System

Abstract

An object of the system according to the embodiment is to convert a situation of each inning into a sentence based on a score, pitching, and a batting result of a baseball game.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data of scores or pitching and batting results of the game. The analysis unit analyzes the data collected by the collection unit. The generation unit converts a situation of each inning into a sentence based on the data analyzed by the analysis unit. The providing unit provides the sentence generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately document the detailed game situation based on the score, pitching, and batting results of baseball games, and there is room for improvement.

[0005] The system according to the embodiment aims to document the situation for each inning based on the score, pitching, and batting results of a baseball game. [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 game scores or pitching and batting results. The analysis unit analyzes the data collected by the collection unit. The generation unit creates text describing the situation for each inning based on the data analyzed by the analysis unit. The provision unit provides the text created by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can document the situation for each inning based on the score, pitching, and batting results of a baseball game. [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 system according to an embodiment of the present invention creates text about the game situation based on the score of a baseball game. This system collects data such as the game score, pitching, and batting results, analyzes them using AI, and provides text about the situation for each inning. For example, data such as the game score, pitching, and batting results is collected. Detailed data about each play is collected, including the type and speed of the pitch and the batting result (hit, out, home run, etc.). Next, AI analyzes the collected data. Based on the collected data, the AI ​​analyzes the situation for each inning, including which player made what play and how the points were scored. Based on the analysis results, the AI ​​creates text about the situation for each inning. For example, a text such as "In the top of the first inning, the leadoff batter got on base with a hit, and the next batter advanced the runner with a sacrifice bunt. Then, the third batter hit a hit to score the first run." The generated text is provided as a service. This allows the system to grasp the situation of the game in real time, making it an extremely convenient service for baseball fans. This system allows baseball fans to grasp the situation of the game in real time, making it an extremely convenient service. For example, they can check the highlights of the game and the detailed flow of the play in text.

[0029] A baseball game situation generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on the score of a game or the results of pitches and batting. The collection unit collects data including, for example, the type and speed of pitches and batting results (hit, out, home run, etc.). The collection unit can also collect data by methods such as data collection using sensors or manual input. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or a machine learning algorithm. The analysis unit analyzes which players made what plays and how points were scored based on the collected data. The generation unit creates text describing the situation for each inning based on the data analyzed by the analysis unit. The generation unit generates text using, for example, natural language generation technology or template-based text generation. The provision unit provides the text generated by the generation unit as a service. The provision unit provides the text by, for example, publishing it on a website, sending notifications via a mobile app, or sending reports via email. As a result, the baseball game situation generation system according to the embodiment collects, analyzes, and provides written data such as game scores, pitching and batting results, making it easy to understand the situation of the game.

[0030] The collection unit can collect data including the type or speed of the pitch and the result of the batting. For example, the collection unit collects data including the type and speed of the pitch and the result of the batting (hit, out, home run, etc.). The collection unit can collect the type of pitch, such as a fastball, curveball, or slider, and the unit of speed (km / h, mph). The collection unit can also collect the type of batting result (single, double, triple, home run, etc.). By collecting detailed data on the game, more accurate analysis becomes possible. Some or all of the above-described processing by the collection unit may be performed using, or without, an AI. For example, the collection unit can input data on the type and speed of the pitch to the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze which player made what play and how a point was scored based on the collected data. The analysis unit, for example, analyzes which player made what play based on the collected data. For example, the analysis unit can analyze the names of players and the type of play (hit, out, stolen base, etc.). The analysis unit can also analyze how a point was scored. For example, the analysis unit can analyze the position of runners and the timing of a score. Furthermore, the analysis unit can analyze data using statistical analysis and machine learning algorithms. This enables detailed analysis of the game and an accurate understanding of the game situation. 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 collected data into a generation AI and have the generation AI analyze the data.

[0032] The generation unit can generate sentences describing the situation for each inning. The generation unit generates sentences for each inning, for example. For example, the generation unit can generate sentences based on information such as the score, the number of outs, the runner's position, and the number of pitches. The generation unit can generate sentences using natural language generation technology or template-based sentence generation. For example, the generation unit can generate sentences such as, "In the top of the first inning, the leadoff batter reached base with a hit, and the next batter advanced the runner with a sacrifice bunt. Then, the third batter hit a timely hit, scoring one run." This allows the situation of the game to be easily understood through sentences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input analyzed data into a generation AI and have the AI ​​generate sentences.

[0033] The providing unit can provide the generated text as a service. For example, the providing unit can provide the generated text as a service. The providing unit can provide the text by publishing it on a website, notifying a user via a mobile app, or sending a report via email. For example, the providing unit can provide the generated text in real time. The providing unit can also generate periodic reports and provide them to a user. By providing the generated text to a user, the situation of the game can be understood in real time. 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 generated text to a generation AI and cause the generation AI to provide the text.

[0034] The collection unit can analyze past game data and select the optimal data collection method. The collection unit, for example, analyzes past game data and selects the optimal data collection method. For example, the collection unit identifies timings when specific plays frequently occur based on past game data and collects data at those timings. The collection unit can also analyze past game data and strengthen data collection in situations where important plays are likely to occur. Furthermore, the collection unit can understand the playing patterns of specific players based on past game data and concentrate data collection when that player appears. This enables efficient data collection by selecting the optimal data collection method based on past game data. 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 past game data into a generation AI and cause the generation AI to select the optimal data collection method.

[0035] The collection unit can adjust the level of detail of the data to be collected based on the importance and attention of the game. The collection unit adjusts the level of detail of the data to be collected based on, for example, the importance and attention of the game. For example, the collection unit collects detailed data (such as the type of pitch, speed, and batting results) in important games or games that attract a lot of attention. The collection unit can also collect only basic data (such as the score and number of outs) in games that attract less attention. Furthermore, the collection unit can collect detailed data in important situations and simplified data in other situations depending on the progress of the game. This enables efficient data collection by adjusting the level of detail of the data depending on the importance and attention of the game. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the importance and attention of the game to the generation AI and cause the generation AI to adjust the level of detail of the data.

[0036] The collection unit can dynamically change the type of data to be collected depending on the progress of the game. For example, the collection unit dynamically changes the type of data to be collected depending on the progress of the game. For example, the collection unit collects basic data (such as the score and number of outs) in the early stages of the game, and collects detailed data (such as the type of pitch, speed, and batting results) in the late stages of the game. In addition, the collection unit can collect detailed data on important plays when the game is close. Furthermore, when the game is one-sided, the collection unit can collect only basic data and reduce the collection of detailed data. This enables efficient data collection by dynamically changing the type of data depending on the progress of the game. 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 on the progress of the game to the generation AI and cause the generation AI to change the type of data.

[0037] The collection unit can prioritize collection of highly relevant data taking into account the geographical location information of the game. The collection unit, for example, prioritizes collection of highly relevant data taking into account the geographical location information of the game. For example, the collection unit collects data taking into account the characteristics of the stadium where the game is being played (wind direction, temperature, etc.). The collection unit can also prioritize collection of data related to specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the collection unit can prioritize collection of data before and after the start of the game taking into account the traffic conditions in the city where the game is being played. In this way, collecting data taking into account the geographical location information of the game enables collection of more relevant data. 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 data on the geographical location information of the game to the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can analyze the number of spectators and viewers at a game and adjust the range of data to be collected. The collection unit, for example, analyzes the number of spectators and viewers at a game and adjusts the range of data to be collected. For example, the collection unit collects detailed data (such as the type of pitch, speed, and batting results) in a game with a large number of spectators. The collection unit can also prioritize the collection of data related to important plays in a game with a large number of viewers. Furthermore, the collection unit can collect only basic data (such as the score and number of outs) in a game with a small number of spectators and viewers. This enables efficient data collection by adjusting the range of data depending on the number of spectators and viewers at a game. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit can input data on the number of spectators and viewers at a game to a generation AI and have the generation AI adjust the range of the data.

[0039] The collection unit can change the type of data to be collected taking into account the weather and environmental conditions of the game. For example, the collection unit changes the type of data to be collected taking into account the weather and environmental conditions of the game. For example, the collection unit collects detailed data on pitch control and batting results when it is raining. The collection unit can also collect data on player performance when it is sunny. Furthermore, the collection unit can prioritize collecting data on the type and speed of pitches when it is windy. This enables efficient data collection by changing the type of data depending on the weather and environmental conditions of the game. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the weather and environmental conditions of the game to the generation AI and cause the generation AI to change the type of data.

[0040] The analysis unit can optimize the analysis algorithm by referring to past match data during analysis. The analysis unit, for example, optimizes the analysis algorithm by referring to past match data during analysis. For example, the analysis unit calculates the probability of a specific play occurring based on past match data and adjusts the analysis algorithm. The analysis unit can also refer to past match data to understand player performance trends and reflect them in the analysis algorithm. Furthermore, the analysis unit can predict the flow of a game based on past match data and optimize the analysis algorithm. In this way, optimizing the analysis algorithm by referring to past match data improves the accuracy of the analysis. 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 past match data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0041] The analysis unit can improve the accuracy of the analysis by taking into account the individual data and performance history of the player during analysis. The analysis unit can improve the accuracy of the analysis by taking into account the individual data and performance history of the player during analysis, for example. For example, the analysis unit analyzes the success probability of a specific play based on the player's past performance data. The analysis unit can also analyze the flow of a game by taking into account the individual data of the player (batting average, earned run average, etc.). Furthermore, the analysis unit can analyze the tendency of plays in specific situations based on the player's performance history. In this way, by taking into account the individual data and performance history of the player, the accuracy of the analysis is improved. 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 individual data and performance history of the player into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The analysis unit can dynamically change the focus of analysis depending on the progress of the game during analysis. For example, the analysis unit dynamically changes the focus of analysis depending on the progress of the game during analysis. For example, the analysis unit focuses on the overall flow of the game in the early stages of the game, and emphasizes important plays in the late stages of the game. In addition, if the game is close, the analysis unit can also focus on scoring scenes and important plays in the analysis. Furthermore, if the game is one-sided, the analysis unit can also focus on the overall flow of the game in the analysis. This enables efficient analysis by dynamically changing the focus of analysis depending on the progress of the game. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the progress of the game to the generation AI and cause the generation AI to change the focus of analysis.

[0043] The analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the game during analysis. The analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the game during analysis, for example. For example, the analysis unit can consider the characteristics of the stadium where the game is being played (wind direction, temperature, etc.) during analysis. The analysis unit can also strengthen the analysis of specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the analysis unit can perform analysis before and after the start of the game by taking into account the traffic conditions in the city where the game is being played. In this way, the analysis taking into account the geographical location information of the game improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the geographical location information of the game into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can analyze the number of spectators and viewers at a game during analysis and adjust the scope of the analysis. For example, the analysis unit can analyze the number of spectators and viewers at a game during analysis and adjust the scope of the analysis. For example, the analysis unit performs detailed analysis for games with a large number of spectators. The analysis unit can also strengthen analysis of important plays for games with a large number of viewers. Furthermore, the analysis unit can perform only basic analysis for games with a small number of spectators and viewers. This enables efficient analysis by adjusting the scope of analysis depending on the number of spectators and viewers at a game. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the number of spectators and viewers at a game into the generation AI and cause the generation AI to adjust the scope of the analysis.

[0045] The analysis unit can change the analysis method during analysis, taking into account the weather and environmental conditions of the game. For example, the analysis unit changes the analysis method during analysis, taking into account the weather and environmental conditions of the game. For example, the analysis unit strengthens analysis of pitch control and batting results when it is raining. The analysis unit can also analyze player performance when it is sunny. Furthermore, the analysis unit can strengthen analysis of pitch type and speed when it is windy. This enables efficient analysis by changing the analysis method according to the weather and environmental conditions of the game. 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 data on the weather and environmental conditions of the game into the generation AI and cause the generation AI to change the analysis method.

[0046] The generation unit can adjust the level of detail of the sentences based on the importance and attention of the match when generating the sentences. For example, the generation unit adjusts the level of detail of the sentences based on the importance and attention of the match when generating the sentences. For example, the generation unit generates detailed sentences for important matches or matches that attract a lot of attention. The generation unit can also generate concise sentences for matches that attract less attention. Furthermore, the generation unit can generate detailed sentences for important scenes and simplified sentences for other scenes depending on the progress of the match. This enables efficient sentence generation by adjusting the level of detail of the sentences based on the importance and attention of the match. 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 data on the importance and attention of the match into the generation AI and cause the generation AI to adjust the level of detail of the sentences.

[0047] The generation unit can customize the content of a sentence by taking into account the individual data and performance history of a player when generating the sentence. For example, the generation unit customizes the content of a sentence by taking into account the individual data and performance history of a player when generating the sentence. For example, the generation unit reflects the success probability of a specific play in the sentence based on the player's past performance data. The generation unit can also reflect the flow of the game in the sentence by taking into account the individual data of the player (batting average, earned run average, etc.). Furthermore, the generation unit can reflect the tendency of a play in a specific situation in the sentence based on the player's performance history. This enables more appropriate sentence generation by taking into account the individual data and performance history of a player. 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 individual data and performance history of a player into the generation AI and cause the generation AI to customize the content of the sentence.

[0048] The generation unit can dynamically change the emphasis of the sentences during sentence generation according to the progress of the game. For example, the generation unit dynamically changes the emphasis of the sentences during sentence generation according to the progress of the game. For example, the generation unit generates sentences by emphasizing the overall flow in the early stages of the game, and generates sentences by emphasizing important plays in the late stages. In addition, if the game is close, the generation unit can generate sentences by emphasizing scoring scenes and important plays. Furthermore, if the game is one-sided, the generation unit can generate sentences by emphasizing the overall flow. This enables efficient sentence generation by dynamically changing the emphasis of the sentences according to the progress of the game. 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 data on the progress of the game into the generation AI and cause the generation AI to change the emphasis of the sentences.

[0049] The generation unit can customize the content of the sentence by taking into account the geographical location information of the game when generating the sentence. For example, the generation unit customizes the content of the sentence by taking into account the geographical location information of the game when generating the sentence. For example, the generation unit generates sentences by taking into account the characteristics of the stadium where the game is being played (wind direction, temperature, etc.). The generation unit can also generate sentences about specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the generation unit can generate sentences before and after the start of the game by taking into account the traffic conditions in the city where the game is being played. In this way, generating sentences by taking into account the geographical location information of the game enables more appropriate sentence generation. 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 data on the geographical location information of the game into the generation AI and cause the generation AI to customize the content of the sentence.

[0050] The generation unit can analyze the number of spectators and viewers at a game and adjust the range of the sentence when generating the sentence. For example, the generation unit can analyze the number of spectators and viewers at a game and adjust the range of the sentence when generating the sentence. For example, the generation unit generates more detailed sentences for games with a large number of spectators. The generation unit can also strengthen sentences related to important plays for games with a large number of viewers. Furthermore, the generation unit can generate only basic sentences for games with a small number of spectators and viewers. This enables efficient sentence generation by adjusting the range of the sentence depending on the number of spectators and viewers at a game. 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 data on the number of spectators and viewers at a game into the generation AI and cause the generation AI to adjust the range of the sentence.

[0051] The generation unit can change the content of the sentences when generating the sentences, taking into account the weather and environmental conditions of the game. For example, the generation unit changes the content of the sentences when generating the sentences, taking into account the weather and environmental conditions of the game. For example, the generation unit generates detailed sentences about pitching control and batting results when it is raining. The generation unit can also generate sentences about player performance when it is sunny. Furthermore, the generation unit can prioritize generating sentences about the type and speed of pitches when it is windy. This enables efficient sentence generation by changing the content of the sentences according to the weather and environmental conditions of the game. 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 data about the weather and environmental conditions of the game into the generation AI and cause the generation AI to change the content of the sentences.

[0052] The providing unit can select the optimal display method by referring to the user's past browsing history when providing the data. For example, the providing unit selects the optimal display method by referring to the user's past browsing history when providing the data. For example, the providing unit adjusts the display method based on the format of matches that the user has frequently viewed in the past. The providing unit can also preferentially display information about specific players or plays based on the user's past browsing history. Furthermore, the providing unit can propose an optimal display layout based on the user's past browsing history. In this way, the optimal display method can be selected by referring to the user's past browsing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data about the user's past browsing history to the generating AI and cause the generating AI to select the optimal display method.

[0053] The providing unit can customize the display content according to the user's current field of interest at the time of providing. The providing unit, for example, customizes the display content according to the user's current field of interest at the time of providing. For example, if the user is interested in a particular player, the providing unit can prioritize displaying information about that player. Also, if the user is interested in a particular play, the providing unit can prioritize displaying information about that play. Furthermore, the providing unit can display information about related games based on the user's current field of interest. This enables more appropriate display by customizing the display content according to the user's current field of interest. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data about the user's current field of interest to a generating AI and cause the generating AI to customize the display content.

[0054] The providing unit can improve the display method by reflecting user feedback when providing the data. The providing unit, for example, improves the display method by reflecting user feedback when providing the data. For example, if a user provides feedback on a provided sentence, the providing unit improves the display method based on that feedback. The providing unit can also analyze the user feedback and propose an optimal display layout. Furthermore, the providing unit can adjust the priority of display content based on user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the display method.

[0055] The providing unit can select the optimal display method by taking into account the user's device information when providing the data. For example, the providing unit selects the optimal display method by taking into account the user's device information when providing the data. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0056] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit, for example, makes the display content multilingual according to the user's language setting when providing the content. For example, the providing unit automatically sets the language of the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can provide the display content in that language. This enables more appropriate display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data of the user's language setting into a generation AI and cause the generation AI to perform multilingual support for the display content.

[0057] The providing unit can analyze the user's social media activity and provide related text at the time of providing. For example, the providing unit can analyze the user's social media activity and provide related text at the time of providing. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related text can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide related text.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can dynamically change the focus of analysis depending on the progress of the game. For example, in the early stages of the game, the analysis focuses on the overall flow of the game, and in the late stages, the analysis emphasizes important plays. In addition, if the game is close, the analysis unit can also focus on scoring scenes and important plays in the analysis. Furthermore, if the game is one-sided, the analysis unit can also focus on the overall flow of the game. This allows for efficient analysis by dynamically changing the focus of analysis depending on the progress of the game. 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 data on the progress of the game to the generation AI and cause the generation AI to change the focus of analysis.

[0060] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the game. For example, the collection unit collects data by taking into account the characteristics of the stadium where the game is being played (wind direction, temperature, etc.). The collection unit can also prioritize collecting data about specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the collection unit can prioritize collecting data before and after the start of the game by taking into account the traffic conditions in the city where the game is being played. In this way, collecting data by taking into account the geographical location information of the game enables the collection of more relevant data. 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 about the geographical location information of the game to the generation AI and cause the generation AI to collect highly relevant data.

[0061] During analysis, the analysis unit can optimize the analysis algorithm by referring to past match data. For example, the analysis unit calculates the probability of a specific play occurring based on the past match data and adjusts the analysis algorithm. The analysis unit can also refer to the past match data to understand player performance trends and reflect them in the analysis algorithm. Furthermore, the analysis unit can predict the flow of the game based on the past match data and optimize the analysis algorithm. In this way, optimizing the analysis algorithm by referring to the past match data improves the accuracy of the analysis. 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 past match data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0062] The collection unit can analyze the number of spectators and viewers at a game and adjust the range of data to be collected. For example, the collection unit collects detailed data (such as the type of pitch, speed, and batting results) for a game with a large number of spectators. The collection unit can also prioritize the collection of data related to important plays for a game with a large number of viewers. Furthermore, the collection unit can collect only basic data (such as the score and number of outs) for a game with a small number of spectators and viewers. This allows for efficient data collection by adjusting the range of data depending on the number of spectators and viewers at a game. 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 data on the number of spectators and viewers at a game to the generation AI and have the generation AI adjust the range of data.

[0063] When generating sentences, the generation unit can adjust the level of detail of the sentences based on the importance and attention of the match. For example, the generation unit generates detailed sentences for important matches or matches that are attracting a lot of attention. The generation unit can also generate concise sentences for matches that attract less attention. Furthermore, the generation unit can generate detailed sentences for important scenes and simplified sentences for other scenes depending on the progress of the match. This enables efficient sentence generation by adjusting the level of detail of the sentences depending on the importance and attention of the match. 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 data on the importance and attention of the match into the generation AI and cause the generation AI to adjust the level of detail of the sentences.

[0064] When providing the data, the providing unit can select the optimal display method by referring to the user's past browsing history. For example, the providing unit can adjust the display method based on the format of matches that the user frequently viewed in the past. The providing unit can also prioritize displaying information about specific players or plays based on the user's past browsing history. Furthermore, the providing unit can also suggest an optimal display layout based on the user's past browsing history. In this way, the optimal display method can be selected by referring to the user's past browsing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data about the user's past browsing history to the generating AI and cause the generating AI to select the optimal display method.

[0065] The collection unit can change the type of data to be collected taking into account the weather and environmental conditions of the game. For example, the collection unit collects detailed data on pitch control and batting results when it is raining. The collection unit can also collect data on player performance when it is sunny. Furthermore, the collection unit can prioritize collecting data on the type and speed of pitches when it is windy. This enables efficient data collection by changing the type of data depending on the weather and environmental conditions of the game. 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 on the weather and environmental conditions of the game to the generation AI and cause the generation AI to change the type of data.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data on the score of a game or the results of pitching and batting. The collection unit collects data including, for example, the type and speed of pitches and the results of batting (hit, out, home run, etc.). The collection unit can also collect data by using sensors, manual input, or other methods. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms. Based on the collected data, the analysis unit analyzes which players made what plays and how points were scored. Step 3: The generation unit generates sentences about the situation of each inning based on the data analyzed by the analysis unit. The generation unit generates sentences using, for example, natural language generation technology or template-based sentence generation. Step 4: The providing unit provides the text generated by the generating unit as a service. The providing unit provides the text by, for example, publishing it on a website, notifying users via a mobile app, or sending reports via email.

[0068] (Example 2) A system according to an embodiment of the present invention creates text about the game situation based on the score of a baseball game. This system collects data such as the game score, pitching, and batting results, analyzes them using AI, and provides text about the situation for each inning. For example, data such as the game score, pitching, and batting results is collected. Detailed data about each play is collected, including the type and speed of the pitch and the batting result (hit, out, home run, etc.). Next, AI analyzes the collected data. Based on the collected data, the AI ​​analyzes the situation for each inning, including which player made what play and how the points were scored. Based on the analysis results, the AI ​​creates text about the situation for each inning. For example, a text such as "In the top of the first inning, the leadoff batter got on base with a hit, and the next batter advanced the runner with a sacrifice bunt. Then, the third batter hit a hit to score the first run." The generated text is provided as a service. This allows the system to grasp the situation of the game in real time, making it an extremely convenient service for baseball fans. This system allows baseball fans to grasp the situation of the game in real time, making it an extremely convenient service. For example, they can check the highlights of the game and the detailed flow of the play in text.

[0069] A baseball game situation generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on the score of a game or the results of pitches and batting. The collection unit collects data including, for example, the type and speed of pitches and batting results (hit, out, home run, etc.). The collection unit can also collect data by methods such as data collection using sensors or manual input. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or a machine learning algorithm. The analysis unit analyzes which players made what plays and how points were scored based on the collected data. The generation unit creates text describing the situation for each inning based on the data analyzed by the analysis unit. The generation unit generates text using, for example, natural language generation technology or template-based text generation. The provision unit provides the text generated by the generation unit as a service. The provision unit provides the text by, for example, publishing it on a website, sending notifications via a mobile app, or sending reports via email. As a result, the baseball game situation generation system according to the embodiment collects, analyzes, and provides written data such as game scores, pitching and batting results, making it easy to understand the situation of the game.

[0070] The collection unit can collect data including the type or speed of the pitch and the result of the batting. For example, the collection unit collects data including the type and speed of the pitch and the result of the batting (hit, out, home run, etc.). The collection unit can collect the type of pitch, such as a fastball, curveball, or slider, and the unit of speed (km / h, mph). The collection unit can also collect the type of batting result (single, double, triple, home run, etc.). By collecting detailed data on the game, more accurate analysis becomes possible. Some or all of the above-described processing by the collection unit may be performed using, or without, an AI. For example, the collection unit can input data on the type and speed of the pitch to the generation AI and have the generation AI analyze the data.

[0071] The analysis unit can analyze which player made what play and how a point was scored based on the collected data. The analysis unit, for example, analyzes which player made what play based on the collected data. For example, the analysis unit can analyze the names of players and the type of play (hit, out, stolen base, etc.). The analysis unit can also analyze how a point was scored. For example, the analysis unit can analyze the position of runners and the timing of a score. Furthermore, the analysis unit can analyze data using statistical analysis and machine learning algorithms. This enables detailed analysis of the game and an accurate understanding of the game situation. 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 collected data into a generation AI and have the generation AI analyze the data.

[0072] The generation unit can generate sentences describing the situation for each inning. The generation unit generates sentences for each inning, for example. For example, the generation unit can generate sentences based on information such as the score, the number of outs, the runner's position, and the number of pitches. The generation unit can generate sentences using natural language generation technology or template-based sentence generation. For example, the generation unit can generate sentences such as, "In the top of the first inning, the leadoff batter reached base with a hit, and the next batter advanced the runner with a sacrifice bunt. Then, the third batter hit a timely hit, scoring one run." This allows the situation of the game to be easily understood through sentences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input analyzed data into a generation AI and have the AI ​​generate sentences.

[0073] The providing unit can provide the generated text as a service. For example, the providing unit can provide the generated text as a service. The providing unit can provide the text by publishing it on a website, notifying a user via a mobile app, or sending a report via email. For example, the providing unit can provide the generated text in real time. The providing unit can also generate periodic reports and provide them to a user. By providing the generated text to a user, the situation of the game can be understood in real time. 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 generated text to a generation AI and cause the generation AI to provide the text.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit 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 analyzes it. When the user is relaxed, the collection unit can collect data at regular intervals and analyze it in batches. Furthermore, when the user is stressed, the collection unit can reduce the frequency of data collection and collect only important plays. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 user emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0075] The collection unit can analyze past game data and select the optimal data collection method. The collection unit, for example, analyzes past game data and selects the optimal data collection method. For example, the collection unit identifies timings when specific plays frequently occur based on past game data and collects data at those timings. The collection unit can also analyze past game data and strengthen data collection in situations where important plays are likely to occur. Furthermore, the collection unit can understand the playing patterns of specific players based on past game data and concentrate data collection when that player appears. This enables efficient data collection by selecting the optimal data collection method based on past game data. 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 past game data into a generation AI and cause the generation AI to select the optimal data collection method.

[0076] The collection unit can adjust the level of detail of the data to be collected based on the importance and attention of the game. The collection unit adjusts the level of detail of the data to be collected based on, for example, the importance and attention of the game. For example, the collection unit collects detailed data (such as the type of pitch, speed, and batting results) in important games or games that attract a lot of attention. The collection unit can also collect only basic data (such as the score and number of outs) in games that attract less attention. Furthermore, the collection unit can collect detailed data in important situations and simplified data in other situations depending on the progress of the game. This enables efficient data collection by adjusting the level of detail of the data depending on the importance and attention of the game. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the importance and attention of the game to the generation AI and cause the generation AI to adjust the level of detail of the data.

[0077] The collection unit can dynamically change the type of data to be collected depending on the progress of the game. For example, the collection unit dynamically changes the type of data to be collected depending on the progress of the game. For example, the collection unit collects basic data (such as the score and number of outs) in the early stages of the game, and collects detailed data (such as the type of pitch, speed, and batting results) in the late stages of the game. In addition, the collection unit can collect detailed data on important plays when the game is close. Furthermore, when the game is one-sided, the collection unit can collect only basic data and reduce the collection of detailed data. This enables efficient data collection by dynamically changing the type of data depending on the progress of the game. 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 on the progress of the game to the generation AI and cause the generation AI to change the type of data.

[0078] 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, when the user is excited, the collection unit prioritizes collecting important plays (such as home runs and scoring scenes). Furthermore, when the user is relaxed, the collection unit can collect balanced data so that the overall flow of the game can be grasped. Furthermore, when the user is stressed, the collection unit can prioritize collecting only highlight scenes of the game. This enables more appropriate data collection by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0079] The collection unit can prioritize collection of highly relevant data taking into account the geographical location information of the game. The collection unit, for example, prioritizes collection of highly relevant data taking into account the geographical location information of the game. For example, the collection unit collects data taking into account the characteristics of the stadium where the game is being played (wind direction, temperature, etc.). The collection unit can also prioritize collection of data related to specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the collection unit can prioritize collection of data before and after the start of the game taking into account the traffic conditions in the city where the game is being played. In this way, collecting data taking into account the geographical location information of the game enables collection of more relevant data. 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 data on the geographical location information of the game to the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit can analyze the number of spectators and viewers at a game and adjust the range of data to be collected. The collection unit, for example, analyzes the number of spectators and viewers at a game and adjusts the range of data to be collected. For example, the collection unit collects detailed data (such as the type of pitch, speed, and batting results) in a game with a large number of spectators. The collection unit can also prioritize the collection of data related to important plays in a game with a large number of viewers. Furthermore, the collection unit can collect only basic data (such as the score and number of outs) in a game with a small number of spectators and viewers. This enables efficient data collection by adjusting the range of data depending on the number of spectators and viewers at a game. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit can input data on the number of spectators and viewers at a game to a generation AI and have the generation AI adjust the range of the data.

[0081] The collection unit can change the type of data to be collected taking into account the weather and environmental conditions of the game. For example, the collection unit changes the type of data to be collected taking into account the weather and environmental conditions of the game. For example, the collection unit collects detailed data on pitch control and batting results when it is raining. The collection unit can also collect data on player performance when it is sunny. Furthermore, the collection unit can prioritize collecting data on the type and speed of pitches when it is windy. This enables efficient data collection by changing the type of data depending on the weather and environmental conditions of the game. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the weather and environmental conditions of the game to the generation AI and cause the generation AI to change the type of data.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is excited, the analysis unit can emphasize important plays in the analysis. Furthermore, if the user is relaxed, the analysis unit can also focus on the overall flow of the game. Furthermore, if the user is stressed, the analysis unit can perform a concise, to-the-point analysis. This enables more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 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 the generation AI and have the generation AI adjust the analysis criteria.

[0083] The analysis unit can optimize the analysis algorithm by referring to past match data during analysis. The analysis unit, for example, optimizes the analysis algorithm by referring to past match data during analysis. For example, the analysis unit calculates the probability of a specific play occurring based on past match data and adjusts the analysis algorithm. The analysis unit can also refer to past match data to understand player performance trends and reflect them in the analysis algorithm. Furthermore, the analysis unit can predict the flow of a game based on past match data and optimize the analysis algorithm. In this way, optimizing the analysis algorithm by referring to past match data improves the accuracy of the analysis. 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 past match data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0084] The analysis unit can improve the accuracy of the analysis by taking into account the individual data and performance history of the player during analysis. The analysis unit can improve the accuracy of the analysis by taking into account the individual data and performance history of the player during analysis, for example. For example, the analysis unit analyzes the success probability of a specific play based on the player's past performance data. The analysis unit can also analyze the flow of a game by taking into account the individual data of the player (batting average, earned run average, etc.). Furthermore, the analysis unit can analyze the tendency of plays in specific situations based on the player's performance history. In this way, by taking into account the individual data and performance history of the player, the accuracy of the analysis is improved. 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 individual data and performance history of the player into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can dynamically change the focus of analysis depending on the progress of the game during analysis. For example, the analysis unit dynamically changes the focus of analysis depending on the progress of the game during analysis. For example, the analysis unit focuses on the overall flow of the game in the early stages of the game, and emphasizes important plays in the late stages of the game. In addition, if the game is close, the analysis unit can also focus on scoring scenes and important plays in the analysis. Furthermore, if the game is one-sided, the analysis unit can also focus on the overall flow of the game in the analysis. This enables efficient analysis by dynamically changing the focus of analysis depending on the progress of the game. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the progress of the game to the generation AI and cause the generation AI to change the focus of analysis.

[0086] 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, if the user is excited, the analysis unit can highlight important plays. Furthermore, if the user is relaxed, the analysis unit can display the overall flow with emphasis. Furthermore, if the user is stressed, the analysis unit can provide a concise and concise display method. This allows for more appropriate display 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0087] The analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the game during analysis. The analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the game during analysis, for example. For example, the analysis unit can consider the characteristics of the stadium where the game is being played (wind direction, temperature, etc.) during analysis. The analysis unit can also strengthen the analysis of specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the analysis unit can perform analysis before and after the start of the game by taking into account the traffic conditions in the city where the game is being played. In this way, the analysis taking into account the geographical location information of the game improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the geographical location information of the game into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0088] The analysis unit can analyze the number of spectators and viewers at a game during analysis and adjust the scope of the analysis. For example, the analysis unit can analyze the number of spectators and viewers at a game during analysis and adjust the scope of the analysis. For example, the analysis unit performs detailed analysis for games with a large number of spectators. The analysis unit can also strengthen analysis of important plays for games with a large number of viewers. Furthermore, the analysis unit can perform only basic analysis for games with a small number of spectators and viewers. This enables efficient analysis by adjusting the scope of analysis depending on the number of spectators and viewers at a game. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the number of spectators and viewers at a game into the generation AI and cause the generation AI to adjust the scope of the analysis.

[0089] The analysis unit can change the analysis method during analysis, taking into account the weather and environmental conditions of the game. For example, the analysis unit changes the analysis method during analysis, taking into account the weather and environmental conditions of the game. For example, the analysis unit strengthens analysis of pitch control and batting results when it is raining. The analysis unit can also analyze player performance when it is sunny. Furthermore, the analysis unit can strengthen analysis of pitch type and speed when it is windy. This enables efficient analysis by changing the analysis method according to the weather and environmental conditions of the game. 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 data on the weather and environmental conditions of the game into the generation AI and cause the generation AI to change the analysis method.

[0090] The generation unit can estimate the user's emotions and adjust the way sentences are expressed based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the way sentences are expressed based on the estimated user emotions. For example, if the user is excited, the generation unit uses expressions that excite the user. Furthermore, if the user is relaxed, the generation unit can use calm expressions. Furthermore, if the user is stressed, the generation unit can use concise and to-the-point expressions. This allows for more appropriate sentence generation by adjusting the way sentences are expressed based on 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 these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the way sentences are expressed.

[0091] The generation unit can adjust the level of detail of the sentences based on the importance and attention of the match when generating the sentences. For example, the generation unit adjusts the level of detail of the sentences based on the importance and attention of the match when generating the sentences. For example, the generation unit generates detailed sentences for important matches or matches that attract a lot of attention. The generation unit can also generate concise sentences for matches that attract less attention. Furthermore, the generation unit can generate detailed sentences for important scenes and simplified sentences for other scenes depending on the progress of the match. This enables efficient sentence generation by adjusting the level of detail of the sentences based on the importance and attention of the match. 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 data on the importance and attention of the match into the generation AI and cause the generation AI to adjust the level of detail of the sentences.

[0092] The generation unit can customize the content of a sentence by taking into account the individual data and performance history of a player when generating the sentence. For example, the generation unit customizes the content of a sentence by taking into account the individual data and performance history of a player when generating the sentence. For example, the generation unit reflects the success probability of a specific play in the sentence based on the player's past performance data. The generation unit can also reflect the flow of the game in the sentence by taking into account the individual data of the player (batting average, earned run average, etc.). Furthermore, the generation unit can reflect the tendency of a play in a specific situation in the sentence based on the player's performance history. This enables more appropriate sentence generation by taking into account the individual data and performance history of a player. 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 individual data and performance history of a player into the generation AI and cause the generation AI to customize the content of the sentence.

[0093] The generation unit can dynamically change the emphasis of the sentences during sentence generation according to the progress of the game. For example, the generation unit dynamically changes the emphasis of the sentences during sentence generation according to the progress of the game. For example, the generation unit generates sentences by emphasizing the overall flow in the early stages of the game, and generates sentences by emphasizing important plays in the late stages. In addition, if the game is close, the generation unit can generate sentences by emphasizing scoring scenes and important plays. Furthermore, if the game is one-sided, the generation unit can generate sentences by emphasizing the overall flow. This enables efficient sentence generation by dynamically changing the emphasis of the sentences according to the progress of the game. 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 data on the progress of the game into the generation AI and cause the generation AI to change the emphasis of the sentences.

[0094] The generation unit can estimate the user's emotion and adjust the length of the sentence based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the sentence based on the estimated user's emotion. For example, the generation unit can generate detailed sentences when the user is excited. Furthermore, the generation unit can generate sentences of appropriate length when the user is relaxed. Furthermore, the generation unit can generate short, concise, and to-the-point sentences when the user is stressed. This enables more appropriate sentence generation by adjusting the length of the sentence according to the user's emotion. The emotion estimation is realized 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 these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the sentence.

[0095] The generation unit can customize the content of the sentence by taking into account the geographical location information of the game when generating the sentence. For example, the generation unit customizes the content of the sentence by taking into account the geographical location information of the game when generating the sentence. For example, the generation unit generates sentences by taking into account the characteristics of the stadium where the game is being played (wind direction, temperature, etc.). The generation unit can also generate sentences about specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the generation unit can generate sentences before and after the start of the game by taking into account the traffic conditions in the city where the game is being played. In this way, generating sentences by taking into account the geographical location information of the game enables more appropriate sentence generation. 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 data on the geographical location information of the game into the generation AI and cause the generation AI to customize the content of the sentence.

[0096] The generation unit can analyze the number of spectators and viewers at a game and adjust the range of the sentence when generating the sentence. For example, the generation unit can analyze the number of spectators and viewers at a game and adjust the range of the sentence when generating the sentence. For example, the generation unit generates more detailed sentences for games with a large number of spectators. The generation unit can also strengthen sentences related to important plays for games with a large number of viewers. Furthermore, the generation unit can generate only basic sentences for games with a small number of spectators and viewers. This enables efficient sentence generation by adjusting the range of the sentence depending on the number of spectators and viewers at a game. 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 data on the number of spectators and viewers at a game into the generation AI and cause the generation AI to adjust the range of the sentence.

[0097] The generation unit can change the content of the sentences when generating the sentences, taking into account the weather and environmental conditions of the game. For example, the generation unit changes the content of the sentences when generating the sentences, taking into account the weather and environmental conditions of the game. For example, the generation unit generates detailed sentences about pitching control and batting results when it is raining. The generation unit can also generate sentences about player performance when it is sunny. Furthermore, the generation unit can prioritize generating sentences about the type and speed of pitches when it is windy. This enables efficient sentence generation by changing the content of the sentences according to the weather and environmental conditions of the game. 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 data about the weather and environmental conditions of the game into the generation AI and cause the generation AI to change the content of the sentences.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the provided text based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the display method of the provided text based on the estimated user emotions. For example, if the user is excited, the providing unit can highlight important plays when displaying the text. Furthermore, if the user is relaxed, the providing unit can also display the text with an emphasis on the overall flow. Furthermore, if the user is stressed, the providing unit can provide a concise, to-the-point display when displaying the text. This allows for more appropriate display by adjusting the display method of the text based on 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 providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0099] The providing unit can select the optimal display method by referring to the user's past browsing history when providing the data. For example, the providing unit selects the optimal display method by referring to the user's past browsing history when providing the data. For example, the providing unit adjusts the display method based on the format of matches that the user has frequently viewed in the past. The providing unit can also preferentially display information about specific players or plays based on the user's past browsing history. Furthermore, the providing unit can propose an optimal display layout based on the user's past browsing history. In this way, the optimal display method can be selected by referring to the user's past browsing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data about the user's past browsing history to the generating AI and cause the generating AI to select the optimal display method.

[0100] The providing unit can customize the display content according to the user's current field of interest at the time of providing. The providing unit, for example, customizes the display content according to the user's current field of interest at the time of providing. For example, if the user is interested in a particular player, the providing unit can prioritize displaying information about that player. Also, if the user is interested in a particular play, the providing unit can prioritize displaying information about that play. Furthermore, the providing unit can display information about related games based on the user's current field of interest. This enables more appropriate display by customizing the display content according to the user's current field of interest. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data about the user's current field of interest to a generating AI and cause the generating AI to customize the display content.

[0101] The providing unit can improve the display method by reflecting user feedback when providing the data. The providing unit, for example, improves the display method by reflecting user feedback when providing the data. For example, if a user provides feedback on a provided sentence, the providing unit improves the display method based on that feedback. The providing unit can also analyze the user feedback and propose an optimal display layout. Furthermore, the providing unit can adjust the priority of display content based on user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the display method.

[0102] The providing unit can estimate the user's emotions and determine the priority of sentences to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of sentences to be provided based on the estimated user emotions. For example, when the user is excited, the providing unit prioritizes displaying important plays. Furthermore, when the user is relaxed, the providing unit can also prioritize displaying the overall flow. Furthermore, when the user is stressed, the providing unit can prioritize displaying concise sentences that are to the point. This enables more appropriate display by determining the priority of sentences to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to determine the priority of sentences.

[0103] The providing unit can select the optimal display method by taking into account the user's device information when providing the data. For example, the providing unit selects the optimal display method by taking into account the user's device information when providing the data. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0104] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit, for example, makes the display content multilingual according to the user's language setting when providing the content. For example, the providing unit automatically sets the language of the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can provide the display content in that language. This enables more appropriate display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data of the user's language setting into a generation AI and cause the generation AI to perform multilingual support for the display content.

[0105] The providing unit can analyze the user's social media activity and provide related text at the time of providing. For example, the providing unit can analyze the user's social media activity and provide related text at the time of providing. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related text can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide related text. === 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 collects data on game scores, pitching results, and batting results using the camera 42 and sensors of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using statistical analysis and machine learning algorithms. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates text describing the situation of each inning based on the analysis results using natural language generation technology. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated text on a website or mobile app. === Hard Collateral 1-2 === 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 glasses 214 and the data processing device 12. For example, the collection unit collects data on game scores, pitching results, and batting results using the camera 42 and sensors of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text about the situation of each inning based on the analysis results using natural language generation technology. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated text on a website or mobile app. === Hard Collateral 1-3 === 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 headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data on game scores, pitching results, and batting results using the camera 42 and sensors of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates text about the situation of each inning based on the analysis results using natural language generation technology. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated text on a website or mobile app. === Hard Collateral 1-4 === 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 robot 414 and the data processing device 12. For example, the collection unit collects data on game scores, pitching results, and batting results using the camera 42 and sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates text describing the situation of each inning based on the analysis results using natural language generation technology. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated text on a website or mobile app.

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

[0107] The analysis unit can dynamically change the focus of analysis depending on the progress of the game. For example, in the early stages of the game, the analysis focuses on the overall flow of the game, and in the late stages, the analysis emphasizes important plays. In addition, if the game is close, the analysis unit can also focus on scoring scenes and important plays in the analysis. Furthermore, if the game is one-sided, the analysis unit can also focus on the overall flow of the game. This allows for efficient analysis by dynamically changing the focus of analysis depending on the progress of the game. 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 data on the progress of the game to the generation AI and cause the generation AI to change the focus of analysis.

[0108] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the game. For example, the collection unit collects data by taking into account the characteristics of the stadium where the game is being played (wind direction, temperature, etc.). The collection unit can also prioritize collecting data about specific players or plays based on the interests of fans in the area where the game is being played. Furthermore, the collection unit can prioritize collecting data before and after the start of the game by taking into account the traffic conditions in the city where the game is being played. In this way, collecting data by taking into account the geographical location information of the game enables the collection of more relevant data. 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 about the geographical location information of the game to the generation AI and cause the generation AI to collect highly relevant data.

[0109] The generation unit can estimate the user's emotions and adjust the way sentences are expressed based on the estimated user emotions. For example, if the user is excited, the generation unit can use expressions that excite the user. Furthermore, if the user is relaxed, the generation unit can use calm expressions. Furthermore, if the user is stressed, the generation unit can use concise and to-the-point expressions. This allows for more appropriate sentence generation by adjusting the way sentences are expressed based on 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, 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 the generation AI and have the generation AI adjust the way sentences are expressed.

[0110] The providing unit can estimate the user's emotions and adjust the display method of the provided text based on the estimated user's emotions. For example, if the user is excited, the providing unit can highlight important plays. Furthermore, if the user is relaxed, the providing unit can display the text with an emphasis on the overall flow. Furthermore, if the user is stressed, the providing unit can provide a concise and to-the-point display method. This allows for more appropriate display by adjusting the display method of the provided text 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0111] During analysis, the analysis unit can optimize the analysis algorithm by referring to past match data. For example, the analysis unit calculates the probability of a specific play occurring based on the past match data and adjusts the analysis algorithm. The analysis unit can also refer to the past match data to understand player performance trends and reflect them in the analysis algorithm. Furthermore, the analysis unit can predict the flow of the game based on the past match data and optimize the analysis algorithm. In this way, optimizing the analysis algorithm by referring to the past match data improves the accuracy of the analysis. 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 past match data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0112] The collection unit can analyze the number of spectators and viewers at a game and adjust the range of data to be collected. For example, the collection unit collects detailed data (such as the type of pitch, speed, and batting results) for a game with a large number of spectators. The collection unit can also prioritize the collection of data related to important plays for a game with a large number of viewers. Furthermore, the collection unit can collect only basic data (such as the score and number of outs) for a game with a small number of spectators and viewers. This allows for efficient data collection by adjusting the range of data depending on the number of spectators and viewers at a game. 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 data on the number of spectators and viewers at a game to the generation AI and have the generation AI adjust the range of data.

[0113] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is excited, the analysis unit can emphasize important plays in the analysis. Furthermore, if the user is relaxed, the analysis unit can also focus on the overall flow of the game. Furthermore, if the user is stressed, the analysis unit can perform a concise, to-the-point analysis. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 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 the generation AI and have the generation AI adjust the analysis criteria.

[0114] When generating sentences, the generation unit can adjust the level of detail of the sentences based on the importance and attention of the match. For example, the generation unit generates detailed sentences for important matches or matches that are attracting a lot of attention. The generation unit can also generate concise sentences for matches that attract less attention. Furthermore, the generation unit can generate detailed sentences for important scenes and simplified sentences for other scenes depending on the progress of the match. This enables efficient sentence generation by adjusting the level of detail of the sentences depending on the importance and attention of the match. 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 data on the importance and attention of the match into the generation AI and cause the generation AI to adjust the level of detail of the sentences.

[0115] When providing the data, the providing unit can select the optimal display method by referring to the user's past browsing history. For example, the providing unit can adjust the display method based on the format of matches that the user frequently viewed in the past. The providing unit can also prioritize displaying information about specific players or plays based on the user's past browsing history. Furthermore, the providing unit can also suggest an optimal display layout based on the user's past browsing history. In this way, the optimal display method can be selected by referring to the user's past browsing history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data about the user's past browsing history to the generating AI and cause the generating AI to select the optimal display method.

[0116] The collection unit can change the type of data to be collected taking into account the weather and environmental conditions of the game. For example, the collection unit collects detailed data on pitch control and batting results when it is raining. The collection unit can also collect data on player performance when it is sunny. Furthermore, the collection unit can prioritize collecting data on the type and speed of pitches when it is windy. This enables efficient data collection by changing the type of data depending on the weather and environmental conditions of the game. 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 on the weather and environmental conditions of the game to the generation AI and cause the generation AI to change the type of data.

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

[0118] Step 1: The collection unit collects data on the score of a game or the results of pitching and batting. The collection unit collects data including, for example, the type and speed of pitches and the results of batting (hit, out, home run, etc.). The collection unit can also collect data by using sensors, manual input, or other methods. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms. Based on the collected data, the analysis unit analyzes which players made what plays and how points were scored. Step 3: The generation unit generates sentences about the situation of each inning based on the data analyzed by the analysis unit. The generation unit generates sentences using, for example, natural language generation technology or template-based sentence generation. Step 4: The providing unit provides the text generated by the generating unit as a service. The providing unit provides the text by, for example, publishing it on a website, notifying users via a mobile app, or sending reports via email.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

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

Claims

1. a collection unit that collects data on game scores or pitching and batting results; an analysis unit that analyzes the data collected by the collection unit; a generating unit that generates text for each inning situation based on the data analyzed by the analyzing unit; a providing unit that provides the sentence generated by the generating unit. A system characterized by:

2. The collecting unit Collect data including pitch type or velocity and batting results 2. The system of claim 1.

3. The analysis unit Based on the collected data, we analyze which players made what plays and how the points were scored.

2. The system of claim 1.

4. The generation unit Document the situation for each inning 2. The system of claim 1.

5. The providing unit Providing generated text as a service 2. The system of claim 1.

6. 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.

7. The collecting unit Analyze past match data and select the most appropriate data collection method 2. The system of claim 1.

8. The collecting unit Adjust the level of detail of data collected based on the importance and popularity of the match 2. The system of claim 1.

Images