific events and moments in matches that regional viewers are interested in. The collection unit can also collect data on tactics and playing styles that regional viewers are interested in. By collecting data related to regional viewers in this way, regionally specialized content can be provided to viewers. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the match to AI and cause the AI ​​to collect data related to regional viewers.

[0039] The collection unit can analyze social media trends and collect related data. The collection unit, for example, analyzes social media trends. For example, the collection unit can collect data on players and teams that are trending on social media. The collection unit can also collect specific events or moments from matches that are trending on social media. The collection unit can also collect data on tactics and playing styles that are trending on social media. This allows for analyzing social media trends to provide viewers with content based on the latest trends. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input social media trend data into AI and have the AI ​​collect related data.

[0040] The collection unit can customize the data to be collected by reflecting the viewer's past viewing history. The collection unit customizes the data to be collected by reflecting, for example, the viewer's past viewing history. For example, the collection unit prioritizes collecting data on matches and players that the viewer has previously viewed. The collection unit can also collect data on tactics and playing styles that the viewer has previously shown interest in. The collection unit can also collect highlights and important moments of matches that the viewer has previously viewed. This makes it possible to provide optimal content to the viewer by reflecting the viewer's past viewing history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the viewer's past viewing history into AI and have the AI ​​customize the data to be collected.

[0041] The analysis unit can dynamically change the analysis algorithm according to the progress of the match. The analysis unit dynamically changes the analysis algorithm according to, for example, the progress of the match. For example, in the early stages of the match, the analysis unit performs analysis regarding the initial positioning of players and the selection of tactics. In addition, the analysis unit can also perform analysis regarding battles and important events in the middle stages of the match. In addition, the analysis unit can also perform analysis regarding decisive moments in the game and the final tactics of players in the late stages of the game. In this way, important data can be analyzed efficiently by changing the analysis algorithm according to the progress of the match. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the progress of the game into AI and cause the AI ​​to dynamically change the analysis algorithm.

[0042] The analysis unit can analyze the player's past tactical data and compare it with the current game. The analysis unit, for example, analyzes the player's past tactical data. For example, the analysis unit analyzes the player's past game data. The analysis unit can also analyze the player's past tactical data. The analysis unit can also analyze the player's past win / loss data. Next, the analysis unit compares the analyzed past tactical data with the current game. For example, the analysis unit compares the player's past game data with the current game data. The analysis unit can also compare the player's past tactical data with the current tactical data. The analysis unit can also use the player's past win / loss data to predict the outcome of the current game. In this way, analyzing the player's past tactical data and comparing it with the current game can provide viewers with a deeper understanding. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the player's past tactical data into AI and have the AI ​​compare it with the current game.

[0043] The analysis unit can enhance the analysis using a specific event during a match as a trigger. For example, the analysis unit enhances the analysis using a specific event during a match as a trigger. For example, the analysis unit analyzes detailed battle data the moment a player achieves a kill. The analysis unit can also analyze tactics and player movements in detail the moment a team scores a goal. The analysis unit can also analyze data to analyze the impact of an important item the moment it is obtained. In this way, by enhancing the analysis using a specific event as a trigger, detailed analysis of important moments can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input a specific event during a match into AI and cause the AI ​​to enhance the analysis.

[0044] The analysis unit can perform analysis related to viewers in each region by taking into account the geographical location information of the match. The analysis unit can perform analysis related to viewers in each region by taking into account the geographical location information of the match. For example, the analysis unit can perform analysis of players and teams that viewers in each region are interested in. The analysis unit can also analyze specific events or moments in the match that viewers in each region are interested in. The analysis unit can also perform analysis related to tactics and playing styles that viewers in each region are interested in. In this way, by performing analysis related to viewers in each region, it is possible to provide viewers with analysis results that are specialized for their region. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical location information of the match into AI and cause the AI ​​to perform analysis related to viewers in each region.

[0045] The analysis unit can analyze social media trends and perform related analysis. The analysis unit, for example, analyzes social media trends. For example, the analysis unit analyzes players and teams that are trending on social media. The analysis unit can also analyze specific events or moments in a match that are trending on social media. The analysis unit can also perform analysis on tactics and playing styles that are trending on social media. In this way, by analyzing social media trends, it is possible to provide viewers with analysis results based on the latest trends. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input social media trend data into AI and have the AI ​​perform related analysis.

[0046] The analysis unit can customize the analysis results by reflecting the viewer's past viewing history. The analysis unit customizes the analysis results by reflecting, for example, the viewer's past viewing history. For example, the analysis unit prioritizes displaying analysis results of matches and players that the viewer has previously viewed. The analysis unit can also display analysis results related to tactics and playing styles in which the viewer has previously shown interest. The analysis unit can also reflect highlights and important moments of matches that the viewer has previously viewed in the analysis results. In this way, by reflecting the viewer's past viewing history, it is possible to provide the viewer with optimal analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the viewer's past viewing history into AI and have the AI ​​customize the analysis results.

[0047] The comment unit can dynamically change the level of detail of the comments depending on the progress of the match. The comment unit dynamically changes the level of detail of the comments depending on, for example, the progress of the match. For example, in the early stages of the match, the comment unit provides detailed comments about players' initial positioning and tactical choices. In addition, the comment unit can provide detailed comments about battles and important events in the middle stages of the match. In addition, the comment unit can provide detailed comments about decisive moments in the match and players' final tactics in the late stages of the match. In this way, by changing the level of detail of the comments depending on the progress of the match, it is possible to provide optimal comments to viewers. Some or all of the above-mentioned processing in the comment unit may be performed using, or without, an AI. For example, the comment unit can input the progress of the match into the AI ​​and cause the AI ​​to dynamically change the level of detail of the comments.

[0048] The commenting unit can provide comments on the current match based on the player's past performance. The commenting unit provides comments based on the player's past performance, for example. For example, the commenting unit can comment on the player's performance in the current match based on the player's past match data. The commenting unit can also provide comments on the tactics for the current match based on the player's past tactical data. The commenting unit can also provide comments on a prediction of the outcome of the current match based on the player's past win / loss data. This allows for providing comments based on the player's past performance to provide viewers with a deeper understanding. Some or all of the above-described processing in the commenting unit may be performed using, or without, an AI. For example, the commenting unit can input the player's past performance data into an AI and have the AI ​​execute comments on the current match.

[0049] The comment section can enhance the comments when a specific event occurs during a match. For example, the comment section can enhance the comments when a specific event occurs during a match. For example, the comment section can provide detailed commentary on a play the moment a player achieves a kill. The comment section can also provide detailed commentary on the tactics and player movements the moment a team scores a goal. The comment section can also analyze the impact of an important item and comment on it the moment it is obtained. In this way, by enhancing the comments when a specific event occurs, viewers can be provided with detailed commentary on important moments. Some or all of the above-described processing in the comment section can be performed using, for example, AI, or without AI. For example, the comment section can input specific events during a match into AI and have the AI ​​enhance the comments.

[0050] The comment unit can provide comments relevant to viewers in each region by taking into account the geographical location information of the match. The comment unit can provide comments relevant to viewers in each region by taking into account the geographical location information of the match. For example, the comment unit can comment on players or teams that viewers in each region are interested in. The comment unit can also comment on specific events or moments in the match that viewers in each region are interested in. The comment unit can also comment on tactics or playing styles that viewers in each region are interested in. In this way, by providing comments relevant to viewers in each region, region-specific comments can be provided to viewers. Some or all of the above-described processing in the comment unit can be performed using, for example, AI, or without AI. For example, the comment unit can input the geographical location information of the match into AI and cause the AI ​​to perform comments relevant to viewers in each region.

[0051] The comment section can analyze social media trends and make related comments. The comment section, for example, analyzes social media trends. For example, the comment section can comment on players or teams that are trending on social media. The comment section can also comment on specific events or moments in a match that are trending on social media. The comment section can also comment on tactics or playing styles that are trending on social media. In this way, by analyzing social media trends, viewers can be provided with comments based on the latest trends. Some or all of the above-mentioned processing in the comment section may be performed using, or without, AI, for example. For example, the comment section can input social media trend data into AI and have the AI ​​execute related comments.

[0052] The comment unit can customize the comment content by reflecting the viewer's past viewing history. The comment unit customizes the comment content by reflecting, for example, the viewer's past viewing history. For example, the comment unit comments on matches and players that the viewer has previously watched. The comment unit can also comment on tactics and playing styles that the viewer has previously shown interest in. The comment unit can also comment on highlights and important moments of matches that the viewer has previously watched. In this way, by reflecting the viewer's past viewing history, it is possible to provide the viewer with comments that are optimal for the viewer. Some or all of the above-described processing in the comment unit may be performed, for example, using AI, or may be performed without using AI. For example, the comment unit can input the viewer's past viewing history into AI and have the AI ​​customize the comment content.

[0053] The providing unit can dynamically change the frequency of content provided depending on the progress of the match. The providing unit dynamically changes the frequency of content provided depending on, for example, the progress of the match. For example, in the early stages of the match, the providing unit frequently provides content related to players' initial positioning and tactical selection. In addition, the providing unit can focus on providing content related to battles and important events in the middle stages of the match. In addition, in the late stages of the match, the providing unit can focus on providing content related to decisive moments in victory or defeat and players' final tactics. In this way, by changing the frequency of content provision depending on the progress of the match, optimal content can be provided to viewers. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the progress of the match into AI and cause the AI ​​to dynamically change the frequency of content provided.

[0054] The providing unit can provide highlights of the current game based on the player's past performance data. The providing unit, for example, provides highlights based on the player's past performance data. For example, the providing unit provides highlights of the current game based on the player's past game data. The providing unit can also provide highlights of the current game based on the player's past tactical data. The providing unit can also provide highlights of the current game based on the player's past win / loss data. In this way, by providing highlights based on the player's past performance data, important scenes can be provided to viewers. Some or all of the above-described processing in the providing unit may be performed using, or without, an AI. For example, the providing unit can input the player's past performance data into an AI and cause the AI ​​to provide highlights of the current game.

[0055] The providing unit can enhance content provision using a specific event during a match as a trigger. For example, the providing unit enhances content provision using a specific event during a match as a trigger. For example, the providing unit can provide content including a detailed commentary on a play the moment a player achieves a kill. The providing unit can also provide content that provides a detailed commentary on the tactics and player movements the moment a team scores a goal. The providing unit can also provide content that analyzes and explains the impact of an important item the moment it is obtained. In this way, by enhancing content provision using a specific event as a trigger, detailed content of important moments can be provided to viewers. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input a specific event during a match into AI and cause the AI ​​to enhance content provision.

[0056] The providing unit can provide content relevant to viewers in each region by taking into account the geographical location information of the match. The providing unit can provide content relevant to viewers in each region by taking into account the geographical location information of the match. For example, the providing unit can provide content about players or teams that viewers in each region are interested in. The providing unit can also provide content about specific events or moments in the match that viewers in each region are interested in. The providing unit can also provide content related to tactics or playing styles that viewers in each region are interested in. In this way, by providing content relevant to viewers in each region, it is possible to provide viewers with content that is specific to their region. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical location information of the match into AI and cause the AI ​​to provide content relevant to viewers in each region.

[0057] The providing unit can analyze social media trends and provide related content. The providing unit, for example, analyzes social media trends. For example, the providing unit can provide content about players or teams that are trending on social media. The providing unit can also provide content about specific events or moments in a game that are trending on social media. The providing unit can also provide content about tactics or playing styles that are trending on social media. In this way, by analyzing social media trends, viewers can be provided with content based on the latest trends. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input social media trend data into AI and cause the AI ​​to provide related content.

[0058] The providing unit can customize the content to be provided by reflecting the viewer's past viewing history. The providing unit customizes the content to be provided by reflecting, for example, the viewer's past viewing history. For example, the providing unit prioritizes providing content of matches or players that the viewer has previously viewed. The providing unit can also provide content related to tactics or playing styles in which the viewer has previously shown interest. The providing unit can also provide content of highlights or important moments of matches that the viewer has previously viewed. In this way, by reflecting the viewer's past viewing history, the most suitable content can be provided to the viewer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's past viewing history into AI and have the AI ​​customize the content to be provided.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can also collect real-time reactions from viewers and provide them to the analysis unit. For example, if a viewer applauds or cheers at a particular scene, the collection unit collects that data and sends it to the analysis unit. Also, if a viewer posts a comment at a particular scene, the collection unit can collect that comment data and send it to the analysis unit. In this way, by collecting real-time reactions from viewers, it is possible to understand the viewer's interests and provide more personalized content.

[0061] The collection unit can prioritize collection of data that is likely to interest the viewer based on the viewer's past viewing history. For example, if the viewer has shown interest in a particular player or team in the past, data related to that player or team can be collected preferentially. Also, if the viewer has shown interest in a particular tactic or playing style in the past, data related to that tactic or playing style can be collected preferentially. In this way, by collecting data preferentially based on the viewer's past viewing history, it is possible to provide the viewer with a more personalized experience.

[0062] The analysis unit can prioritize displaying analysis results that are likely to interest the viewer based on the viewer's past viewing history. For example, if the viewer has shown interest in a particular player or team in the past, analysis results related to that player or team can be prioritized. Also, if the viewer has shown interest in a particular tactic or playing style in the past, analysis results related to that tactic or playing style can be prioritized. This allows for a more personalized experience for the viewer by prioritized display of analysis results based on the viewer's past viewing history.

[0063] The comment section can prioritize comments that are likely to interest the viewer based on the viewer's past viewing history. For example, if a viewer has previously shown interest in a particular player or team, it can prioritize comments about that player or team. Also, if a viewer has previously shown interest in a particular tactic or playing style, it can prioritize comments about that tactic or playing style. This allows the viewer to have a more personalized experience by prioritizing comments based on the viewer's past viewing history.

[0064] The providing unit can provide content that is likely to interest a viewer based on the viewer's past viewing history with priority. For example, if a viewer has shown interest in a particular player or team in the past, content related to that player or team can be provided with priority. Also, if a viewer has shown interest in a particular tactic or playing style in the past, content related to that tactic or playing style can be provided with priority. In this way, by providing content with priority based on the viewer's past viewing history, a more personalized experience can be provided to the viewer.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects match data. Match data includes scores, player movements, tactics, etc. The collection unit collects match progress in real time and uses specific sensors to collect player movement and tactical data. For example, it collects player movement distance, speed, and actions. Step 2: The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms. For example, it analyzes player tactics, game development, tactical pattern recognition, and tactical success rate. Step 3: The commentary section provides commentary in a natural style based on the analysis results. This commentary includes audio commentary and text commentary. For example, it explains the players' tactics, provides live commentary on the game's development, automatically generates highlights of the game, and selects and edits important scenes. Step 4: The provider provides the commentary and commentary to viewers in real time. This can be streaming or on-demand. For example, the commentary and commentary can be streamed or on-demand.

[0067] (Example 2) An esports commentary system according to an embodiment of the present invention uses generative AI to collect and analyze game data, provide commentary, and provide it to viewers. The esports commentary system collects and analyzes game data, provides commentary, and provides it to viewers, thereby providing a realistic esports experience. For example, the esports commentary system collects data such as the progress of the game, player movements, and tactics. The esports commentary system then analyzes the collected data to analyze player tactics and the development of the game. The esports commentary system then provides commentary in a natural commentary style based on the analysis results. The esports commentary system then provides commentary to viewers in real time, allowing viewers to grasp the situation of the game in real time and gain a deeper understanding. The esports commentary system also automatically generates match highlights and provides them to viewers. This allows viewers to enjoy the game without missing any important moments. This allows the esports commentary system to provide viewers with a realistic esports experience. For example, even if a viewer starts watching a match in the middle, they can still understand the flow of the match by watching the highlights generated by AI. This makes it easier for viewers to understand the overall picture of the match, allowing them to enjoy esports even more.

[0068] An esports commentary system according to an embodiment includes a collection unit, an analysis unit, a commenting unit, and a providing unit. The collection unit collects match data. The match data includes, but is not limited to, scores, player movements, and tactics. The collection unit collects, for example, real-time match progress data. The collection unit can also collect player movements using a specific sensor. The collection unit can also collect tactical data for the match. For example, the collection unit collects player movement distance and speed. The collection unit can also collect player actions. The analysis unit analyzes the collected data. The analysis is performed using, for example, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes player tactics. The analysis unit can also analyze the progression of the match. The analysis unit can also perform tactical pattern recognition. For example, the analysis unit analyzes the success rate of tactics. The commenting unit provides commentary and analysis in a natural commentary style based on the analysis results. The commentary includes, but is not limited to, audio commentary and text commentary. For example, the commentary unit provides commentary on players' tactics. The commentary unit can also provide live commentary on the development of a match. The commentary unit can also automatically generate match highlights. For example, the commentary unit selects and edits important scenes. The providing unit provides the commentary to viewers in real time. The providing unit can provide, but is not limited to, streaming or on-demand delivery. For example, the providing unit can stream the commentary. The providing unit can also provide on-demand delivery. This allows the esports commentary system according to the embodiment to provide viewers with a realistic esports experience. For example, viewers can grasp the situation of the match in real time and gain a deeper understanding. Viewers can also enjoy the match without missing important scenes.

[0069] The collection unit can collect at least one of data on the progress of a match, player movements, and tactics. The collection unit, for example, collects data on the progress of a match. For example, the collection unit collects data on score fluctuations. The collection unit can also collect data on player positions. The collection unit can also collect data on player movements. For example, the collection unit can collect data on player movement. The collection unit can also collect data on player speeds. The collection unit can also collect data on player actions. For example, the collection unit can collect data on player attack patterns. The collection unit can also collect data on player defense patterns. The collection unit can also collect data on tactics. For example, the collection unit can collect data on formations. The collection unit can also collect data on attack patterns. The collection unit can also collect data on defense patterns. By collecting data on the progress of a match, player movements, tactics, and the like, detailed match information can be provided. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can collect the progress of the game in real time, input it into the AI, and have the AI ​​analyze the progress of the game.

[0070] The analysis unit can analyze the collected data and analyze player tactics and the development of the game. The analysis unit, for example, analyzes the collected data. For example, the analysis unit analyzes player tactics. The analysis unit can also analyze the development of the game. The analysis unit can also perform tactical pattern recognition. For example, the analysis unit analyzes the success rate of tactics. The analysis unit can also analyze the flow of the game. The analysis unit can also detect important events. For example, the analysis unit can detect important moments of the game. The analysis unit can also analyze player movements. The analysis unit can also analyze player actions. For example, the analysis unit analyzes player attack patterns. The analysis unit can also analyze player defensive patterns. By analyzing player tactics and the development of the game, detailed game commentary can be provided to viewers. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​analyze the players' tactics and the progress of the game.

[0071] The commentary unit can provide commentary and analysis in a natural commentary style based on the analysis results. The commentary unit, for example, provides commentary and analysis based on the analysis results. For example, the commentary unit provides commentary on players' tactics. The commentary unit can also provide commentary on the development of a match. The commentary unit can also automatically generate match highlights. For example, the commentary unit selects and edits important scenes. The commentary unit can also provide commentary and analysis using voice synthesis technology. The commentary unit can also provide commentary and analysis using natural language processing technology. For example, the commentary unit can provide commentary and analysis in a natural commentary style using voice synthesis technology. The commentary unit can also provide commentary in text using natural language processing technology. This allows for commentary and analysis in a natural commentary style to provide a realistic esports experience to viewers. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without AI. For example, the commentary unit can input the analysis results into AI and have the AI ​​perform commentary and analysis in a natural commentary style.

[0072] The providing unit can provide commentary and commentary to viewers in real time. For example, the providing unit provides commentary and commentary to viewers in real time. For example, the providing unit performs streaming distribution. The providing unit can also perform on-demand distribution. The providing unit can also perform real-time distribution. For example, the providing unit distributes commentary and commentary in real time. The providing unit can also use technology to minimize latency. For example, the providing unit distributes commentary and commentary using low-latency streaming technology. The providing unit can also increase the frequency of data updates. For example, the providing unit updates data in real time to provide viewers with the latest information. This allows viewers to enjoy a realistic esports experience by providing commentary and commentary in real time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input commentary and commentary data into AI and have the AI ​​perform real-time distribution.

[0073] The commentary unit can automatically generate match highlights and provide them to viewers. The commentary unit, for example, automatically generates match highlights. For example, the commentary unit selects and edits important scenes. The commentary unit can also set criteria for generating highlights. For example, the commentary unit sets criteria for selecting important scenes. The commentary unit can also set an editing method. For example, the commentary unit sets a scene switching method. The commentary unit can also set the length of the highlights. For example, the commentary unit adjusts the length of the highlights. By automatically generating match highlights, viewers can enjoy the highlights without missing any important scenes. Some or all of the above-described processing in the commentary unit may be performed using, for example, AI, or may be performed without using AI. For example, the commentary unit can input match data into AI and have the AI ​​generate the highlights.

[0074] The collection unit can estimate the viewer's emotions and adjust the type of data to be collected based on the estimated viewer's emotions. The collection unit, for example, estimates the viewer's emotions. For example, the collection unit can estimate the viewer's emotions using facial expression recognition technology. The collection unit can also estimate the viewer's emotions using voice analysis technology. The collection unit can also estimate the viewer's emotions using survey results. For example, the collection unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The collection unit can also record the viewer's voice and estimate the emotions using voice analysis technology. The collection unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the collection unit adjusts the type of data to be collected based on the estimated viewer's emotions. For example, if the viewer is excited, the collection unit can prioritize collecting highlights and important moments of the game. If the viewer is relaxed, the collection unit can collect details of the overall flow and tactics of the game. If the viewer is nervous, the collection unit can collect moments of player mistakes and recovery. In this way, by adjusting the type of data based on the viewer's emotions, it is possible to provide the viewer with optimal content. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit may input viewer emotion data into an AI and have the AI ​​adjust the type of data to be collected.

[0075] The collection unit can dynamically change the frequency of data collection depending on the progress of the match. The collection unit dynamically changes the frequency of data collection depending on, for example, the progress of the match. For example, in the early stages of the match, the collection unit frequently collects data related to players' initial placements and tactical selections. In addition, the collection unit can focus on collecting data related to battles and important events in the middle stages of the match. In addition, in the late stages of the match, the collection unit can intensively collect data related to decisive moments in victory or defeat and players' final tactics. In this way, important data can be efficiently collected by changing the frequency of data collection depending on the progress of the match. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the progress of the match into AI and cause the AI ​​to dynamically change the frequency of data collection.

[0076] The collection unit can collect past performance data of a player and compare it with the current game. The collection unit, for example, collects past performance data of a player. For example, the collection unit collects past game data of a player. The collection unit can also collect past tactical data of a player. The collection unit can also collect past win / loss data of a player. Next, the collection unit compares the collected past performance data with the current game. For example, the collection unit compares the past game data of a player with the current game data. The collection unit can also compare the past tactical data of a player with the current tactical data. The collection unit can also use the past win / loss data of a player to predict the outcome of the current game. In this way, collecting past performance data of a player and comparing it with the current game can provide viewers with a deeper understanding. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past performance data of a player into AI and have the AI ​​compare it with the current game.

[0077] The collection unit can enhance data collection using a specific event during a match as a trigger. For example, the collection unit enhances data collection using a specific event during a match as a trigger. For example, the collection unit collects detailed battle data the moment a player achieves a kill. The collection unit can also collect detailed information about tactics and player movements the moment a team scores a goal. The collection unit can also collect data for analyzing the impact of an important item the moment it is obtained. In this way, detailed data about important moments can be collected by enhancing data collection using a specific event as a trigger. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a specific event during a match into an AI and cause the AI ​​to enhance data collection.

[0078] The collection unit can estimate the viewer's emotions and determine the priority of data to be collected based on the estimated viewer's emotions. The collection unit, for example, estimates the viewer's emotions. For example, the collection unit can estimate the viewer's emotions using facial expression recognition technology. The collection unit can also estimate the viewer's emotions using voice analysis technology. The collection unit can also estimate the viewer's emotions using survey results. For example, the collection unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The collection unit can also record the viewer's voice and estimate the emotions using voice analysis technology. The collection unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the collection unit determines the priority of data to be collected based on the estimated viewer's emotions. For example, if the viewer is excited, action scenes and highlights can be collected with priority. If the viewer is relaxed, the overall flow of the game and details of tactics can be collected with priority. If the viewer is nervous, the collection unit can also collect player mistakes and recovery moments with priority. In this way, by prioritizing data based on the viewer's emotions, it is possible to provide the viewer with optimal content. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit may input viewer emotion data into an AI and have the AI ​​determine the priority of the data to be collected.

[0079] The collection unit can collect data related to regional viewers based on the geographical location information of the match. The collection unit, for example, collects data related to regional viewers taking into account the geographical location information of the match. For example, the collection unit collects data on players and teams that regional viewers are interested in. The collection unit can also collect specific events and moments in matches that regional viewers are interested in. The collection unit can also collect data on tactics and playing styles that regional viewers are interested in. By collecting data related to regional viewers in this way, regionally specialized content can be provided to viewers. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the match to AI and cause the AI ​​to collect data related to regional viewers.

[0080] The collection unit can analyze social media trends and collect related data. The collection unit, for example, analyzes social media trends. For example, the collection unit can collect data on players and teams that are trending on social media. The collection unit can also collect specific events or moments from matches that are trending on social media. The collection unit can also collect data on tactics and playing styles that are trending on social media. This allows for analyzing social media trends to provide viewers with content based on the latest trends. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input social media trend data into AI and have the AI ​​collect related data.

[0081] The collection unit can customize the data to be collected by reflecting the viewer's past viewing history. The collection unit customizes the data to be collected by reflecting, for example, the viewer's past viewing history. For example, the collection unit prioritizes collecting data on matches and players that the viewer has previously viewed. The collection unit can also collect data on tactics and playing styles that the viewer has previously shown interest in. The collection unit can also collect highlights and important moments of matches that the viewer has previously viewed. This makes it possible to provide optimal content to the viewer by reflecting the viewer's past viewing history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the viewer's past viewing history into AI and have the AI ​​customize the data to be collected.

[0082] The analysis unit can estimate the viewer's emotions and adjust the focus of analysis based on the estimated viewer's emotions. The analysis unit, for example, estimates the viewer's emotions. For example, the analysis unit can estimate the viewer's emotions using facial expression recognition technology. The analysis unit can also estimate the viewer's emotions using audio analysis technology. The analysis unit can also estimate the viewer's emotions using survey results. For example, the analysis unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the viewer's voice and estimate the emotions using audio analysis technology. The analysis unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the analysis unit adjusts the focus of analysis based on the estimated viewer's emotions. For example, if the viewer is excited, the analysis can focus on action scenes and highlights. If the viewer is relaxed, the analysis can focus on the overall flow of the game and tactical details. If the viewer is nervous, the analysis can focus on player mistakes and recovery moments. In this way, by adjusting the focus of analysis based on the viewer's emotions, it is possible to provide the viewer with optimal analysis results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input viewer emotion data into AI and have the AI ​​adjust the focus of the analysis.

[0083] The analysis unit can dynamically change the analysis algorithm according to the progress of the match. The analysis unit dynamically changes the analysis algorithm according to, for example, the progress of the match. For example, in the early stages of the match, the analysis unit performs analysis regarding the initial positioning of players and the selection of tactics. In addition, the analysis unit can also perform analysis regarding battles and important events in the middle stages of the match. In addition, the analysis unit can also perform analysis regarding decisive moments in the game and the final tactics of players in the late stages of the game. In this way, important data can be analyzed efficiently by changing the analysis algorithm according to the progress of the match. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the progress of the game into AI and cause the AI ​​to dynamically change the analysis algorithm.

[0084] The analysis unit can analyze the player's past tactical data and compare it with the current game. The analysis unit, for example, analyzes the player's past tactical data. For example, the analysis unit analyzes the player's past game data. The analysis unit can also analyze the player's past tactical data. The analysis unit can also analyze the player's past win / loss data. Next, the analysis unit compares the analyzed past tactical data with the current game. For example, the analysis unit compares the player's past game data with the current game data. The analysis unit can also compare the player's past tactical data with the current tactical data. The analysis unit can also use the player's past win / loss data to predict the outcome of the current game. In this way, analyzing the player's past tactical data and comparing it with the current game can provide viewers with a deeper understanding. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the player's past tactical data into AI and have the AI ​​compare it with the current game.

[0085] The analysis unit can enhance the analysis using a specific event during a match as a trigger. For example, the analysis unit enhances the analysis using a specific event during a match as a trigger. For example, the analysis unit analyzes detailed battle data the moment a player achieves a kill. The analysis unit can also analyze tactics and player movements in detail the moment a team scores a goal. The analysis unit can also analyze data to analyze the impact of an important item the moment it is obtained. In this way, by enhancing the analysis using a specific event as a trigger, detailed analysis of important moments can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input a specific event during a match into AI and cause the AI ​​to enhance the analysis.

[0086] The analysis unit can estimate the viewer's emotions and adjust the display method of the analysis results based on the estimated viewer's emotions. The analysis unit, for example, estimates the viewer's emotions. For example, the analysis unit can estimate the viewer's emotions using facial expression recognition technology. The analysis unit can also estimate the viewer's emotions using voice analysis technology. The analysis unit can also estimate the viewer's emotions using survey results. For example, the analysis unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the viewer's voice and estimate the emotions using voice analysis technology. The analysis unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the analysis unit adjusts the display method of the analysis results based on the estimated viewer's emotions. For example, if the viewer is excited, action scenes and highlights can be emphasized. If the viewer is relaxed, the overall flow of the game and details of tactics can be displayed. If the viewer is nervous, player mistakes and recovery moments can be emphasized. In this way, by adjusting the display method of the analysis results based on the viewer's emotions, it is possible to provide the viewer with optimal analysis results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input viewer emotion data into AI and have the AI ​​adjust the display method of the analysis results.

[0087] The analysis unit can perform analysis related to viewers in each region by taking into account the geographical location information of the match. The analysis unit can perform analysis related to viewers in each region by taking into account the geographical location information of the match. For example, the analysis unit can perform analysis of players and teams that viewers in each region are interested in. The analysis unit can also analyze specific events or moments in the match that viewers in each region are interested in. The analysis unit can also perform analysis related to tactics and playing styles that viewers in each region are interested in. In this way, by performing analysis related to viewers in each region, it is possible to provide viewers with analysis results that are specialized for their region. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical location information of the match into AI and cause the AI ​​to perform analysis related to viewers in each region.

[0088] The analysis unit can analyze social media trends and perform related analysis. The analysis unit, for example, analyzes social media trends. For example, the analysis unit analyzes players and teams that are trending on social media. The analysis unit can also analyze specific events or moments in a match that are trending on social media. The analysis unit can also perform analysis on tactics and playing styles that are trending on social media. In this way, by analyzing social media trends, it is possible to provide viewers with analysis results based on the latest trends. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input social media trend data into AI and have the AI ​​perform related analysis.

[0089] The analysis unit can customize the analysis results by reflecting the viewer's past viewing history. The analysis unit customizes the analysis results by reflecting, for example, the viewer's past viewing history. For example, the analysis unit prioritizes displaying analysis results of matches and players that the viewer has previously viewed. The analysis unit can also display analysis results related to tactics and playing styles in which the viewer has previously shown interest. The analysis unit can also reflect highlights and important moments of matches that the viewer has previously viewed in the analysis results. In this way, by reflecting the viewer's past viewing history, it is possible to provide the viewer with optimal analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the viewer's past viewing history into AI and have the AI ​​customize the analysis results.

[0090] The comment unit can estimate the viewer's emotions and adjust the tone of the comments based on the estimated viewer's emotions. The comment unit, for example, estimates the viewer's emotions. For example, the comment unit can estimate the viewer's emotions using facial expression recognition technology. The comment unit can also estimate the viewer's emotions using voice analysis technology. The comment unit can also estimate the viewer's emotions using survey results. For example, the comment unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The comment unit can also record the viewer's voice and estimate the emotions using voice analysis technology. The comment unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the comment unit adjusts the tone of the comments based on the estimated viewer's emotions. For example, if the viewer is excited, the comment unit can make comments in an energetic and enthusiastic tone. If the viewer is relaxed, the comment unit can make comments in a calm tone. If the viewer is nervous, the comment unit can make comments in a reassuring tone. In this way, by adjusting the tone of the comments based on the viewer's emotions, it is possible to provide optimal comments to the viewer. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the comment section can be performed using, or without, AI. For example, the comment section can input viewer emotion data into AI and have the AI ​​adjust the tone of the comments.

[0091] The comment unit can dynamically change the level of detail of the comments depending on the progress of the match. The comment unit dynamically changes the level of detail of the comments depending on, for example, the progress of the match. For example, in the early stages of the match, the comment unit provides detailed comments about players' initial positioning and tactical choices. In addition, the comment unit can provide detailed comments about battles and important events in the middle stages of the match. In addition, the comment unit can provide detailed comments about decisive moments in the match and players' final tactics in the late stages of the match. In this way, by changing the level of detail of the comments depending on the progress of the match, it is possible to provide optimal comments to viewers. Some or all of the above-mentioned processing in the comment unit may be performed using, or without, an AI. For example, the comment unit can input the progress of the match into the AI ​​and cause the AI ​​to dynamically change the level of detail of the comments.

[0092] The commenting unit can provide comments on the current match based on the player's past performance. The commenting unit provides comments based on the player's past performance, for example. For example, the commenting unit can comment on the player's performance in the current match based on the player's past match data. The commenting unit can also provide comments on the tactics for the current match based on the player's past tactical data. The commenting unit can also provide comments on a prediction of the outcome of the current match based on the player's past win / loss data. This allows for providing comments based on the player's past performance to provide viewers with a deeper understanding. Some or all of the above-described processing in the commenting unit may be performed using, or without, an AI. For example, the commenting unit can input the player's past performance data into an AI and have the AI ​​execute comments on the current match.

[0093] The comment section can enhance the comments when a specific event occurs during a match. For example, the comment section can enhance the comments when a specific event occurs during a match. For example, the comment section can provide detailed commentary on a play the moment a player achieves a kill. The comment section can also provide detailed commentary on the tactics and player movements the moment a team scores a goal. The comment section can also analyze the impact of an important item and comment on it the moment it is obtained. In this way, by enhancing the comments when a specific event occurs, viewers can be provided with detailed commentary on important moments. Some or all of the above-described processing in the comment section can be performed using, for example, AI, or without AI. For example, the comment section can input specific events during a match into AI and have the AI ​​enhance the comments.

[0094] The comment unit can estimate the viewer's emotions and adjust the length of the comments based on the estimated viewer's emotions. The comment unit, for example, estimates the viewer's emotions. For example, the comment unit can estimate the viewer's emotions using facial expression recognition technology. The comment unit can also estimate the viewer's emotions using voice analysis technology. The comment unit can also estimate the viewer's emotions using survey results. For example, the comment unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The comment unit can also record the viewer's voice and estimate the emotions using voice analysis technology. The comment unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the comment unit adjusts the length of the comments based on the estimated viewer's emotions. For example, if the viewer is excited, the comment unit can make short, to-the-point comments. If the viewer is relaxed, the comment unit can make detailed, longer comments. If the viewer is nervous, the comment unit can make comments of an appropriate length to give a sense of security. In this way, by adjusting the length of the comments based on the viewer's emotions, it is possible to provide optimal comments to the viewer. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the comment section may be performed using, or without, AI. For example, the comment section may input viewer emotion data into AI and have the AI ​​adjust the length of the comments.

[0095] The comment unit can provide comments relevant to viewers in each region by taking into account the geographical location information of the match. The comment unit can provide comments relevant to viewers in each region by taking into account the geographical location information of the match. For example, the comment unit can comment on players or teams that viewers in each region are interested in. The comment unit can also comment on specific events or moments in the match that viewers in each region are interested in. The comment unit can also comment on tactics or playing styles that viewers in each region are interested in. In this way, by providing comments relevant to viewers in each region, region-specific comments can be provided to viewers. Some or all of the above-described processing in the comment unit can be performed using, for example, AI, or without AI. For example, the comment unit can input the geographical location information of the match into AI and cause the AI ​​to perform comments relevant to viewers in each region.

[0096] The comment section can analyze social media trends and make related comments. The comment section, for example, analyzes social media trends. For example, the comment section can comment on players or teams that are trending on social media. The comment section can also comment on specific events or moments in a match that are trending on social media. The comment section can also comment on tactics or playing styles that are trending on social media. In this way, by analyzing social media trends, viewers can be provided with comments based on the latest trends. Some or all of the above-mentioned processing in the comment section may be performed using, or without, AI, for example. For example, the comment section can input social media trend data into AI and have the AI ​​execute related comments.

[0097] The comment unit can customize the comment content by reflecting the viewer's past viewing history. The comment unit customizes the comment content by reflecting, for example, the viewer's past viewing history. For example, the comment unit comments on matches and players that the viewer has previously watched. The comment unit can also comment on tactics and playing styles that the viewer has previously shown interest in. The comment unit can also comment on highlights and important moments of matches that the viewer has previously watched. In this way, by reflecting the viewer's past viewing history, it is possible to provide the viewer with comments that are optimal for the viewer. Some or all of the above-described processing in the comment unit may be performed, for example, using AI, or may be performed without using AI. For example, the comment unit can input the viewer's past viewing history into AI and have the AI ​​customize the comment content.

[0098] The providing unit can estimate the viewer's emotions and adjust the format of the content to be provided based on the estimated viewer's emotions. The providing unit, for example, estimates the viewer's emotions. For example, the providing unit can estimate the viewer's emotions using facial expression recognition technology. The providing unit can also estimate the viewer's emotions using voice analysis technology. The providing unit can also estimate the viewer's emotions using survey results. For example, the providing unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The providing unit can also record the viewer's voice and estimate the emotions using voice analysis technology. The providing unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the providing unit adjusts the format of the content to be provided based on the estimated viewer's emotions. For example, if the viewer is excited, the providing unit can focus on highlights and action scenes. If the viewer is relaxed, the providing unit can provide the viewer with the overall flow of the game and details of the tactics. If the viewer is nervous, the providing unit can provide content that gives the viewer a sense of security. In this way, by adjusting the format of the content based on the viewer's emotions, it is possible to provide the viewer with optimal content. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, an AI. For example, the providing unit may input viewer emotion data into an AI and have the AI ​​adjust the format of the content to be provided.

[0099] The providing unit can dynamically change the frequency of content provided depending on the progress of the match. The providing unit dynamically changes the frequency of content provided depending on, for example, the progress of the match. For example, in the early stages of the match, the providing unit frequently provides content related to players' initial positioning and tactical selection. In addition, the providing unit can focus on providing content related to battles and important events in the middle stages of the match. In addition, in the late stages of the match, the providing unit can focus on providing content related to decisive moments in victory or defeat and players' final tactics. In this way, by changing the frequency of content provision depending on the progress of the match, optimal content can be provided to viewers. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the progress of the match into AI and cause the AI ​​to dynamically change the frequency of content provided.

[0100] The providing unit can provide highlights of the current game based on the player's past performance data. The providing unit, for example, provides highlights based on the player's past performance data. For example, the providing unit provides highlights of the current game based on the player's past game data. The providing unit can also provide highlights of the current game based on the player's past tactical data. The providing unit can also provide highlights of the current game based on the player's past win / loss data. In this way, by providing highlights based on the player's past performance data, important scenes can be provided to viewers. Some or all of the above-described processing in the providing unit may be performed using, or without, an AI. For example, the providing unit can input the player's past performance data into an AI and cause the AI ​​to provide highlights of the current game.

[0101] The providing unit can enhance content provision using a specific event during a match as a trigger. For example, the providing unit enhances content provision using a specific event during a match as a trigger. For example, the providing unit can provide content including a detailed commentary on a play the moment a player achieves a kill. The providing unit can also provide content that provides a detailed commentary on the tactics and player movements the moment a team scores a goal. The providing unit can also provide content that analyzes and explains the impact of an important item the moment it is obtained. In this way, by enhancing content provision using a specific event as a trigger, detailed content of important moments can be provided to viewers. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input a specific event during a match into AI and cause the AI ​​to enhance content provision.

[0102] The providing unit can estimate the viewer's emotions and determine the priority of content to be provided based on the estimated viewer's emotions. The providing unit, for example, estimates the viewer's emotions. For example, the providing unit can estimate the viewer's emotions using facial expression recognition technology. The providing unit can also estimate the viewer's emotions using voice analysis technology. The providing unit can also estimate the viewer's emotions using survey results. For example, the providing unit can capture the viewer's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The providing unit can also record the viewer's voice and estimate the emotions using voice analysis technology. The providing unit can also conduct a survey of viewers and estimate the emotions based on the results. Next, the providing unit determines the priority of content to be provided based on the estimated viewer's emotions. For example, if the viewer is excited, action scenes and highlights can be preferentially provided. If the viewer is relaxed, the overall flow of the game and details of tactics can be preferentially provided. If the viewer is nervous, content that gives a sense of security can be preferentially provided. In this way, by determining the priority of content based on the viewer's emotions, it is possible to provide the optimal content to the viewer. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, an AI. For example, the providing unit may input viewer emotion data into an AI and have the AI ​​determine the priority of content to be provided.

[0103] The providing unit can provide content relevant to viewers in each region by taking into account the geographical location information of the match. The providing unit can provide content relevant to viewers in each region by taking into account the geographical location information of the match. For example, the providing unit can provide content about players or teams that viewers in each region are interested in. The providing unit can also provide content about specific events or moments in the match that viewers in each region are interested in. The providing unit can also provide content related to tactics or playing styles that viewers in each region are interested in. In this way, by providing content relevant to viewers in each region, it is possible to provide viewers with content that is specific to their region. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical location information of the match into AI and cause the AI ​​to provide content relevant to viewers in each region.

[0104] The providing unit can analyze social media trends and provide related content. The providing unit, for example, analyzes social media trends. For example, the providing unit can provide content about players or teams that are trending on social media. The providing unit can also provide content about specific events or moments in a game that are trending on social media. The providing unit can also provide content about tactics or playing styles that are trending on social media. In this way, by analyzing social media trends, viewers can be provided with content based on the latest trends. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input social media trend data into AI and cause the AI ​​to provide related content.

[0105] The providing unit can customize the content to be provided by reflecting the viewer's past viewing history. The providing unit customizes the content to be provided by reflecting, for example, the viewer's past viewing history. For example, the providing unit prioritizes providing content of matches or players that the viewer has previously viewed. The providing unit can also provide content related to tactics or playing styles in which the viewer has previously shown interest. The providing unit can also provide content of highlights or important moments of matches that the viewer has previously viewed. In this way, by reflecting the viewer's past viewing history, the most suitable content can be provided to the viewer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's past viewing history into AI and have the AI ​​customize the content to be provided. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, comment unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the comment unit can provide commentary and commentary in a natural commentary style based on the analysis results by the control unit 46A of the smart device 14. For example, the provision unit can provide commentary and commentary to viewers in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, comment unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the comment unit can provide commentary and commentary in a natural commentary style based on the analysis results by the control unit 46A of the smart glasses 214. For example, the provision unit can provide commentary and commentary to viewers in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, comment unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 and microphone 238 of the headset type terminal 314. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the comment unit can provide commentary and commentary in a natural commentary style based on the analysis results by the control unit 46A of the headset type terminal 314. For example, the provision unit can provide commentary and commentary to viewers in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, comment unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect game data using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the comment unit can provide commentary and narration in a natural commentary style based on the analysis results by the control unit 46A of the robot 414. For example, the provision unit can provide commentary and narration to viewers in real time by the specific processing unit 290 of the data processing device 12.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can also collect real-time reactions from viewers and provide them to the analysis unit. For example, if a viewer applauds or cheers at a particular scene, the collection unit collects that data and sends it to the analysis unit. Also, if a viewer posts a comment at a particular scene, the collection unit can collect that comment data and send it to the analysis unit. In this way, by collecting real-time reactions from viewers, it is possible to understand the viewer's interests and provide more personalized content.

[0108] The analysis unit can analyze real-time viewer reaction data to estimate the viewer's interests and concerns. For example, if a viewer applauds or cheers at a particular scene, the analysis unit can analyze that data to estimate that the viewer is interested in that scene. Also, if a viewer posts a comment at a particular scene, the analysis unit can analyze that comment data to estimate the viewer's interests. In this way, by analyzing the viewer's real-time reaction data, the viewer's interests and concerns can be understood and more personalized content can be provided.

[0109] The comment section can dynamically change the content of the comments based on real-time viewer reaction data. For example, if a viewer applauds or cheers at a particular scene, a detailed commentary about that scene can be added. Also, if a viewer posts a comment on a particular scene, a response to that comment can be included. This allows the content of the comments to be dynamically changed based on real-time viewer reaction data, providing a more personalized experience to the viewer.

[0110] The providing unit can dynamically change the format of the content to be provided based on the viewer's real-time reaction data. For example, if the viewer applauds or cheers at a particular scene, the providing unit can provide that scene as a highlight. Also, if the viewer posts a comment on a particular scene, the comment can be displayed. In this way, by dynamically changing the format of the content to be provided based on the viewer's real-time reaction data, it is possible to provide the viewer with a more personalized experience.

[0111] The collection unit can prioritize collection of data that is likely to interest the viewer based on the viewer's past viewing history. For example, if the viewer has shown interest in a particular player or team in the past, data related to that player or team can be collected preferentially. Also, if the viewer has shown interest in a particular tactic or playing style in the past, data related to that tactic or playing style can be collected preferentially. In this way, by collecting data preferentially based on the viewer's past viewing history, it is possible to provide the viewer with a more personalized experience.

[0112] The analysis unit can prioritize displaying analysis results that are likely to interest the viewer based on the viewer's past viewing history. For example, if the viewer has shown interest in a particular player or team in the past, analysis results related to that player or team can be prioritized. Also, if the viewer has shown interest in a particular tactic or playing style in the past, analysis results related to that tactic or playing style can be prioritized. This allows for a more personalized experience for the viewer by prioritized display of analysis results based on the viewer's past viewing history.

[0113] The comment section can prioritize comments that are likely to interest the viewer based on the viewer's past viewing history. For example, if a viewer has previously shown interest in a particular player or team, it can prioritize comments about that player or team. Also, if a viewer has previously shown interest in a particular tactic or playing style, it can prioritize comments about that tactic or playing style. This allows the viewer to have a more personalized experience by prioritizing comments based on the viewer's past viewing history.

[0114] The providing unit can provide content that is likely to interest a viewer based on the viewer's past viewing history with priority. For example, if a viewer has shown interest in a particular player or team in the past, content related to that player or team can be provided with priority. Also, if a viewer has shown interest in a particular tactic or playing style in the past, content related to that tactic or playing style can be provided with priority. In this way, by providing content with priority based on the viewer's past viewing history, a more personalized experience can be provided to the viewer.

[0115] The collection unit can prioritize collection of data that is likely to interest viewers based on real-time viewer reaction data. For example, if viewers applaud or cheer at a particular scene, data related to that scene can be collected preferentially. Also, if a viewer posts a comment at a particular scene, data related to that scene can be collected preferentially. In this way, by collecting data preferentially based on real-time viewer reaction data, it is possible to provide viewers with a more personalized experience.

[0116] The analysis unit can prioritize displaying analysis results that are likely to interest viewers based on real-time viewer reaction data. For example, if a viewer applauds or cheers at a particular scene, the analysis results for that scene can be prioritized. Also, if a viewer posts a comment at a particular scene, the analysis results for that scene can be prioritized. This makes it possible to provide viewers with a more personalized experience by prioritized display of analysis results based on real-time viewer reaction data.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects match data. Match data includes scores, player movements, tactics, etc. The collection unit collects match progress in real time and uses specific sensors to collect player movement and tactical data. For example, it collects player movement distance, speed, and actions. Step 2: The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms. For example, it analyzes player tactics, game development, tactical pattern recognition, and tactical success rate. Step 3: The commentary section provides commentary in a natural style based on the analysis results. This commentary includes audio commentary and text commentary. For example, it explains the players' tactics, provides live commentary on the game's development, automatically generates highlights of the game, and selects and edits important scenes. Step 4: The provider provides the commentary and commentary to viewers in real time. This can be streaming or on-demand. For example, the commentary and commentary can be streamed or on-demand.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects match data; an analysis unit that analyzes the data collected by the collection unit; a comment section that provides commentary and commentary based on the analysis results obtained by the analysis section; a providing unit that provides the live commentary and explanations provided by the comment unit to viewers. A system characterized by:

2. The collecting unit Collect data on at least one of the following: the progress of the match, player movements, and tactics 2. The system of claim 1.

3. The analysis unit Analyze the collected data to analyze player tactics and game developments 2. The system of claim 1.

4. The comment section Based on the analysis results, we provide live commentary and explanation in a natural commentary style.

2. The system of claim 1.

5. The providing unit Providing live commentary and commentary to viewers in real time 2. The system of claim 1.

6. The comment section Automatically generate match highlights and deliver them to viewers 2. The system of claim 1.

7. The collecting unit Estimate viewer sentiment and adjust the type of data you collect based on that sentiment 2. The system of claim 1.

8. The collecting unit Dynamically change the frequency of data collection depending on the progress of the match 2. The system of claim 1.

Images