System

Abstract

An object of the system according to the embodiment is to analyze player data and provide a quick and detailed profile to a commentator.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data of a player. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a profile of the player on the basis of the analysis result obtained by the analysis unit. The provision unit provides the profile generated by the generation unit to the commentator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for commentators to quickly grasp detailed profiles of athletes during live sports broadcasts.

[0005] The system according to the embodiment aims to analyze player data and provide commentators with quick and detailed profiles. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on players. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a player profile based on the analysis results obtained by the analysis unit. The provision unit provides the profile generated by the generation unit to a commentator. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the data of the players and provide commentators with a quick and detailed profile. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sports commentary system according to an embodiment of the present invention collects player data, analyzes it using AI, generates player profiles, and provides them to commentators. The sports commentary system collects data such as player past game data, statistical information, and interview articles, and analyzes it using AI to generate a profile that includes the player's characteristics, strengths, past performance, and other information. The generated profile is provided to commentators for use in commentary. For example, the sports commentary system collects statistical information such as the number of goals scored, assists, and number of games played by a player. Next, the sports commentary system uses AI to analyze the collected data to analyze the player's characteristics, strengths, past performance, and other information to generate a profile. For example, the AI ​​analyzes the player's scoring pattern, assist tendencies, and movements during a game to clarify the player's playing style. Next, the sports commentary system provides the generated profile to commentators. The commentators use the player's characteristics, strengths, past performance, and other information based on the generated profile in their commentary. For example, when a player scores a goal during a game, commenting on the player's scoring pattern and past performance can provide viewers with a deeper understanding of the game. This allows sports broadcast commentary systems to provide viewers with more detailed information about players, enhancing commentary during sports broadcasts. This allows sports broadcast commentary systems to collect player data, analyze it using AI, generate player profiles, and provide them to commentators. For example, by explaining a player's past performance and characteristics, viewers can more easily understand the player's playing style and strengths. In addition, because profiles are generated based on data analyzed by AI, commentators can provide commentary based on the most up-to-date information.

[0029] A sports broadcast commentary system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects player data. The player data includes, but is not limited to, past game data, statistical information, interview articles, social media posts, and fan comments. The collection unit collects, for example, past game data of players. For example, the collection unit may collect game scores, playing time, performance indicators, and the like. The collection unit may also collect player statistical information. For example, the collection unit may collect scoring percentage, assists, and rebounds. The collection unit may also collect player interview articles. For example, the collection unit may collect player interviews, coach comments, and the like. The collection unit may also collect player social media posts. For example, the collection unit may collect posts from Twitter (registered trademark), Instagram (registered trademark), Facebook (registered trademark), and the like. The collection unit may also collect fan comments. For example, the collection unit may collect social media comments, forum posts, and the like. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes a player's characteristics, strengths, and past performance based on the collected data. For example, the analysis unit analyzes a player's scoring patterns, assist tendencies, movements during a game, etc., to clarify the player's playing style. The generation unit generates a player profile based on the analysis results obtained by the analysis unit. The generation unit generates a profile including the player's performance, characteristics, past interview content, etc., based on the analysis results. The provision unit provides the profile generated by the generation unit to a commentator. The provision unit provides the generated profile to a commentator, for example, in digital format. The provision unit can also provide the generated profile to a commentator in paper form. The provision unit can also update the generated profile in real time. As a result, the sports broadcast commentary system according to the embodiment can collect and analyze player data, generate a profile, and provide it to a commentator. For example, by commenting on a player's past performance and characteristics, viewers can more easily understand the player's playing style and strengths.In addition, profiles are generated based on data analyzed by AI, allowing commentators to provide commentary based on the most up-to-date information.

[0030] The collection unit may collect data including a player's past game data, statistical information, interview articles, social media posts, and fan comments. The collection unit may, for example, collect a player's past game data, such as game scores, playing time, and performance indicators. The collection unit may also collect a player's statistical information, such as scoring percentage, assists, and rebounds. The collection unit may also collect a player's interview articles, such as interviews with the player and comments from coaches. The collection unit may also collect a player's social media posts, such as posts on Twitter, Instagram, and Facebook. The collection unit may also collect fan comments, such as social media comments and forum posts. By collecting various data, a detailed player profile can be generated. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input a player's social media posts into AI, which then automatically collects the data.

[0031] The analysis unit can analyze a player's characteristics, strengths, and past performance based on the collected data. The analysis unit, for example, analyzes a player's characteristics, strengths, and past performance based on the collected data. For example, the analysis unit analyzes a player's scoring patterns, assist tendencies, movements during a game, etc. to clarify the player's playing style. The analysis unit can also use a machine learning algorithm to analyze a player's characteristics and strengths. For example, the analysis unit can analyze a player's scoring patterns using a machine learning algorithm to clarify the player's characteristics. The analysis unit can also use statistical analysis to analyze a player's past performance. For example, the analysis unit can analyze a player's game wins and losses and individual performance indicators using statistical analysis to clarify the player's performance. In this way, a detailed profile can be generated by analyzing a player's characteristics, strengths, and past performance. Some or all of the above-described processing by 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 collected data into AI, and the AI ​​can automatically analyze the data.

[0032] The generation unit can generate a player profile based on the analysis results. The generation unit generates a profile including, for example, the player's performance, characteristics, and past interview content based on the analysis results. For example, the generation unit generates a profile based on statistical information such as the player's number of goals scored, number of assists, and number of games played. The generation unit can also generate a profile based on the player's characteristics and strengths. For example, the generation unit generates a profile based on the player's scoring pattern, assist tendencies, movements during games, and the like. The generation unit can also generate a profile based on the player's past interview content. For example, the generation unit generates a profile based on information obtained from an interview article about the player. In this way, generating a profile based on the analysis results can provide accurate information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into AI, which then automatically generates a profile.

[0033] The providing unit can provide the generated profile to the commentator. For example, the providing unit can provide the generated profile to the commentator in digital format. The providing unit can also provide the generated profile to the commentator in paper form. The providing unit can also update the generated profile in real time. For example, the providing unit can provide the generated profile to the commentator through a web application or a mobile application. The providing unit can also print the generated profile on a printer and provide it to the commentator. The providing unit can also send the generated profile to the commentator by email. In this way, providing the profile to the commentator enriches the commentary. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated profile into AI, and the AI ​​can automatically provide it to the commentator.

[0034] The providing unit can receive feedback from the commentator and reflect it in the next analysis. The providing unit, for example, receives feedback from the commentator and reflects it in the next analysis. For example, the providing unit collects the commentator's comments and evaluations and reflects them in the next analysis. The providing unit can also collect the commentator's areas for improvement and additional information and reflect them in the next analysis. For example, the providing unit adjusts the analysis algorithm based on the commentator's feedback to improve the accuracy of the next analysis. In this way, the accuracy of the analysis is improved by reflecting the feedback. Some or all of the above-mentioned processing in 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 commentator's feedback into AI, which can automatically reflect it in the next analysis.

[0035] The analysis unit can analyze the interview article using natural language processing. The analysis unit analyzes the interview article using, for example, natural language processing. For example, the analysis unit analyzes the words in the interview article using morphological analysis. The analysis unit can also analyze the sentence structure of the interview article using grammatical analysis. The analysis unit can also analyze the content of the interview article using semantic analysis. For example, the analysis unit analyzes the meaning of the sentences in the interview article to clarify the characteristics and strengths of the player. In this way, the analysis unit improves the accuracy of the analysis of the interview article by using natural language processing. 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 interview article into AI, and the AI ​​can automatically perform natural language processing.

[0036] The analysis unit can analyze statistical information using machine learning. The analysis unit analyzes statistical information using, for example, machine learning. For example, the analysis unit analyzes statistical information such as the number of goals scored and the number of assists scored by a player using regression analysis. The analysis unit can also analyze player performance data using a classification algorithm. The analysis unit can also analyze player match data using clustering. For example, the analysis unit clusters the player's scoring patterns and assist patterns to clarify the player's characteristics. This improves the accuracy of the analysis of statistical information by using machine learning. 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 statistical information into AI, which then automatically performs machine learning.

[0037] The collection unit can collect past game data of a player in real time and immediately reflect it in the analysis. The collection unit, for example, collects past game data of a player in real time and immediately reflects it in the analysis. For example, the collection unit can collect data on a player's points and assists in real time during a game and reflect it in the analysis. The collection unit can also collect player performance data immediately after the game ends and analyze it for the next game. The collection unit can also track the movements of players during a game in real time, collect the data, and reflect it in the analysis. In this way, by collecting data in real time, the latest information can be immediately reflected in the analysis. 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 data during a game into AI, which can automatically collect the data and reflect it in the analysis.

[0038] The collection unit can collect information about the player's health condition and injury history from medical data and reflect it in the profile. The collection unit, for example, collects information about the player's health condition and injury history from medical data and reflects it in the profile. For example, the collection unit collects the results of the player's regular medical checkup and reflects it in the profile. The collection unit can also collect information about the player's past treatments and surgeries from medical data and reflect it in the profile. The collection unit can also collect information about the player's injury recovery status from medical data and reflect it in the profile. In this way, by collecting medical data, the player's health condition and injury history can be accurately reflected in the profile. 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 medical data into AI, which can automatically collect the data and reflect it in the profile.

[0039] The collection unit can collect training data of the player and analyze changes in performance. The collection unit, for example, collects training data of the player and analyzes changes in performance. For example, the collection unit collects data of the player's training sessions and analyzes changes in performance. The collection unit can also collect the player's heart rate and calorie consumption during training and analyze changes in performance. The collection unit can also collect the contents of the player's training program and analyze changes in performance. In this way, by collecting training data, changes in the player's performance can be analyzed in detail. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input training data into AI, which then automatically collects data and analyzes changes in performance.

[0040] The collection unit can analyze the player's social media activity and collect fan reactions and comments. The collection unit, for example, analyzes the player's social media activity and collects fan reactions and comments. For example, the collection unit collects fan comments on the player's social media posts and reflects them in the analysis. The collection unit can also collect the frequency and content of the player's social media activity and reflect them in the profile. The collection unit can also collect the number of followers and engagement rate of the player on social media and reflect them in the analysis. In this way, by analyzing the social media activity, fan reactions and comments can be collected and reflected in the profile. 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 player's social media data into AI, which can automatically collect the data and reflect it in the analysis.

[0041] The collection unit can collect performance data for each region based on the geographical location information of the player. The collection unit, for example, collects performance data for each region taking into account the geographical location information of the player. For example, the collection unit collects performance data from games in which the player plays in different regions and reflects the data in the analysis. The collection unit can also collect differences in performance for each region based on the geographical location information of the player. The collection unit can also collect performance data from games in which the player plays in a specific region and reflect the data in the player's profile. In this way, by taking the geographical location information into account, detailed performance data for each region can be collected. 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 player into AI, which can automatically collect data and reflect the data in the analysis.

[0042] The collection unit can analyze past interview articles of players using natural language processing to extract emotions and intentions. For example, the collection unit can analyze past interview articles of players using natural language processing to extract emotions and intentions. For example, the collection unit can analyze interview articles of players using natural language processing to extract emotions and intentions. The collection unit can also extract specific keywords and phrases from the interview articles of players and reflect them in the profile. The collection unit can also analyze interview articles of players and reflect the psychological state and intentions of the players in the profile. In this way, emotions and intentions can be accurately extracted from the interview articles using natural language processing. 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 interview articles into AI, and the AI ​​can automatically perform natural language processing to extract emotions and intentions.

[0043] The analysis unit can perform video analysis of a player's movements during a game and analyze the playing style in detail. The analysis unit, for example, can perform video analysis of a player's movements during a game and analyze the playing style in detail. For example, the analysis unit can perform video analysis of a player's movements during a game and analyze the playing style in detail. The analysis unit can also perform video analysis of a player's scoring and assist scenes to clarify the playing style. The analysis unit can also perform video analysis of a player's movement patterns during a game and analyze the playing style. In this way, the video analysis can be used to analyze the playing style of a player in detail. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data from a game into AI, which can automatically perform video analysis and analyze the playing style.

[0044] The analysis unit can analyze a player's performance data in time series and visualize his / her growth and transition. The analysis unit, for example, analyzes a player's performance data in time series and visualizes his / her growth and transition. For example, the analysis unit can analyze a player's performance data in time series and visualize his / her growth and transition. The analysis unit can also analyze the transition of a player's number of goals and assists in time series and visualize his / her growth. The analysis unit can also analyze the transition of a player's movements during a game in time series and visualize his / her growth. In this way, the analysis in time series can visualize the player's growth and transition. 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 time series data into AI, which automatically performs time series analysis and visualizes his / her growth and transition.

[0045] The analysis unit can analyze the player's psychological state from interview articles and SNS posts and reflect it in the player's profile. The analysis unit, for example, can analyze the player's psychological state from interview articles and SNS posts and reflect it in the player's profile. For example, the analysis unit can analyze the player's interview article and reflect the psychological state in the player's profile. The analysis unit can also analyze the player's SNS posts and reflect the psychological state in the player's profile. The analysis unit can also analyze the player's psychological state from the player's interview articles and SNS posts and reflect it in the player's profile. In this way, by analyzing the interview articles and SNS posts, the player's psychological state can be accurately reflected in the player's profile. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input interview articles and SNS data into AI, which can automatically analyze the data and reflect the player's psychological state in the player's profile.

[0046] The analysis unit can analyze the statistical information of a player using machine learning to construct a performance prediction model. The analysis unit can, for example, analyze the statistical information of a player using machine learning to construct a performance prediction model. For example, the analysis unit can analyze statistical information on the number of goals and assists a player has using machine learning to construct a performance prediction model. The analysis unit can also analyze statistical information on the player's movements during a game using machine learning to construct a performance prediction model. The analysis unit can also analyze the player's past game data using machine learning to construct a performance prediction model. In this way, a model for predicting player performance can be constructed using machine learning. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input statistical information into AI, and the AI ​​can automatically perform machine learning to construct a prediction model.

[0047] The analysis unit can cluster the game data of a player and compare the player with similar players. The analysis unit, for example, clusters the game data of a player and compares the player with similar players. For example, the analysis unit can cluster the game data of a player and compare the player with similar players. The analysis unit can also cluster the player's scoring patterns and assist patterns and compare the player with similar players. The analysis unit can also cluster the player's movement patterns during a game and compare the player with similar players. This makes it possible to compare the player with similar players using clustering. 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 game data into AI, which can automatically perform clustering and compare the player with similar players.

[0048] The analysis unit can compare a player's performance with that of different sports or competitions and analyze similarities and differences. For example, the analysis unit can compare a player's performance with that of different sports or competitions and analyze similarities and differences. For example, the analysis unit can compare a player's performance with that of different sports or competitions and analyze similarities and differences. The analysis unit can also compare a player's scoring patterns and assist patterns with that of different sports or competitions and analyze similarities and differences. The analysis unit can also compare a player's movement patterns during a match with that of different sports or competitions and analyze similarities and differences. This allows for comparisons with different sports or competitions to provide a new perspective on a player's performance. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data from different sports or competitions into AI, which can then automatically analyze the data and identify similarities and differences.

[0049] The generation unit can include not only past performance but also future predictions in the player's profile. For example, the generation unit can include not only past performance but also future predictions in the player's profile. For example, the generation unit can include a future performance prediction in the profile based on the player's past performance. The generation unit can also analyze the player's growth pattern and include a future performance prediction in the profile. The generation unit can also include a performance prediction in future games in the profile based on the player's past game data. In this way, the inclusion of future predictions makes it possible to visualize the player's future performance. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past performance data into AI, which can automatically make future predictions and reflect them in the profile.

[0050] The generation unit can reflect fan comments and ratings in the player's profile. The generation unit, for example, reflects fan comments and ratings in the player's profile. For example, the generation unit can reflect fan comments on the player's social media posts in the profile. The generation unit can also reflect the player's post-match ratings as fan comments in the profile. The generation unit can also reflect fan ratings of the player's performance in the profile. In this way, the player's profile is more enriched by reflecting fan comments and ratings. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input fan comment data into AI, which can automatically analyze the data and reflect it in the profile.

[0051] The generation unit can include training data and health data in the player's profile. The generation unit, for example, includes the training data and health data in the player's profile. For example, the generation unit includes the player's training data in the profile. For example, the content and frequency of training sessions, performance data during training, etc. are included. The generation unit can also include the player's health data in the profile. For example, the results of regular health checkups, injury history, recovery status, etc. are included. The generation unit can also include the player's training program change history and training results in the profile. In this way, the inclusion of training data and health data can provide detailed information about the player. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the training data and health data into AI, which can automatically analyze the data and reflect it in the profile.

[0052] The generation unit can include comparison data with other players in the player's profile. The generation unit, for example, includes comparison data with other players in the player's profile. For example, the generation unit can include data comparing the player's number of goals and number of assists with other players in the profile. The generation unit can also include data comparing the player's movement patterns during a game with other players in the profile. The generation unit can also include data comparing the player's performance data with other players in the profile. In this way, by including comparison data with other players, the player's performance can be evaluated relatively. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the comparison data into AI, which can automatically analyze the data and reflect it in the profile.

[0053] The generation unit can include quotes and comments extracted from past interview articles in the player's profile. For example, the generation unit can include quotes and comments extracted from past interview articles in the player's profile. For example, the generation unit can include quotes extracted from past interview articles in the player's profile. The generation unit can also include comments extracted from the player's interview articles in the player's profile. The generation unit can also include moving episodes extracted from the player's interview articles in the player's profile. In this way, the inclusion of quotes and comments can convey the player's personality and character. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input interview articles into AI, which can automatically extract quotes and comments and reflect them in the profile.

[0054] The generation unit can include highlight scenes from a game as video links in the player's profile. For example, the generation unit includes highlight scenes from a game as video links in the player's profile. For example, the generation unit includes highlights of the player's scoring and assist scenes as video links in the profile. The generation unit can also include highlights of important plays from the player's game as video links in the profile. The generation unit can also include moving scenes from the player's game as video links in the profile. In this way, the inclusion of highlight scenes makes it possible to visually convey the player's performance. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input video data from a game into AI, which can automatically extract highlight scenes and reflect them in the profile as video links.

[0055] The providing unit can provide an interface that allows a commentator to update the profile in real time. The providing unit, for example, provides an interface that allows a commentator to update the profile in real time. For example, the providing unit provides an interface that allows a commentator to update a player's profile in real time during a match. The providing unit can also provide an interface that allows a commentator to input new data and instantly update the profile. The providing unit can also provide an interface that allows a commentator to update the profile based on the player's performance during a match. This allows the profile to be updated in real time, thereby providing the latest information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data entered by a commentator into AI, and the AI ​​can automatically update the profile.

[0056] The providing unit can provide a database that allows commentators to easily refer to past match data and analysis results. The providing unit, for example, provides a database that allows commentators to easily refer to past match data and analysis results. For example, the providing unit provides a database that allows commentators to easily refer to past match data. The providing unit can also provide a database that allows commentators to easily refer to players' past performances and statistical information. The providing unit can also provide a database that allows commentators to easily refer to players' past interview articles and social media posts. This allows commentators to easily refer to past data and analysis results, thereby improving the quality of commentary. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past data into AI, which automatically constructs a database that commentators can easily refer to.

[0057] The providing unit can add a function that allows a commentator to provide feedback on a profile. For example, the providing unit adds a function that allows a commentator to provide feedback on a profile. For example, the providing unit adds a function that allows a commentator to provide feedback on the content of a profile. The providing unit can also add a function that allows a commentator to provide feedback on the accuracy and detail of a profile. The providing unit can also add a function that allows a commentator to provide feedback on improvements to a profile or additional information. This allows the commentator to provide feedback, thereby improving the accuracy and content of the profile. Some or all of the above-described processing in 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 commentator's feedback into AI, which can automatically analyze the data and reflect it in the next profile generation.

[0058] The providing unit can provide a function that allows a commentator to customize a profile. The providing unit, for example, provides a function that allows a commentator to customize a profile. For example, the providing unit provides a function that allows a commentator to customize the display content of a player's profile. The providing unit can also provide a function that allows a commentator to customize the display order of a player's profile. The providing unit can also provide a function that allows a commentator to customize the display format of a player's profile. This allows the commentator to customize the profile, making it possible to provide information according to the commentator's needs. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a commentator's customization request into AI, and the AI ​​can automatically customize the profile.

[0059] The providing unit can provide a function that allows commentators to share their profiles with viewers. For example, the providing unit can provide a function that allows commentators to share their profiles with viewers. For example, the providing unit can provide a function that allows commentators to share player profiles with viewers in real time. The providing unit can also provide a function that allows commentators to share player profiles on social media or websites. The providing unit can also provide a function that allows commentators to send player profiles to viewers by email. This allows viewers to deepen their understanding by sharing profiles with viewers. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the profile data to be shared into AI, which can automatically analyze the data and provide it to viewers.

[0060] The providing unit can provide a function for a commentator to make a match prediction based on the profile. The providing unit, for example, provides a function for a commentator to make a match prediction based on the profile. For example, the providing unit can provide a function for a commentator to predict the outcome of a match based on the player's profile. The providing unit can also provide a function for a commentator to predict important plays during a match based on the player's profile. The providing unit can also provide a function for a commentator to predict the development of a match based on the player's profile. This improves the quality of commentary by predicting a match based on the profile. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input match prediction data into AI, which can automatically analyze the data and provide a prediction result.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The analysis unit can estimate a player's level of fatigue based on the player's performance data. For example, the analysis unit can analyze data such as the player's movements during a match, heart rate, and distance traveled to estimate the player's level of fatigue. The analysis unit can also analyze changes in the player's level of fatigue for each match based on the player's past match data. Furthermore, the analysis unit can estimate the player's level of fatigue after training based on the player's training data. This allows for a detailed analysis of the player's level of fatigue to be useful in managing the player's condition.

[0063] The information providing unit can propose a training plan to improve a player's performance based on the player's profile. For example, the information providing unit can propose an individual training plan based on the player's past performance and characteristics. The information providing unit can also propose a training plan to strengthen specific skills based on the player's scoring patterns and assist tendencies. Furthermore, the information providing unit can propose a training plan aimed at injury prevention and recovery based on the player's health data. This makes it possible to provide specific advice to improve a player's performance.

[0064] The collection unit collects audio data of players during a match, and the analysis unit analyzes the audio data, thereby clarifying the communication style of the players. For example, the collection unit collects instructions and conversations of players during a match. The collection unit can also collect audio of interviews with players. Furthermore, the collection unit can collect audio posts made by players on social media. This allows for a detailed analysis of the communication style of players, which can be used to evaluate their roles and leadership within the team.

[0065] The collection unit collects biometric data of the player during a match, and the analysis unit analyzes the data to estimate the player's stress level. For example, the collection unit collects the player's heart rate and electrodermal activity. The collection unit can also collect the player's breathing pattern. Furthermore, the collection unit can collect the player's body temperature and sweat rate. This allows for a detailed analysis of the player's stress level and identifies factors that affect performance during a match.

[0066] The data provision department can propose a career plan for a player based on the player's profile. For example, the data provision department can propose a future career plan based on the player's past performance and characteristics. The data provision department can also propose a specific position or role based on the player's scoring pattern or assist tendency. Furthermore, the data provision department can propose a career plan that takes injury risk into account based on the player's health data. This makes it possible to provide specific advice to support the player's career over the long term.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects player data. Player data includes past game data, statistical information, interviews, social media posts, fan comments, etc. For example, game scores, playing time, performance indicators, scoring percentage, assists, rebounds, player interviews, coach comments, Twitter, Instagram, Facebook posts, social media comments, forum posts, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the characteristics and strengths of players, their past performance, scoring patterns, assist tendencies, and movements during games to clarify the players' playing styles. Step 3: The generation unit generates a player profile based on the analysis results obtained by the analysis unit. For example, the generation unit generates a profile including the player's performance, characteristics, past interview content, etc. based on the analysis results. Step 4: The providing unit provides the profile generated by the generating unit to the commentator. For example, the generated profile may be provided to the commentator in a digital format or in paper form and updated in real time.

[0069] (Example 2) A sports commentary system according to an embodiment of the present invention collects player data, analyzes it using AI, generates player profiles, and provides them to commentators. The sports commentary system collects data such as player past game data, statistical information, and interview articles, and analyzes it using AI to generate a profile that includes the player's characteristics, strengths, past performance, and other information. The generated profile is provided to commentators for use in commentary. For example, the sports commentary system collects statistical information such as the number of goals scored, assists, and number of games played by a player. Next, the sports commentary system uses AI to analyze the collected data to analyze the player's characteristics, strengths, past performance, and other information to generate a profile. For example, the AI ​​analyzes the player's scoring pattern, assist tendencies, and movements during a game to clarify the player's playing style. Next, the sports commentary system provides the generated profile to commentators. The commentators use the player's characteristics, strengths, past performance, and other information based on the generated profile in their commentary. For example, when a player scores a goal during a game, commenting on the player's scoring pattern and past performance can provide viewers with a deeper understanding of the game. This allows sports broadcast commentary systems to provide viewers with more detailed information about players, enhancing commentary during sports broadcasts. This allows sports broadcast commentary systems to collect player data, analyze it using AI, generate player profiles, and provide them to commentators. For example, by explaining a player's past performance and characteristics, viewers can more easily understand the player's playing style and strengths. In addition, because profiles are generated based on data analyzed by AI, commentators can provide commentary based on the most up-to-date information.

[0070] A sports broadcast commentary system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects player data. The player data includes, but is not limited to, past game data, statistical information, interview articles, social media posts, and fan comments. The collection unit, for example, collects past game data of players. For example, the collection unit may collect game scores, playing time, and performance indicators. The collection unit may also collect player statistical information. For example, the collection unit may collect scoring percentage, assist numbers, and rebound numbers. The collection unit may also collect player interview articles. For example, the collection unit may collect player interviews and coach comments. The collection unit may also collect player social media posts. For example, the collection unit may collect posts on Twitter, Instagram, Facebook, and the like. The collection unit may also collect fan comments. For example, the collection unit may collect social media comments and forum posts. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the player's characteristics, strengths, and past performance based on the collected data. For example, the analysis unit analyzes a player's scoring patterns, assist tendencies, movements during a game, etc., to clarify the player's playing style. The generation unit generates a player profile based on the analysis results obtained by the analysis unit. The generation unit generates a profile including, for example, the player's performance, characteristics, past interview content, etc., based on the analysis results. The provision unit provides the profile generated by the generation unit to a commentator. The provision unit provides the generated profile to a commentator, for example, in digital format. The provision unit can also provide the generated profile to a commentator in paper form. The provision unit can also update the generated profile in real time. In this way, the sports broadcast commentary system according to the embodiment can collect and analyze player data, generate a profile, and provide it to a commentator. For example, by commenting on a player's past performance and characteristics, viewers can more easily understand the player's playing style and strengths.In addition, profiles are generated based on data analyzed by AI, allowing commentators to provide commentary based on the most up-to-date information.

[0071] The collection unit may collect data including a player's past game data, statistical information, interview articles, social media posts, and fan comments. The collection unit may, for example, collect a player's past game data, such as game scores, playing time, and performance indicators. The collection unit may also collect a player's statistical information, such as scoring percentage, assists, and rebounds. The collection unit may also collect a player's interview articles, such as interviews with the player and comments from coaches. The collection unit may also collect a player's social media posts, such as posts on Twitter, Instagram, and Facebook. The collection unit may also collect fan comments, such as social media comments and forum posts. By collecting various data, a detailed player profile can be generated. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input a player's social media posts into AI, which then automatically collects the data.

[0072] The analysis unit can analyze a player's characteristics, strengths, and past performance based on the collected data. The analysis unit, for example, analyzes a player's characteristics, strengths, and past performance based on the collected data. For example, the analysis unit analyzes a player's scoring patterns, assist tendencies, movements during a game, etc. to clarify the player's playing style. The analysis unit can also use a machine learning algorithm to analyze a player's characteristics and strengths. For example, the analysis unit can analyze a player's scoring patterns using a machine learning algorithm to clarify the player's characteristics. The analysis unit can also use statistical analysis to analyze a player's past performance. For example, the analysis unit can analyze a player's game wins and losses and individual performance indicators using statistical analysis to clarify the player's performance. In this way, a detailed profile can be generated by analyzing a player's characteristics, strengths, and past performance. Some or all of the above-described processing by 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 collected data into AI, and the AI ​​can automatically analyze the data.

[0073] The generation unit can generate a player profile based on the analysis results. The generation unit generates a profile including, for example, the player's performance, characteristics, and past interview content based on the analysis results. For example, the generation unit generates a profile based on statistical information such as the player's number of goals scored, number of assists, and number of games played. The generation unit can also generate a profile based on the player's characteristics and strengths. For example, the generation unit generates a profile based on the player's scoring pattern, assist tendencies, movements during games, and the like. The generation unit can also generate a profile based on the player's past interview content. For example, the generation unit generates a profile based on information obtained from an interview article about the player. In this way, generating a profile based on the analysis results can provide accurate information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into AI, which then automatically generates a profile.

[0074] The providing unit can provide the generated profile to the commentator. For example, the providing unit can provide the generated profile to the commentator in digital format. The providing unit can also provide the generated profile to the commentator in paper form. The providing unit can also update the generated profile in real time. For example, the providing unit can provide the generated profile to the commentator through a web application or a mobile application. The providing unit can also print the generated profile on a printer and provide it to the commentator. The providing unit can also send the generated profile to the commentator by email. In this way, providing the profile to the commentator enriches the commentary. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated profile into AI, and the AI ​​can automatically provide it to the commentator.

[0075] The providing unit can receive feedback from the commentator and reflect it in the next analysis. The providing unit, for example, receives feedback from the commentator and reflects it in the next analysis. For example, the providing unit collects the commentator's comments and evaluations and reflects them in the next analysis. The providing unit can also collect the commentator's areas for improvement and additional information and reflect them in the next analysis. For example, the providing unit adjusts the analysis algorithm based on the commentator's feedback to improve the accuracy of the next analysis. In this way, the accuracy of the analysis is improved by reflecting the feedback. Some or all of the above-mentioned processing in 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 commentator's feedback into AI, which can automatically reflect it in the next analysis.

[0076] The analysis unit can analyze the interview article using natural language processing. The analysis unit analyzes the interview article using, for example, natural language processing. For example, the analysis unit analyzes the words in the interview article using morphological analysis. The analysis unit can also analyze the sentence structure of the interview article using grammatical analysis. The analysis unit can also analyze the content of the interview article using semantic analysis. For example, the analysis unit analyzes the meaning of the sentences in the interview article to clarify the characteristics and strengths of the player. In this way, the analysis unit improves the accuracy of the analysis of the interview article by using natural language processing. 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 interview article into AI, and the AI ​​can automatically perform natural language processing.

[0077] The analysis unit can analyze statistical information using machine learning. The analysis unit analyzes statistical information using, for example, machine learning. For example, the analysis unit analyzes statistical information such as the number of goals scored and the number of assists scored by a player using regression analysis. The analysis unit can also analyze player performance data using a classification algorithm. The analysis unit can also analyze player match data using clustering. For example, the analysis unit clusters the player's scoring patterns and assist patterns to clarify the player's characteristics. This improves the accuracy of the analysis of statistical information by using machine learning. 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 statistical information into AI, which then automatically performs machine learning.

[0078] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, when the user is excited, the collection unit collects data in real time and immediately reflects the data in the analysis. Furthermore, when the user is relaxed, the collection unit can periodically collect data and adjust the frequency of analysis. Furthermore, when the user is stressed, the collection unit can reduce the frequency of data collection and collect only the minimum amount of data necessary. This enables efficient data collection by adjusting the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 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 the user's emotion data into an AI, which can automatically adjust the timing of data collection.

[0079] The collection unit can collect past game data of a player in real time and immediately reflect it in the analysis. The collection unit, for example, collects past game data of a player in real time and immediately reflects it in the analysis. For example, the collection unit can collect data on a player's points and assists in real time during a game and reflect it in the analysis. The collection unit can also collect player performance data immediately after the game ends and analyze it for the next game. The collection unit can also track the movements of players during a game in real time, collect the data, and reflect it in the analysis. In this way, by collecting data in real time, the latest information can be immediately reflected in the analysis. 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 data during a game into AI, which can automatically collect the data and reflect it in the analysis.

[0080] The collection unit can collect information about the player's health condition and injury history from medical data and reflect it in the profile. The collection unit, for example, collects information about the player's health condition and injury history from medical data and reflects it in the profile. For example, the collection unit collects the results of the player's regular medical checkup and reflects it in the profile. The collection unit can also collect information about the player's past treatments and surgeries from medical data and reflect it in the profile. The collection unit can also collect information about the player's injury recovery status from medical data and reflect it in the profile. In this way, by collecting medical data, the player's health condition and injury history can be accurately reflected in the profile. 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 medical data into AI, which can automatically collect the data and reflect it in the profile.

[0081] The collection unit can collect training data of the player and analyze changes in performance. The collection unit, for example, collects training data of the player and analyzes changes in performance. For example, the collection unit collects data of the player's training sessions and analyzes changes in performance. The collection unit can also collect the player's heart rate and calorie consumption during training and analyze changes in performance. The collection unit can also collect the contents of the player's training program and analyze changes in performance. In this way, by collecting training data, changes in the player's performance can be analyzed in detail. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input training data into AI, which then automatically collects data and analyzes changes in performance.

[0082] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting data from the game. Furthermore, if the user is relaxed, the collection unit can prioritize collecting past game data and statistical information. Furthermore, if the user is stressed, the collection unit can prioritize collecting only important data. This enables efficient data collection by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 the user's emotion data into an AI, which then automatically determines the priority of the data.

[0083] The collection unit can analyze the player's social media activity and collect fan reactions and comments. The collection unit, for example, analyzes the player's social media activity and collects fan reactions and comments. For example, the collection unit collects fan comments on the player's social media posts and reflects them in the analysis. The collection unit can also collect the frequency and content of the player's social media activity and reflect them in the profile. The collection unit can also collect the number of followers and engagement rate of the player on social media and reflect them in the analysis. In this way, by analyzing the social media activity, fan reactions and comments can be collected and reflected in the profile. 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 player's social media data into AI, which can automatically collect the data and reflect it in the analysis.

[0084] The collection unit can collect performance data for each region based on the geographical location information of the player. The collection unit, for example, collects performance data for each region taking into account the geographical location information of the player. For example, the collection unit collects performance data from games in which the player plays in different regions and reflects the data in the analysis. The collection unit can also collect differences in performance for each region based on the geographical location information of the player. The collection unit can also collect performance data from games in which the player plays in a specific region and reflect the data in the player's profile. In this way, by taking the geographical location information into account, detailed performance data for each region can be collected. 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 player into AI, which can automatically collect data and reflect the data in the analysis.

[0085] The collection unit can analyze past interview articles of players using natural language processing to extract emotions and intentions. For example, the collection unit can analyze past interview articles of players using natural language processing to extract emotions and intentions. For example, the collection unit can analyze interview articles of players using natural language processing to extract emotions and intentions. The collection unit can also extract specific keywords and phrases from the interview articles of players and reflect them in the profile. The collection unit can also analyze interview articles of players and reflect the psychological state and intentions of the players in the profile. In this way, emotions and intentions can be accurately extracted from the interview articles using natural language processing. 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 interview articles into AI, and the AI ​​can automatically perform natural language processing to extract emotions and intentions.

[0086] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, when the user is excited, the analysis unit performs a detailed analysis to increase the accuracy. Furthermore, when the user is relaxed, the analysis unit can adjust the accuracy of the analysis and provide only the necessary information. Furthermore, when the user is stressed, the analysis unit can lower the accuracy of the analysis and provide concise information. This allows for adjusting the accuracy of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then automatically adjust the accuracy of the analysis.

[0087] The analysis unit can perform video analysis of a player's movements during a game and analyze the playing style in detail. The analysis unit, for example, can perform video analysis of a player's movements during a game and analyze the playing style in detail. For example, the analysis unit can perform video analysis of a player's movements during a game and analyze the playing style in detail. The analysis unit can also perform video analysis of a player's scoring and assist scenes to clarify the playing style. The analysis unit can also perform video analysis of a player's movement patterns during a game and analyze the playing style. In this way, the video analysis can be used to analyze the playing style of a player in detail. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video data from a game into AI, which can automatically perform video analysis and analyze the playing style.

[0088] The analysis unit can analyze a player's performance data in time series and visualize his / her growth and transition. The analysis unit, for example, analyzes a player's performance data in time series and visualizes his / her growth and transition. For example, the analysis unit can analyze a player's performance data in time series and visualize his / her growth and transition. The analysis unit can also analyze the transition of a player's number of goals and assists in time series and visualize his / her growth. The analysis unit can also analyze the transition of a player's movements during a game in time series and visualize his / her growth. In this way, the analysis in time series can visualize the player's growth and transition. 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 time series data into AI, which automatically performs time series analysis and visualizes his / her growth and transition.

[0089] The analysis unit can analyze the player's psychological state from interview articles and SNS posts and reflect it in the player's profile. The analysis unit, for example, can analyze the player's psychological state from interview articles and SNS posts and reflect it in the player's profile. For example, the analysis unit can analyze the player's interview article and reflect the psychological state in the player's profile. The analysis unit can also analyze the player's SNS posts and reflect the psychological state in the player's profile. The analysis unit can also analyze the player's psychological state from the player's interview articles and SNS posts and reflect it in the player's profile. In this way, by analyzing the interview articles and SNS posts, the player's psychological state can be accurately reflected in the player's profile. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input interview articles and SNS data into AI, which can automatically analyze the data and reflect the player's psychological state in the player's profile.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can display detailed analysis results when the user is excited. The analysis unit can also display concise analysis results when the user is relaxed. The analysis unit can also display visually easy-to-understand analysis results when the user is stressed. This allows for more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI, which can automatically adjust the display method of the analysis results.

[0091] The analysis unit can analyze the statistical information of a player using machine learning to construct a performance prediction model. The analysis unit can, for example, analyze the statistical information of a player using machine learning to construct a performance prediction model. For example, the analysis unit can analyze statistical information on the number of goals and assists a player has using machine learning to construct a performance prediction model. The analysis unit can also analyze statistical information on the player's movements during a game using machine learning to construct a performance prediction model. The analysis unit can also analyze the player's past game data using machine learning to construct a performance prediction model. In this way, a model for predicting player performance can be constructed using machine learning. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input statistical information into AI, and the AI ​​can automatically perform machine learning to construct a prediction model.

[0092] The analysis unit can cluster the game data of a player and compare the player with similar players. The analysis unit, for example, clusters the game data of a player and compares the player with similar players. For example, the analysis unit can cluster the game data of a player and compare the player with similar players. The analysis unit can also cluster the player's scoring patterns and assist patterns and compare the player with similar players. The analysis unit can also cluster the player's movement patterns during a game and compare the player with similar players. This makes it possible to compare the player with similar players using clustering. 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 game data into AI, which can automatically perform clustering and compare the player with similar players.

[0093] The analysis unit can compare a player's performance with that of different sports or competitions and analyze similarities and differences. For example, the analysis unit can compare a player's performance with that of different sports or competitions and analyze similarities and differences. For example, the analysis unit can compare a player's performance with that of different sports or competitions and analyze similarities and differences. The analysis unit can also compare a player's scoring patterns and assist patterns with that of different sports or competitions and analyze similarities and differences. The analysis unit can also compare a player's movement patterns during a match with that of different sports or competitions and analyze similarities and differences. This allows for comparisons with different sports or competitions to provide a new perspective on a player's performance. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data from different sports or competitions into AI, which can then automatically analyze the data and identify similarities and differences.

[0094] The generation unit can estimate the user's emotions and adjust the profile presentation style based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the profile presentation style based on the estimated user emotions. For example, if the user is excited, the generation unit can generate a visually stimulating profile. Furthermore, if the user is relaxed, the generation unit can generate a profile with a calm design. Furthermore, if the user is stressed, the generation unit can generate a simple, highly visible profile. This allows for adjusting the profile presentation style according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into an AI, which can then automatically adjust the profile presentation style.

[0095] The generation unit can include not only past performance but also future predictions in the player's profile. For example, the generation unit can include not only past performance but also future predictions in the player's profile. For example, the generation unit can include a future performance prediction in the profile based on the player's past performance. The generation unit can also analyze the player's growth pattern and include a future performance prediction in the profile. The generation unit can also include a performance prediction in future games in the profile based on the player's past game data. In this way, the inclusion of future predictions makes it possible to visualize the player's future performance. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past performance data into AI, which can automatically make future predictions and reflect them in the profile.

[0096] The generation unit can reflect fan comments and ratings in the player's profile. The generation unit, for example, reflects fan comments and ratings in the player's profile. For example, the generation unit can reflect fan comments on the player's social media posts in the profile. The generation unit can also reflect the player's post-match ratings as fan comments in the profile. The generation unit can also reflect fan ratings of the player's performance in the profile. In this way, the player's profile is more enriched by reflecting fan comments and ratings. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input fan comment data into AI, which can automatically analyze the data and reflect it in the profile.

[0097] The generation unit can include training data and health data in the player's profile. The generation unit, for example, includes the training data and health data in the player's profile. For example, the generation unit includes the player's training data in the profile. For example, the content and frequency of training sessions, performance data during training, etc. are included. The generation unit can also include the player's health data in the profile. For example, the results of regular health checkups, injury history, recovery status, etc. are included. The generation unit can also include the player's training program change history and training results in the profile. In this way, the inclusion of training data and health data can provide detailed information about the player. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the training data and health data into AI, which can automatically analyze the data and reflect it in the profile.

[0098] The generation unit can estimate the user's emotions and adjust the length of the profile based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the profile based on the estimated user emotions. For example, if the user is excited, the generation unit can generate a longer profile including detailed information. Also, if the user is relaxed, the generation unit can generate a profile of appropriate length. Also, if the user is stressed, the generation unit can generate a concise and short profile. This allows for adjusting the length of the profile according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into an AI, which can then automatically adjust the length of the profile.

[0099] The generation unit can include comparison data with other players in the player's profile. The generation unit, for example, includes comparison data with other players in the player's profile. For example, the generation unit can include data comparing the player's number of goals and number of assists with other players in the profile. The generation unit can also include data comparing the player's movement patterns during a game with other players in the profile. The generation unit can also include data comparing the player's performance data with other players in the profile. In this way, by including comparison data with other players, the player's performance can be evaluated relatively. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the comparison data into AI, which can automatically analyze the data and reflect it in the profile.

[0100] The generation unit can include quotes and comments extracted from past interview articles in the player's profile. For example, the generation unit can include quotes and comments extracted from past interview articles in the player's profile. For example, the generation unit can include quotes extracted from past interview articles in the player's profile. The generation unit can also include comments extracted from the player's interview articles in the player's profile. The generation unit can also include moving episodes extracted from the player's interview articles in the player's profile. In this way, the inclusion of quotes and comments can convey the player's personality and character. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input interview articles into AI, which can automatically extract quotes and comments and reflect them in the profile.

[0101] The generation unit can include highlight scenes from a game as video links in the player's profile. For example, the generation unit includes highlight scenes from a game as video links in the player's profile. For example, the generation unit includes highlights of the player's scoring and assist scenes as video links in the profile. The generation unit can also include highlights of important plays from the player's game as video links in the profile. The generation unit can also include moving scenes from the player's game as video links in the profile. In this way, the inclusion of highlight scenes makes it possible to visually convey the player's performance. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input video data from a game into AI, which can automatically extract highlight scenes and reflect them in the profile as video links.

[0102] The providing unit can estimate the user's emotions and adjust the profile provision method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the profile provision method based on the estimated user emotions. For example, if the user is excited, the providing unit can provide the profile in a visually stimulating manner. Furthermore, if the user is relaxed, the providing unit can provide the profile in a calm manner. Furthermore, if the user is stressed, the providing unit can provide the profile in a simple, highly visible manner. This allows for adjusting the provision method according to the user's emotions to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into an AI, which can automatically adjust the profile provision method.

[0103] The providing unit can provide an interface that allows a commentator to update the profile in real time. The providing unit, for example, provides an interface that allows a commentator to update the profile in real time. For example, the providing unit provides an interface that allows a commentator to update a player's profile in real time during a match. The providing unit can also provide an interface that allows a commentator to input new data and instantly update the profile. The providing unit can also provide an interface that allows a commentator to update the profile based on the player's performance during a match. This allows the profile to be updated in real time, thereby providing the latest information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data entered by a commentator into AI, and the AI ​​can automatically update the profile.

[0104] The providing unit can provide a database that allows commentators to easily refer to past match data and analysis results. The providing unit, for example, provides a database that allows commentators to easily refer to past match data and analysis results. For example, the providing unit provides a database that allows commentators to easily refer to past match data. The providing unit can also provide a database that allows commentators to easily refer to players' past performances and statistical information. The providing unit can also provide a database that allows commentators to easily refer to players' past interview articles and social media posts. This allows commentators to easily refer to past data and analysis results, thereby improving the quality of commentary. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past data into AI, which automatically constructs a database that commentators can easily refer to.

[0105] The providing unit can add a function that allows a commentator to provide feedback on a profile. For example, the providing unit adds a function that allows a commentator to provide feedback on a profile. For example, the providing unit adds a function that allows a commentator to provide feedback on the content of a profile. The providing unit can also add a function that allows a commentator to provide feedback on the accuracy and detail of a profile. The providing unit can also add a function that allows a commentator to provide feedback on improvements to a profile or additional information. This allows the commentator to provide feedback, thereby improving the accuracy and content of the profile. Some or all of the above-described processing in 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 commentator's feedback into AI, which can automatically analyze the data and reflect it in the next profile generation.

[0106] The providing unit can estimate the user's emotions and determine the priority of profiles to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of profiles to be provided based on the estimated user emotions. For example, when the user is excited, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is stressed, the providing unit can prioritize providing concise information. This allows for determining the priority of profiles according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI, which can automatically determine the priority of profiles.

[0107] The providing unit can provide a function that allows a commentator to customize a profile. The providing unit, for example, provides a function that allows a commentator to customize a profile. For example, the providing unit provides a function that allows a commentator to customize the display content of a player's profile. The providing unit can also provide a function that allows a commentator to customize the display order of a player's profile. The providing unit can also provide a function that allows a commentator to customize the display format of a player's profile. This allows the commentator to customize the profile, making it possible to provide information according to the commentator's needs. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a commentator's customization request into AI, and the AI ​​can automatically customize the profile.

[0108] The providing unit can provide a function that allows commentators to share their profiles with viewers. For example, the providing unit can provide a function that allows commentators to share their profiles with viewers. For example, the providing unit can provide a function that allows commentators to share player profiles with viewers in real time. The providing unit can also provide a function that allows commentators to share player profiles on social media or websites. The providing unit can also provide a function that allows commentators to send player profiles to viewers by email. This allows viewers to deepen their understanding by sharing profiles with viewers. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the profile data to be shared into AI, which can automatically analyze the data and provide it to viewers.

[0109] The providing unit can provide a function for a commentator to make a match prediction based on the profile. The providing unit, for example, provides a function for a commentator to make a match prediction based on the profile. For example, the providing unit can provide a function for a commentator to predict the outcome of a match based on the player's profile. The providing unit can also provide a function for a commentator to predict important plays during a match based on the player's profile. The providing unit can also provide a function for a commentator to predict the development of a match based on the player's profile. This improves the quality of commentary by predicting a match based on the profile. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input match prediction data into AI, which can automatically analyze the data and provide a prediction result. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation 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 player data using the camera 42 and microphone 38B of the smart device 14. The analysis unit, for example, analyzes the collected data by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a player profile based on the analysis result by the specific processing unit 290 of the data processing device 12. The provision unit, for example, can provide the profile generated by the control unit 46A of the smart device 14 to a commentator. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect player data using the camera 42 and the microphone 238 of the smart glasses 214. The analysis unit, for example, analyzes the collected data by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a player profile based on the analysis result by the specific processing unit 290 of the data processing device 12. The provision unit, for example, can provide the profile generated by the control unit 46A of the smart glasses 214 to a commentator. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect player data using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit, for example, analyzes the collected data by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a player profile based on the analysis result by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the profile generated by the control unit 46A of the headset type terminal 314 to a commentator. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit 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 player data using the camera 42 and microphone 238 of the robot 414. The analysis unit, for example, analyzes the collected data by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a player profile based on the analysis result by the specific processing unit 290 of the data processing device 12. The provision unit, for example, can provide the profile generated by the control unit 46A of the robot 414 to a commentator.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate a player's level of fatigue based on the player's performance data. For example, the analysis unit can analyze data such as the player's movements during a match, heart rate, and distance traveled to estimate the player's level of fatigue. The analysis unit can also analyze changes in the player's level of fatigue for each match based on the player's past match data. Furthermore, the analysis unit can estimate the player's level of fatigue after training based on the player's training data. This allows for a detailed analysis of the player's level of fatigue to be useful in managing the player's condition.

[0112] The information providing unit can propose a training plan to improve a player's performance based on the player's profile. For example, the information providing unit can propose an individual training plan based on the player's past performance and characteristics. The information providing unit can also propose a training plan to strengthen specific skills based on the player's scoring patterns and assist tendencies. Furthermore, the information providing unit can propose a training plan aimed at injury prevention and recovery based on the player's health data. This makes it possible to provide specific advice to improve a player's performance.

[0113] The collection unit collects audio data of players during a match, and the analysis unit analyzes the audio data, thereby clarifying the communication style of the players. For example, the collection unit collects instructions and conversations of players during a match. The collection unit can also collect audio of interviews with players. Furthermore, the collection unit can collect audio posts made by players on social media. This allows for a detailed analysis of the communication style of players, which can be used to evaluate their roles and leadership within the team.

[0114] The analysis unit can estimate a player's psychological state and predict the player's performance based on the estimated psychological state. For example, the analysis unit can analyze a player's interview articles and social media posts to estimate the player's psychological state. The analysis unit can also estimate the player's psychological state by analyzing video of the player's facial expressions and movements during a game. Furthermore, the analysis unit can analyze the relationship between the player's psychological state and performance based on the player's past game data and predict future performance. This makes it possible to predict performance taking the player's psychological state into account.

[0115] The information provision department can propose measures to improve a player's engagement with fans based on the player's profile. For example, the information provision department can propose ways to communicate with fans based on the player's social media activities and fan comments. The information provision department can also propose plans for fan events and autograph sessions based on the player's performance during a game. Furthermore, the information provision department can propose content for fans based on the player's interview articles and past anecdotes. This makes it possible to provide specific measures to strengthen engagement between players and fans.

[0116] The collection unit collects biometric data of the player during a match, and the analysis unit analyzes the data to estimate the player's stress level. For example, the collection unit collects the player's heart rate and electrodermal activity. The collection unit can also collect the player's breathing pattern. Furthermore, the collection unit can collect the player's body temperature and sweat rate. This allows for a detailed analysis of the player's stress level and identifies factors that affect performance during a match.

[0117] The analysis unit can estimate a player's motivation level based on the player's performance data. For example, the analysis unit can estimate the player's motivation level by analyzing the player's movements and scoring patterns during a match. The analysis unit can also estimate the player's motivation level during training based on the player's training data. Furthermore, the analysis unit can also estimate the player's motivation level by analyzing the player's interview articles and social media posts. This allows for a detailed analysis of the player's motivation level and allows for measures to be taken to improve performance.

[0118] The data provision department can propose a career plan for a player based on the player's profile. For example, the data provision department can propose a future career plan based on the player's past performance and characteristics. The data provision department can also propose a specific position or role based on the player's scoring pattern or assist tendency. Furthermore, the data provision department can propose a career plan that takes injury risk into account based on the player's health data. This makes it possible to provide specific advice to support the player's career over the long term.

[0119] The collection unit collects facial expression data of players during a match, and the analysis unit analyzes the data to estimate the player's emotions. For example, the collection unit collects facial expressions of players during a match using a camera. The collection unit can also collect facial expressions of players during interviews. Furthermore, the collection unit can collect facial expression data from photos and videos included in the player's social media posts. This allows for detailed analysis of the player's emotions and an understanding of their psychological state during a match.

[0120] The data provision department can propose measures to support the mental health of athletes based on their profiles. For example, the data provision department can propose a mental health support plan based on the athletes' past performance and characteristics. The data provision department can also suggest stress management and relaxation methods based on the athletes' interview articles and social media posts. Furthermore, the data provision department can suggest collaboration with mental health experts based on the athletes' health data. This makes it possible to provide specific measures to comprehensively support the athletes' mental health.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects player data. Player data includes past game data, statistical information, interviews, social media posts, fan comments, etc. For example, game scores, playing time, performance indicators, scoring percentage, assists, rebounds, player interviews, coach comments, Twitter, Instagram, Facebook posts, social media comments, forum posts, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the characteristics and strengths of players, their past performance, scoring patterns, assist tendencies, and movements during games to clarify the players' playing styles. Step 3: The generation unit generates a player profile based on the analysis results obtained by the analysis unit. For example, the generation unit generates a profile including the player's performance, characteristics, past interview content, etc. based on the analysis results. Step 4: The providing unit provides the profile generated by the generating unit to the commentator. For example, the generated profile may be provided to the commentator in a digital format or in paper form and updated in real time.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 data on players; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a player profile based on the analysis results obtained by the analysis unit; a providing unit that provides the profile generated by the generating unit to the commentator; Equipped with A system characterized by:

2. The collecting unit Collect data including players' past match data, statistics, interviews, social media posts, and fan comments 2. The system of claim 1.

3. The analysis unit Analyze players' characteristics, strengths, and past performance based on collected data 2. The system of claim 1.

4. The generation unit Generate player profiles based on the analysis results 2. The system of claim 1.

5. The providing unit Providing generated profiles to commentators 2. The system of claim 1.

6. The providing unit Receive feedback from commentators and incorporate it into your next analysis 2. The system of claim 1.

7. The analysis unit Analyzing interview articles using natural language processing 2. The system of claim 1.

8. The analysis unit Analyzing statistical information using machine learning 2. The system of claim 1.

9. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

Images